UNIVERSITY OF CALGARY Non-medical reasons for colectomy among ulcerative colitis patients by

UNIVERSITY OF CALGARY
Non-medical reasons for colectomy among ulcerative colitis patients
by
María Eugenia Negrón
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
VETERINARY MEDICAL SCIENCES GRADUATE PROGRAM
CALGARY, ALBERTA
July, 2014
© María E. Negrón
2014
Abstract
Approximately 16% of ulcerative colitis (UC) patients will have a colectomy
within 10 years of their diagnosis of UC. Due to the high morbidity and mortality
associated with colectomy procedures, clinicians and patients strive to avoid surgery. The
decline of colectomy rates over the last 6 decades has been mainly attributed to advances
in medical management of UC. Clostridium difficile infection and colorectal neoplasia
have also been associated with driving the risk of colectomy. This dissertation focuses on
describing the effect of C. difficile and colorectal neoplasia on the risk of colectomy for
UC patients. Chapters 2 and 3 establish the cumulative risk of acquiring C. difficile
infection after the diagnosis of UC and demonstrated that C. difficile infections increase
the short- and long-term risk of colectomy. Further, both Chapters 2 and 3 demonstrate
that C. difficile infections increased the risk of postoperative complications following
colectomy for UC. Chapter 4 demonstrate that the incidence of colectomy for colorectal
dysplasia and cancer has remained stable over time, and showed that the observed
decrease in colectomy rates is most likely due to a decrease in colectomies from
medically refractory disease. Finally, Chapter 5 determined the cost-effectiveness of
different surveillance colonoscopy intervals for colorectal dysplasia among a subgroup of
inflammatory bowel disease (IBD) patients with a higher risk of developing colorectal
dysplasia and cancer; i.e., patients with IBD and primary sclerosing cholangitis (PSC).
ii
Preface
This dissertation consists of four manuscripts – two of which have been accepted
for publication, and two are ready for submission. For all four manuscripts, the first
author was involved with study concept and design, acquisition of data, analysis and
interpretation of data, drafting of the manuscript, and critical revision. This was done
under the guidance of the supervisor and co-supervisor. All authors provided critical
reviews of the manuscripts and contributed intellectual content. After receiving written
permission from the publishers and co-authors, all four manuscripts were reproduced in
their entirety as chapters in this dissertation.
Manuscript I) Negrón ME, Barkema HW, Rioux K, De Buck J, Checkley S, Proulx MC,
Frolkis A, Beck PL, Dieleman L, Panaccione R, Ghosh S, Kaplan GG. Clostridium
difficile infection worsens prognosis of ulcerative colitis. Accepted: Canadian Journal of
Gastroenterology and Hepatology.
Manuscript II) Negrón ME, Barkema HW, Kaplan GG. Clostridium difficile infection
diagnosed early in ulcerative colitis patients impacts disease related outcomes: A
population-based inception cohort study. Will be submitted to: Gut.
Manuscript III) Negrón ME, Barkema HW, Kaplan GG. Changes in the annual incidence
of colectomy for colorectal neoplasia among UC patients: A population-base study. Will
be submitted to Clinical Gastroenterology and Hepatology
iii
Manuscript IV) Negrón ME, Kaplan GG, Barkema HW, Eksteen B, Clement F, Manns
BJ, Coward S, Panaccione R, Ghosh S, Heitman SJ. Colorectal cancer surveillance in
patients with inflammatory bowel disease and primary sclerosing cholangitis: An
economic evaluation. Accepted: Inflammatory Bowel Disease.
iv
Acknowledgements
I would like to express my most sincere gratitude to my supervisor and cosupervisor, Drs. Herman W. Barkema and Gilaad G. Kaplan. Herman and Gil, I admire
both of you as my mentors, colleagues and as a people. Thank you for your patience, for
believing in me and guiding me during these last 4 years. You have no idea how much I
learned from both of you! I also want to thank my committee members: Drs. Sylvia
Checkley, Jeroen De Buck, Bertus Eksteen and Kevin Rioux for their guidance and
support. Additionally, I want to thank Drs. Fiona Clement and Steven Heitman who
introduced me to the world of health economics. I hope I make you both proud! I would
like to show my deepest appreciation to Karin Orsel, who has been a friend and a mentor
during these last 4 years. Thank you for being available to chat and for giving guidance
when I needed it. I would like to acknowledge my lab members, Alex, Ellen and
Stephanie for all those stimulating discussions and for all the fun we had during these last
few years. In particular, Alex, I admire your wisdom and your attitude towards life.
Thank you for taking time to brainstorm with me.
To the amazing people I get to call my friends in Calgary. You guys are the
epitome of what a family away from home means. Most importantly, Andrew, your care,
support and confidence in me were all that kept me going at times during these last few
months. Thank you, love.
I would like to acknowledge the DIMR departments for providing the data for the
patients with ulcerative colitis living in the Calgary Health Zone, and Calgary Laboratory
Services for providing the Clostridium difficile test results. Finally, I would like to
recognize the financial assistance of the University of Calgary (Teaching Assistantships,
v
Graduate Research Scholarship and Travel Grants) and the Alberta Inflammatory Bowel
Disease Consortium (Scholarship).
vi
Dedication
To my family… the one I was given and the one I chose along the way.
vii
Table of Contents
Abstract ................................................................................................................................ii
Preface ............................................................................................................................... iii
Table of Contents............................................................................................................. viii
List of Tables .....................................................................................................................xii
List of Symbols, Abbreviations and Nomenclature.......................................................... xiv
Epigraph............................................................................................................................ xvi
CHAPTER ONE: INTRODUCTION ................................................................................. 1
1.1 General Overview ...................................................................................................... 2
1.2 Background of Ulcerative Colitis .............................................................................. 6
1.2.1 Burden of ulcerative colitis: incidence, prevalence and economic impact ....... 7
1.2.2 Risk factors........................................................................................................ 8
1.2.3 Natural history of the disease ............................................................................ 9
1.2.4 Medications ..................................................................................................... 10
1.2.5 Surgery ............................................................................................................ 11
1.3 Clostridium difficile ................................................................................................. 13
1.4 Colorectal Dysplasia and Cancer............................................................................. 16
1.5 Thesis Objectives and Hypothesis ........................................................................... 20
CHAPTER TWO: CLOSTRIDIUM DIFFICILE INFECTION WORSENS THE
PROGNOSIS OF ULCERATIVE COLITIS ........................................................... 24
2.1 Abstract.................................................................................................................... 25
2.2 Introduction ............................................................................................................. 26
2.3 Materials and Methods ............................................................................................ 27
2.3.1 Data sources .................................................................................................... 27
2.3.2 Study population ............................................................................................. 27
2.3.3 Selection of cases and controls ....................................................................... 28
2.3.4 Exposure .......................................................................................................... 29
2.3.5 Covariates ........................................................................................................ 29
2.3.6 Statistical analysis ........................................................................................... 30
2.4 Results ..................................................................................................................... 32
2.5 Discussion................................................................................................................ 34
2.6 Acknowledgements ................................................................................................. 37
2.7 Financial Disclosures............................................................................................... 38
2.8 References ............................................................................................................... 39
2.9 Appendix 2.A Post-operative complications classification ..................................... 50
2.10 Appendix 2.B Comorbidity classifications............................................................ 51
2.11 Appendix 2.C Postoperative complications observed among patients tested for
C. difficile. ............................................................................................................. 52
2.12 Appendix 2.D Sensitivity analysis: Logistic regression results of emergent
colectomy with C. difficile diagnosis in hospital or 14 days prior to admission
as primary exposure. .............................................................................................. 54
2.13 Appendix 2.E Sensitivity analyses: Logistic regression results of any postoperative complication and infectious post-operative complication with C.
viii
difficile diagnosis in hospital or 14 days prior to admission as primary
exposure. ................................................................................................................ 55
2.14 Appendix 2.F Sensitivity analysis: Logistic regression results of emergent
colectomy including those not tested for C. difficile. ............................................ 56
2.15 Appendix 2.G Sensitivity analyses: Logistic regression results of any postoperative complication and infectious post-operative complication including
those not tested for C. difficile. .............................................................................. 57
CHAPTER THREE: CLOSTRIDIUM DIFFICILE INFECTION DIAGNOSED
EARLY IN ULCERATIVE COLITIS PATIENTS IMPACTS DISEASE
RELATED OUTCOMES: A POPULATION-BASED INCEPTION COHORT
STUDY ..................................................................................................................... 58
3.1 Abstract.................................................................................................................... 59
3.2 Introduction ............................................................................................................. 61
3.3 Materials and Methods ............................................................................................ 62
3.3.1 Study population ............................................................................................. 62
3.3.1.1 Validation cohort ................................................................................... 62
3.3.1.2 Inception cohort of UC patients ............................................................ 63
3.3.2 Data sources .................................................................................................... 63
3.3.2.1 Validation of the ICD-10 code for C. difficile diagnosis among UC
patients .................................................................................................... 63
3.3.2.2 Inception cohort of patients with UC .................................................... 64
3.3.3 Development of the UC inception cohort ....................................................... 65
3.3.4 Outcome and predictor variables .................................................................... 66
3.3.5 Analyses .......................................................................................................... 67
3.3.5.1 Validation of the ICD-10 code for C. difficile diagnosis among UC
patients .................................................................................................... 67
3.3.5.2 Effect of C. difficile on UC incident cases ............................................ 68
3.4 Results ..................................................................................................................... 69
3.4.1 Validation of the ICD-10 code for C. difficile diagnosis among UC
patients ............................................................................................................. 69
3.4.2 Effect of C. difficile on UC incident cases ...................................................... 70
3.5 Discussion................................................................................................................ 72
3.6 References ............................................................................................................... 76
3.7 Appendix 3.A List of postoperative complications and the respective ICD-10
code by category .................................................................................................... 88
3.8 Appendix 3.B Charlson comorbidities .................................................................... 93
3.9 Appendix 3.C Classification of colectomy approach and ICD-10 codes ................ 95
3.10 Appendix 3.D Types of postoperative complications ........................................... 97
3.11 Appendix 3.E Characteristics of colectomy patients stratified by developing
infectious complications in hospital ...................................................................... 98
3.12 Appendix 3.F Results of multilevel logistic regression model for the
development of infectious complications with C. difficile as primary predictor
(hospital was included as a random effect in the model) ...................................... 99
ix
CHAPTER FOUR: CHANGES IN THE ANNUAL INCIDENCE OF COLECTOMY
FOR COLORECTAL NEOPLASIA AMONG PATIENTS WITH
ULCERATIVE COLITIS: A POPULATION-BASED COHORT ........................ 100
4.1 Abstract.................................................................................................................. 101
4.2 Introduction ........................................................................................................... 102
4.3 Materials and Methods .......................................................................................... 103
4.3.1 Data sources and study population ................................................................ 103
4.3.1.1 Data Integration, Measurement, and Reporting Hospital Discharge
Abstract ................................................................................................. 103
4.3.1.2 Alberta Health Care Insurance Plan (AHCIP)..................................... 104
4.3.2 Identification of UC colectomy patients in CHZ hospitals and data
collection ....................................................................................................... 104
4.3.3 Identification of UC patients at risk for colorectal cancer in CHZ using
AHCIP ........................................................................................................... 106
4.3.4 Analyses ........................................................................................................ 107
4.3.4.1 Risk factors for colectomy for colorectal dysplasia or cancer ............ 107
4.3.4.2 Incidence of colectomy for colorectal dysplasia or cancer and
medically refractory disease ................................................................. 107
4.4 Results ................................................................................................................... 108
4.4.1 Risk factors for colectomy for colorectal dysplasia or cancer ...................... 108
4.4.2 Incidence of colectomy for colorectal neoplasia and medically refractory
disease ............................................................................................................ 109
4.5 Discussion.............................................................................................................. 110
4.6 References ............................................................................................................. 114
4.7 Appendix 4.A Comorbidity classifications ........................................................... 123
CHAPTER FIVE: COLORECTAL CANCER SURVEILLANCE IN PATIENTS
WITH INFLAMMATORY BOWEL DISEASE AND PRIMARY
SCLEROSING CHOLANGITIS: AN ECONOMIC EVALUATION .................. 124
5.1 Abstract.................................................................................................................. 125
5.2 Introduction ........................................................................................................... 126
5.3 Materials and Methods .......................................................................................... 127
5.3.1 Model overview ............................................................................................ 127
5.3.2 Model assumptions........................................................................................ 128
5.3.3 Model validation ........................................................................................... 129
5.3.4 Parameters ..................................................................................................... 130
5.3.4.1 Dysplasia and cancer related risk ........................................................ 130
5.3.4.2 Median time to liver transplant and graft loss ..................................... 130
5.3.4.3 Mortality .............................................................................................. 130
5.3.4.4 Colonoscopy performance characteristics. .......................................... 131
5.3.4.5 Costs .................................................................................................... 131
5.3.4.6 Utilities ................................................................................................ 132
5.3.5 Analyses ........................................................................................................ 132
5.4 Results ................................................................................................................... 134
5.4.1 Base-case analysis ......................................................................................... 134
5.4.2 Sensitivity analyses ....................................................................................... 135
5.4.3 Probabilistic sensitivity analysis ................................................................... 136
x
5.5 Discussion.............................................................................................................. 137
5.6 References ............................................................................................................. 142
5.7 Appendix 5.A Summary of health states included in the model ........................... 158
CHAPTER SIX: DISCUSSION OF WORK PRESENTED ........................................... 159
6.1 Overview of main findings .................................................................................... 160
6.2 Limitations ............................................................................................................. 163
6.2.1 Limitations of observational studies ............................................................. 163
6.2.2 Limitations of decision analysis study .......................................................... 167
6.3 Implications on Clinical Management and Public Health ..................................... 168
6.4 Future Research ..................................................................................................... 169
6.5 Conclusions ........................................................................................................... 171
REFERENCES ................................................................................................................ 173
xi
List of Tables
Table 2.1. Characteristics of ulcerative colitis patients admitted to hospital stratified
by flare (medically responsive) and emergent colectomy. Emergent colectomy
patients were further stratified into development of any postoperative
complication and development of infectious complication ....................................... 44
Table 2.2. Logistic regression results of emergent colectomy with C. difficile
diagnosis as primary exposure. .................................................................................. 47
Table 2.3. Logistic regression results for emergent colectomy patients having any
post-operative complication and infections post-operative complication with C.
difficile diagnosis as primary exposure. .................................................................... 48
Table 3.1. Characteristics of patients diagnosed with ulcerative colitis in Alberta,
Canada, between April 1st, 2003 and March 31st, 2010 stratified by colectomy. ...... 81
Table 3.2. Results of the competing risk regression and the Cox proportional hazard
models for time to colectomy and time to death since UC diagnosis with prior C.
difficile diagnosis as the primary predictor................................................................ 83
Table 3.3. Characteristics of colectomy patients stratified by developing any
postoperative complication in hospital ...................................................................... 84
Table 3.4. Results of multilevel regression models for the development of any
postoperative complication and a postoperative infectious complication with C.
difficile as primary predictor (facility was included as a random effect in the
models) ...................................................................................................................... 85
Table 4.1. Characteristics of ulcerative colitis patients admitted to Calgary Health
Zone hospitals that had a colectomy for colorectal dysplasia or cancer versus
colectomy for medically refractory UC. .................................................................. 118
Table 4.2. Logistic regression results comparing colectomy patients’ characteristics
(colorectal dysplasia or cancer vs. medically refractory) with year of colectomy
as primary exposure. ................................................................................................ 120
Table 5.1. Base-case parameter estimates and distributions and ranges used in the
probabilistic sensitivity analysis .............................................................................. 150
Table 5.2. The number of patients who developed dysplasia and cancer, as well as the
number of colonoscopies performed over a lifetime horizon for a hypothetical
1,000 average IBD-PSC patient cohort ................................................................... 152
Table 5.3. Base case-analysis results (in CAN$) for cost-utility analysis ....................... 153
Table 5.4. Scenario-analysis results (in CAN$) for cost-utility analysis ........................ 154
xii
List of Figures and Illustrations
Figure 2.1. Flow diagram illustrating the inclusion and exclusion criteria for
identifying patients admitted with a flare for ulcerative colitis and tested
Clostridium difficile infection in hospital and up to 90 days prior to admission....... 49
Figure 3.1. Flow diagram of patient selection criteria ....................................................... 86
Figure 3.2. Survival curves for the proportion of colectomy free UC patients (a) and
survival of UC patients (b) stratified by having C. difficile diagnosis (red line)
and no C. difficile diagnosis (blue line) ..................................................................... 87
Figure 4.1. Annual incidence of colectomy for colorectal dysplasia or cancer (green
line), and medically refractory disease (red line) among UC patients living in the
Calgary Health Zone between 1997 and 2009. The graph presents annual
adjusted incidence of colectomy per 1,000 UC patients at risk* ............................. 122
Figure 5.1. Bubble diagram summarizing the structure of the Markov model, flow of
patients through the health states, and the outcomes considered in this cost-utility
analysis. ................................................................................................................... 155
Figure 5.2. Incremental cost-effectiveness scatterplots with 95% confidence ellipses
for annual surveillance compared to biennial surveillance. The diagonal line
represents a willingness to pay threshold of CAN$50,000 per quality-adjusted
life-year. ................................................................................................................... 156
Figure 5.3. Cost-effectiveness acceptability curve. ......................................................... 157
xiii
List of Symbols, Abbreviations and Nomenclature
Symbol
Definition
5-ASA
ACCS
AHCIP
CAN
CCI
CD
CI
CLS
CRC
D
DAD
DALM
DIMR
5-aminosalycilic acid
Ambulatory care classification system
Alberta Health Care Insurance Plan
Canadian dollar
Canadian Classification of Health Intervention
Crohn’s disease
Confidence interval
Calgary Laboratory Services
Colorectal cancer
Days
Discharge abstract database
Dysplasia associated lesion or mass
Data integration, measurement and reporting
hospital discharge abstract
Enzyme immunosorbent assay
Expert opinion
Generalized linear model
Hazard ratio
Incremental cost-effectiveness ratio
International Classification of Diseases, 9th
edition
International Classification of Diseases, 10th
edition
International Classification of Diseases, 10th
edition, Canadian Classification
International Classification of Diseases, 10th
edition, Canadian Classification
International Classification of Diseases, 9th
edition, Clinical Modification
International Classification of Diseases, 10th
edition, Clinical Modification
Interquartile range
Liver transplant
National Institute for Health and Care
Excellence
Negative predictive value
Not significant
Odds ratio
Alberta’s Physician Claims
Positive predictive value
Primary sclerosing cholangitis
Probabilistic sensitivity analysis
EIA
EO
GLM
HR
ICER
ICD-9
ICD-10
ICD-9-CA
ICD-10-CA
ICD-9-CM
ICD-10-CM
IQR
LT
NICE
NPV
NS
OR
PC
PPV
PSC
PSA
xiv
QALY
RR
sHR
SIR
SMR
STROBE
TNF
UC
UDCA
USA
WTP
X2
Y
Quality adjusted life year
Risk ratio
sub Hazard ratio
Standardized incident rate
Standardized mortality ratio
Strengthening and Reporting of Observational
Studies in Epidemiology
Tumour necrosis factor
Ulcerative colitis
Ursodeoxycolic acid
United States
Willingness to pay
Chi-squared
Year
xv
Epigraph
“Whatever you want to do, if you want to be great at it, you have to love it and be
able to make sacrifices for it”
- Maya Angelou
xvi
Chapter One: Introduction
1
1.1 General Overview
Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) that is
characterized by chronic inflammation of the large bowel. This disease is common in
industrialized nations with North America and Europe reporting the highest incidences.
The increasing incidence throughout developed and developing countries has situated UC
as an emerging global disease. Canada has the highest prevalence of IBD in the world.1
There are ~104,000 people living with UC and ~4,500 new diagnosis every year.2 UC is
relapsing and remitting disease. The clinical course of UC is varied ranging from mild
disease severity that is controlled with medications (e.g. aminosalicylates) to a severe
course where patients undergo surgery to remove the colon. Overtime the prognosis for
UC has improved,3 which has been attributed to better understanding of the disease,
earlier diagnosis and advancements in patient management and medications. The
treatment goal of UC is to optimize quality of life by inducing and maintaining remission,
and preventing hospital admissions and surgery.
Colectomy for UC is curative in most patients; however, physicians and patients
aim to avoid colectomy due to the potential short- and long-term complications.4,5 A
systematic review and meta-analysis of population-based studies demonstrated that 16%
of UC patients will undergo a colectomy within 10 years of their diagnosis. The metaanalysis showed that the over the past several decades the 10-year risk of colectomy has
decreased over time.6 However, this meta-analysis was not designed to explain the causes
for the reduced risk of colectomy within 10 years of diagnosis. This manuscript, as well
as others,7 suggests that advances in therapeutic treatments for UC may explain the
decreasing incidence of colectomy in UC. However, non-medication factors, such as
2
infections (e.g. Clostridium difficile) and colorectal neoplasia, may influence the risk of
colectomy. This thesis will evaluate the effect of C. difficile and colorectal neoplasia on
the risk of colectomy for patients with UC.
Due to the high morbidity and mortality associated with colectomy, a major
research objective for clinicians and investigators is the prevention of colectomy for UC
patients.8-10 Treatment for UC involves suppressing the immune system in order to
control colonic inflammation. However, when these medications fail (i.e. medically
refractory disease) then a colectomy is indicated. A subset of UC patients undergoes
colectomy for gastrointestinal infections (e.g. C. difficile), and colorectal dysplasia and/or
cancer. Clostridium difficile and colorectal neoplasia are potential modifiable risk factors
for colectomy for UC. By understanding the clinical importance of these drivers of
colectomy, clinicians may use this information to mitigate the burden of colectomy for
UC. For this reason, this dissertation addresses important gaps in the literature on the
epidemiology of C. difficile infections and colorectal neoplasia among UC patients.
UC patients have a higher risk of acquiring a C. difficile infection when compared
to the non-IBD population.11-15 Additionally, C. difficile infection increases the risk of
surgery among UC patients, but the magnitude of the risk and its effect on short- and
long-term risk of colectomy is controversial in the literature.13,16-18 In addition, most of
these studies used administrative healthcare databases to identify patients with UC with
C. difficile infection. While these data sources are fairly accessible and allow researchers
to capture a large sample of patients, the accuracy of the C. difficile diagnostic code has
not been validated for UC.11,15,18-21 Chapters 2 and 3 uses a multi-method approach to
address these gaps in the literature. In Chapter 2 the effect of C. difficile infection on
3
colectomy is studied in a cohort of patients whereby every chart is reviewed to confirm
the diagnosis of C. difficile. In Chapter 3 the effect of C. difficile infection on colectomy
is studied in a larger sample using an administrative healthcare database after the
accuracy of the diagnostic code for C. difficile was validated. Overall, these two chapters
assess the risk of C. difficile infections following UC diagnosis, evaluate the effect of C.
difficile infection on the short- and long-term risk of colectomy among UC patients, and
determine the impact of C. difficile infections on postoperative complications. Chapter 2,
titled “Clostridium difficile infection worsens the prognosis of ulcerative colitis patients”
was accepted in the Canadian Journal of Gastroenterology. Chapter 3, titled
“Clostridium difficile infection diagnosed early in ulcerative colitis patients impacts
disease related outcomes: A population-based inception cohort study” will be submitted
to Gut.
UC patients also have a higher risk of developing colorectal cancer when
compared to the general population. Meta-analyses have demonstrated the cumulative
risk of developing colon cancer among patients with UC is 1% at 10-years and 8% at 20years.22 However, the risk overtime of colorectal cancer for UC patients is variable with
some studies reporting no changes and others demonstrating that the risk of colorectal
cancer has decreased over time.22-26 Inflammation is one of the most important risk
factors of developing colorectal neoplasia as several studies have demonstrated a direct
relationship between histological inflammation on previous biopsy specimens and the
development of colorectal dysplasia or cancer.22-24 As a result, advances in the medical
management of UC that have reduced chronic inflammation, also may have affected the
risk of dysplasia and colon cancer among patients. As a result, the decreasing risk of
4
surgery over time among UC patients may in part be explained by the reduced need for
colectomy for dysplasia and cancer. However, studies have not adequately evaluated
whether the reduced risk of colectomy overtime has been influenced by a reduction in the
need for colectomy for dysplasia and/or cancer. Chapter 4 titled “Changes in the annual
incidence of colectomy for colorectal neoplasia among UC patients: A population-base
study” addresses this gap in the literature by evaluating if the incidence of colectomy for
colorectal dysplasia or cancer has changed over time when compared to colectomy due to
medically refractory disease. This study will be submitted to Clinical Gastroenterology
and Hepatology.
The risk of colorectal dysplasia or cancer is considered to be minimal within the
first 8 to 10 years after UC diagnosis.25 However, patients with UC diagnosed with
concomitant primary sclerosing cholangitis (PSC) have 10 times higher the risk of
developing colorectal cancer and it occurs early in the disease.26 Due to this difference in
risk, contemporary guidelines for the management of IBD patients recommend
surveillance colonoscopy every 1-2 year, starting 8-10 years after diagnosis.27,28 On the
other hand, annual colonoscopy is recommended for IBD-PSC patients immediately after
both diseases are concurrently recognized.27,28 However, the cost-effectiveness of this
recommendation has never been determined and the implication of different time
intervals of surveillance colonoscopies has never been assessed. Chapter 5, titled
“Colorectal cancer surveillance in patients with inflammatory bowel disease and primary
sclerosing cholangitis: An economic evaluation” is the first cost-utility analysis that
evaluates different colorectal cancer surveillance intervals among IBD-PSC. This paper
was accepted for publication in the Inflammatory Bowel Disease.
5
UC is a chronic and potentially debilitating disease that is primarily diagnosed in
individuals at the prime of their lives.1 Consequently, UC imparts a significant burden to
patients, their families, and to the healthcare system. Colectomy remains among the
greatest burden to patients with UC. Thus, patients and physicians strive to avoid
colectomy. Important advances in the medical management of UC have led to a decrease
in the risk of colectomy overtime. However, other non-medication factors influence the
risk of colectomy including C. difficile infection and colorectal neoplasia. Collectively
the 4 studies in this thesis dissertation evaluate the impact of C. difficile infection and
colorectal neoplasia on colectomy for UC with the goal of improving understanding these
modifiable risk factors in order to reduce the burden of UC.
1.2 Background of Ulcerative Colitis
UC is thought to be a complex polygenic disorder, meaning that it is likely
associated with an interaction of multiple genes, in combination with the person’s
lifestyle and environmental factors.29 Characteristic lesions of UC include continuous
inflammation of colonic mucosa (beginning in the rectum and continuing proximally).30
Typically, patients with UC present with a history of rectal bleeding, tenesmus, diarrhoea
and abdominal pain. The diagnosis of UC is based on the patient’s clinical history in
conjunction with endoscopy and confirmation by histopathology of the affected tissue.
Most patients require daily medications to induce and maintain clinical remission and
when these drugs fail a colectomy (i.e. resection of the colon) is often required. The
following sections will overview the epidemiology, natural history, and management of
UC.
6
1.2.1 Burden of ulcerative colitis: incidence, prevalence and economic impact
UC is common in industrialized nations, with Northern Europe and North
America reporting the highest incidences.1 In Europe, the incidence of UC ranges from
0.7 to 9.8 cases per 100,000 person-years.1 North America has the highest incidence of
UC, ranging from 3.1 to 14.6 cases per 100,000 persons-years.1 Approximately 104,000
people live with UC in Canada, and ~4,500 people are diagnosed with UC every year.2
The fact that the incidence is increasing overtime in both, developed and developing
countries, suggests that UC is a global disease.1 Diagnosis occurs commonly between the
second and third decade of life; however, some studies suggest a bimodal age distribution
with a second smaller peak later in life.1 The prognosis of UC has improved dramatically
with better patient management. The overall mortality is similar to the general population
(standardized mortality ratio (SMR): 1.1; 95% confidence interval (CI): 0.9-1.2).3 The
global prevalence is expected to continue to rise due to the fact that UC is often
diagnosed in the young population and mortality is low. Because of the chronic nature of
the disease where most patients require continuous or intermittent treatment throughout
their lifetime, UC is a costly disease to patients and society.
A recent meta-analysis reported that the annual direct medical costs (in-patient,
out-patient, medications, physician consults, diagnostics and emergency visits) ranged
from $3,374 to 11,477 and €2,210 to 10,395 per patient in the US and Europe,
respectively.31 Further, between $16,442 and 55,541 is spent per surgery (includes all inpatient costs) in the USA for UC patients.31 In 2012, annual costs of IBD in Canada were
estimated to be $2.8 billion, with $1.2 billion being related to direct medical costs
(medications, hospitalizations, physician costs and procedures) and the remainder $1.6
7
billion is due to indirect costs (to society and the patient, i.e. productivity losses due to
disability and sickness, early retirement or death).2
1.2.2 Risk factors
UC arises from an interaction of multiple genes in combination with the person’s
lifestyle and environmental exposures. The first clues suggesting genetic susceptibility
came from studies showing that IBD occurs in “clusters” within families.32 Since then
numerous genes associated with the development of UC have been identified. A genomewide analysis by Jostins et al. identified 163 susceptibility genes for IBD.33 One hundred
and ten of these genes are associated with both UC and Crohn’s disease, whereas only 30
are specific for Crohn’s disease and 23 are UC-specific.33 In UC, most of these genes are
associated with the immune system and intestinal barrier function.34 However, the fact
that these genes are not present among all UC patients and that the incidence of IBD is
rising in developing nations as they become industrialized suggest that individual
lifestyle and environmental exposures influence the development of UC.
One theory to explain the rise of UC in industrialized nations is the Hygiene
Hypothesis, which suggests that reduced exposure to enteric microorganisms during early
childhood may lead to an inappropriate immunological response upon exposure to
gastrointestinal infection later in life.35 Similarly, the “hit and run” hypothesis suggests
that pathogens may initiate an initial inflammatory response that, in the presence of
genetic alterations is not fully controlled.36 Several environmental risk factors have been
shown to influence the development of UC including smoking, appendectomy and
breastfeeding.37 Environmental risk factors (e.g. diet or smoking) might influence the
development of UC by shifting microbiota composition and diversity.38-41
8
1.2.3 Natural history of the disease
The disease course of UC is characterized by periods of disease exacerbations
(i.e., flares) followed by periods of remission. Disease activity varies from mild to severe.
The majority of the patients have moderate UC, which has the classical intermittent
disease course. A subset of the UC population has mild UC, experiencing one relapsing
episode followed by long-term remission.42 Lastly, some patients with UC experience a
severe form of UC, characterized by fulminant medically refractory disease with a need
of colectomy within a short period form their diagnosis. In addition to these flares, one
third of UC patients will develop extraintestinal manifestations including arthritis (e.g.
ankylosing spondylitis and sacroiliitis), erythema nodosum, pyoderma gangrenosum,
uveitis and PSC.43
Primary sclerosing cholangitis is a chronic cholestatic liver disease, characterized
by chronic inflammation, and destruction and fibrosis of the intrahepatic and/or
extrahepatic biliary tree.44 Liver transplant is the only curative option for these patients.
Approximately 68% of PSC patients will also have a diagnosis of IBD with the most
common type being UC.45 Most often the IBD diagnosis precedes PSC diagnosis. Their
prognosis is worse than those with only UC, with a mortality risk 3 times higher than the
general population.46 The median time to liver transplant or death among these patients is
12-13 years.26,47
Both disease severity and the number of relapses are influenced by the extent of
disease activity at presentation and +/- progression during follow up. Based on the
Montreal Classification, disease extent is stratified into 3 subgroups: proctitis, left-sided
colitis, and pancolitis or extensive UC. Proctitis is limited to the rectum. Left-sided UC is
9
the most common disease extent at diagnosis and extends up to the splenic flexure.
Pancolitis extends proximal to the splenic flexure and is the most severe of the three
forms.48 Patients presenting at diagnosis with left-sided UC often progress to pancolitis.
Additionally, age at diagnosis is an important determinant on disease extent and
progression. For example, in one study, diagnosis at >40 years of age was protective for
developing pancolitis.49
1.2.4 Medications
Disease severity varies with some patients having a mild course that is
successfully controlled by medications, whereas others suffer from refractory disease
unresponsive to medication leading to surgical removal of the colon. Even among those
with successfully controlled UC, the response to medications is quite varied.50
The first goal of medical treatment of UC is to suppress inflammation and induce
remission while allowing the tissues to heal. The second goal is to maintain remission
and prevent a relapse. Inducing and maintaining remission improves quality of life, and
reduces the likelihood of colectomy. The choice of treatment varies according to disease
severity, disease extent, and disease course. Additionally, physicians also take into
consideration patient’s extraintestinal manifestations, age, comorbidities and patient’s
preferences when determining which therapeutic approach is optimal of each patient.
Five-ASA is the treatment of choice for patients with mild to moderate UC for
induction as well as for remission therapy.51 A recent Cochrane Systematic Review of 11
trials involving 1,598 patients confirmed the effectiveness of 5-ASA for mild to moderate
UC demonstrating it was superior to placebo for maintaining remission.52 However,
several studies have reported compliance issues because patients are required to take
10
multiple doses every day. Poor adherence to medications have been associated with
relapse of disease activity53 Steroids are used for patients unresponsive to 5-ASA.51
Corticosteroids are effective therapy in inducing remission of moderate-to-severe UC.54
However, corticosteroids are not effective for maintenance of remission and prolonged
use of steroids is associated with multiple serious adverse events.27
Immunomodulatory drugs such as azathioprine and 6-mercaptopurine are used for
patients unresponsive to 5-ASA or for patients with chronic active disease that is
refractory or dependent to corticosteroids.51 However, the major limitation of
immunomodulators is that they are not effective at induction of remission and are
associated with toxicity (e.g. leukopenia and pancreatitis).55 Infliximab and adalimumab
are monoclonal antibodies against tumour necrosis factor-alpha (anti-TNF) that is
indicated for the induction and remission of UC. A network meta-analysis demonstrated
similar efficacy across anti-TNF medications.56 Overall, patients were 2.45 times more
likely to achieve remission (RR=2.45; 95% CI: 1.72-3.47) and 1.65 times more likely to
improve their disease activity (RR=1.65; 95% CI: 1.37-1.99) when compared to patients
treated with placebo.56 Additionally, another meta-analysis demonstrated that infliximab
was associated with lower risk of surgery (OR=0.57; 95% CI: 0.037-0.88).57
1.2.5 Surgery
Colectomy is indicated for patients who do not respond to medical treatment or
for those who develop colorectal dysplasia or cancer. A restorative proctocolectomy is
considered to be curative. Additionally, following colectomy, the chance of developing
colorectal dysplasia or cancer is reduced dramatically. However, the procedure is
associated with short-term postoperative complications including a 3.2% risk of
11
postoperative mortality.58 Additionally, long-term complications may arise including
pouchitis, reduced fertility, sexual dysfunction, fecal incontinence and pouch
complications.59,60 It is, therefore, not surprising that patients prefer medical management
as opposed to undergoing surgery.4,5
Colectomies are commonly divided into elective or emergent procedures. Elective
colectomies are performed among those with refractory disease or due to colorectal
dysplasia or cancer. On the other hand, emergent colectomies are done in life-threatening
situations where hospitalized patients fail to respond to medical management or
experience complications and are associated with higher risk of developing postoperative
complications and mortality.
By 10 years following a UC diagnosis approximately 16% of the patients have
undergone a colectomy.6 The highest colectomy risk is observed during the first years
after diagnosis, most likely due to patients presenting with fulminant colitis.61 Still, the
overall risk of surgery among UC patients is decreasing over time suggesting that earlier
diagnosis, better patient management, and the development of new medications play an
important role.6 However, the incidence of emergent colectomies (which account for
~50% of colectomies performed) has remained stable in the last decade, whereas elective
procedures decreased significantly.7
The reason why the risk of colectomy is decreasing over time is not completely
understood.
Advances
in
medical
management
from
the
introduction
of
immunosuppressants in the 1990’s to the advent of anti-TNF therapies in the 2000’s
likely explains the reduction in the risk of colectomy for patients with UC. However,
other non-medication factors may also, in part, explain the reduced risk of colectomy.
12
There previous advances in medical management that led to reduced chronic
inflammation may have also decreased the risk of developing dysplasia or cancer among
patients with UC. In addition, C difficile infections influence the risk of colectomy for
UC patients. Studying factors such as colorectal neoplasia and C difficile infection is
important because these are potentially modifiable risk factors of the colectomy.
1.3 Clostridium difficile
Currently, the most common gastrointestinal infectious microorganism associated
with relapse of UC is C. difficile. Clostridium difficile is a Gram-positive spore forming
anaerobe commonly associated with nosocomial acquisition and/or exposure to
antibiotics. Clinical signs vary and can range from asymptomatic, to diarrhea, sepsis and
even death. An increase in incidence of C. difficile infections during the last decade20,62
and outbreaks of a hyper-virulent strain in Quebec, Canada,63 have increased public
awareness of the importance of this bacterium. IBD is not considered a risk factor for
C.difficile infection,64 which is a completely different opinion from 30 years ago when
the role of C. difficile in IBD was not considered to be important.65-69 Several
epidemiological studies have consistently reported higher ocurrence of C. difficile
infections among IBD patients when compared to the non-IBD population with the
highest number of C. difficile infections reported among UC patients.11-15 Nguyen et al.
reported an eight and three times higher prevalence of C. difficile infection among UC
patients when compared to the non-IBD and CD patients, respectively.11 In the US, the
incidence of C. difficile infections among UC patients has been increasing with the
incidence nearly doubling between 1998 and 2004 (26.6 per 1,000 admissions in 1998 vs.
13
51.2 per 1,000 admissions in 2004). Similar findings have been reported among single
center studies and other studies using administrative hospital databases from North
America and the UK.12,13,16 In contrast, studies from The Netherlands and Germany
reported a low prevalence of C. difficile infection among UC patients.70,71 This might
indicate that geographic variation and the background C. difficile infection pressure may
play a role. However, heterogeneity between studies may also be explained by the use of
different diagnostic tests, case definitions and comparator groups. In addition, studies
using administrative health databases have not validated the accuracy of the diagnostic
code for C. difficile infection among UC patients.15
Among non-IBD patients, risk factors for C. difficile infection include antibiotic
use, older age, presence of comorbidities, immunosuppression, admission to hospital and
prolonged hospitalization.72 Alteration of the gut microbiota, as a result of antibiotic
usage, allows C. difficile to proliferate and produce toxins causing the observed clinical
signs.73 However, the association between antibiotic use and an increased risk of C.
difficile infection is not commonly observed among UC patients,13 perhaps due to the
already altered gut microbiota characteristic of UC.74 As in the non-IBD patients, the risk
of C. difficile infection increases with age and comorbidities. However, IBD patients’
average age at C. difficile diagnosis is much lower than in the general population.15
Immunosuppression has been recognized as a risk factor for C. difficile infection among
UC patients. The use of corticosteroids, thiopurines, and methotrexate has been
consistently associated with an increased risk of C. difficile infection while results for
infliximab have been inconsistent.13,75,76 Greater disease extent has been associated with
C. difficile infections among UC patients.77 However, one could argue that this is as a
14
result of the underlying immunosuppression due to extensive medication use to control
UC. Recently, Ananthakishnan et al. identified several IBD-related risk loci associated
with C. difficile infection with several of them being associated with the host immune
response.76
The gold standard for the detection of C. difficile is stool culture and toxin
detection or cell culture cytotoxicity assay.78 However, this test is not clinically practical
because it is resource intensive and has a slow turnaround time. As a result, enzyme
immunoassays (EIA) that detect C. difficile toxins have replaced stool culture in a clinical
laboratory setting. However, EIAs are less sensitive (range: 31-99%) while specificity
ranges between 65 and 100%.79 A two-step approach is recommended to address the
issue of low positive predictive value due to low prevalence of C. difficile infection and
suboptimal specificity, which would result in a decrease of false-positive results.79 For
example, during the study period in Chapter 2 (from 2000 to 2009), Calgary Laboratory
Services used an enzyme immunosorbent assay (EIA) for toxins A and B (TechLab C.
difficile TOX A/B II, TechLab Enteric Diagnostics, USA) as a screening method; and
tested the stool sample with a second EIA (Triage Clostridium difficile panel, Biosite
Diagnostics, USA) if the results of the first EIA were positive.
Supportive care and antibiotics (metronidazole or vancomycin) are the mainstay
treatments for C. difficile infections. Metronidazole is cheaper, can be given
intravenously and is recommended for patients with mild and non-recurrent C. difficile
infection. On the other hand, vancomycin is the drug of choice for severe and recurrent C.
difficile infection. Fecal microbiota transplant has been proven to be effective for
recurrent C. difficile infections.80,81 The American College of Gastroenterology
15
Clostridium difficile infection task force suggested that IBD patients may begin treatment
for both C. difficile infection and the IBD flare while waiting for C. difficile test results;
but escalation of immunosuppression should be avoided in the setting of C. difficile
infection.82 However, these recommendations are based on low-quality evidence.82
It is not clear whether C. difficile infection causes an infectious colitis
superimposed on UC or precipitates an UC flare. A C. difficile diagnosis among UC
patients is associated with a higher rate of endoscopies, hospitalizations and
complications such as toxic megacolon, colectomy, and mortality. Whether C. difficile
infection increases the risk of colectomy has been a matter of debate. While some studies
demonstrate that C. difficile infection is associated with a short-term colectomy risk,13,16
other studies demonstrate that a C. difficile infection increases the long-term colectomy
risk17,18 and one study found a negative association.11 In addition, diagnosis of C. difficile
infection among UC patients is associated with higher mortality rates.11,16,83
Thus, C. difficile infection is an important modifiable complication of UC that
may increase the risk of colectomy. This dissertation will enhance the literature by
validating the accuracy of the code for C. difficile used in an administrative database,
determine the risk of acquiring C. difficile after the diagnosis of UC, assess the effect of
C. difficile infection on the risk of colectomy and mortality, and evaluate the impact of C.
difficile infection on postoperative outcomes following colectomy for UC.
1.4 Colorectal Dysplasia and Cancer
Inflammatory bowel disease is one of the most important high-risk conditions for
colorectal cancer. Among IBD patients, colorectal cancer arises from an inflammation16
dysplasia carcinoma sequence.84,85 However, in clinical practice, cancers can arise
without observing this sequence.
The incidence of colorectal cancer has been described extensively in the literature
and summarized in multiple meta-analyses.25,86-89 Eaden et al. published the first metaanalysis describing the colorectal cancer risk among UC patients and reported the 10-,
20- and 30-year cumulative risk of colorectal cancer to be 1, 8 and 18%, respectively.86
Since then, many meta-analyses have been performed with contradicting reports most
likely due to a variety of methodologies and inclusion criteria. Only one of these studies
reported that the incidence of colorectal cancer is increasing with an overall incidence
rate of 14 cases per 1,000 patient-years.87 Two studies reported that the incidence of
colorectal cancer has decreased over time with only one performing a meta-regression,
but the results were not statistically significant.88,89
In the setting of IBD, some of the factors that increase the risk of colorectal
cancer are: age, gender, disease extent, disease duration, PSC, and familial history of
colorectal cancer.25,90,91 Disease duration is the most important risk factor for the
development of dysplasia or colorectal cancer – i.e. the longer duration the disease the
higher the incidence of neoplasia. Individuals who are young at diagnosis are at increased
risk of developing cancer or dysplasia because of the higher burden of chronic
inflammation. Men have a higher risk of developing colorectal cancer than females.25
Inflammation plays an important role in the development colorectal cancer.92 Several
studies have demonstrated a direct relationship between the degree of histological
inflammation on previous biopsy specimens and the development of colorectal cancer.2224
Patients with pancolitis have a higher risk compared to patients with left-sided UC or
17
proctitis.88,93 The increased risk of development of colorectal cancer observed in
longstanding colitis and in greater disease extent supports the role of inflammation as a
predisposing factor. The cumulative risk of developing cancer during the first 10 years of
UC diagnosis is minimal (<1%).25 According to a meta-analysis by Jess et al., the 20-year
cumulative risk of colorectal cancer is between 1.1-5.3%.25
Patients with UC and PSC have an increased risk of developing colorectal
neoplasia when compared to patients with UC alone (OR=4.79; 95% CI: 3.58-6.41), but
the reason for this is not completely understood.94 A recent population-based study
concluded that the risk of developing CRC among IBD-PSC patients is 10 times higher
and their diagnosis of cancer/dysplasia occurs at a younger age when compared to the
general IBD population.26 In addition, cancers diagnosed among IBD-PSC patients are
more often located in the right colon with an overall worse prognosis.95 The risk of
colorectal cancer continues after liver transplant with an incidence rate of 13.5 per 1000
person-years (95% CI: 8.7-18.2).96
Typically dysplasia is categorized as flat or dysplasia associated lesion or mass
(DALM).30 The IBD dysplasia morphology group defined dysplasia as “an unequivocal
neoplastic alteration of the colonic epithelium”.97 This same group developed a dysplasia
grading system classifying them into: indefinite, low grade and high grade based on the
extent of affected epithelium.97 Diagnosis of colorectal dysplasia and cancer is based on
the traditional random biopsy-driven colonoscopy surveillance practice that most
gastroenterologists currently apply. Novel techniques with better sensitivity of identifying
colonic dysplasia are available in certain academic centers including mucosal dye spray
during colonoscopy (sensitivity= 83.5% and specificity= 91.3%) and confocal
18
endomicroscopy (sensitivity= 81% and specificity= 88%).98-100 While these techniques
may improve the detection rates of dysplasia and early CRC, they are more time
consuming, require additional training, and the costs to integrate these modalities into an
endoscopy unit are higher. Further, the vast majority of practices do not have access to
these advanced techniques for dysplasia surveillance.
A complete proctocolectomy is indicated following the diagnosis of multi-focal
low-grade dysplasia, high-grade dysplasia, dysplasia associated lesion or mass, and
adenocarcinoma. However, the management of unifocal low-grade dysplasia is
controversial. Though, the American College of Gastroenterology recommends
colectomy also for low-grade dysplasia27 due to the increase likelihood of finding
synchronous cancer in the resected specimen.101,102 Ullman et al. demonstrated that 23%
of the patients who had a colectomy for low-grade dysplasia had synchronous advanced
neoplasia.101 Further, 53% of the flat low-grade dysplasia progressed to advanced
neoplasia by 5 years.101 However, despite the increased risk of developing cancer, 60%
UC patients say that, if diagnosed with dysplasia, they would choose not to undergo a
recommended elective colectomy and instead choose to be followed routinely with
surveillance colonoscopy.103
Colorectal cancer prevention efforts have been focused around early detection
(i.e. dysplasia detection). The identification of these risk factors and knowledge of the
behaviour and progression of dysplasia in UC was crucial in the development of
surveillance strategies. Several strategies have been recommended to improve survival
among patients diagnosed with colorectal cancer. Contemporary guidelines for the
management of IBD patients recommend surveillance colonoscopy every 1 to 2 years, 8
19
to 10 years after diagnosis based on several economic analyses assessing the costeffectiveness on different surveillance strategies.27,28 In contrast, annual colonoscopy is
recommended for IBD-PSC patients immediately after both diseases are concurrently
recognized but the cost-effectiveness of this intervention is unknown.27,28
Chemoprevention is defined as “the use of natural or synthetic chemical agents to
reverse, suppress or delay the process of carcinogenesis”.90 However, chemoprevention
has been a matter of controversy. Ursodeoxycholic acid (UDCA), 5-ASA and biologics
are the most common chemoprotectants used among IBD patients. While a recent metaanalysis concluded that both, UDCA and 5-ASA, do not confer a protective effect against
the development of colorectal cancer, recent experimental colitis models and case-control
studies suggest that biologics could decrease the risk of colorectal cancer.104-107
Thus, this dissertation will enhance the literature by determining if the observed
overall decreased risk of colectomy is in part due to a decrease in risk of colectomy due
to colorectal neoplasia and evaluates the cost-effectiveness of different colorectal cancer
surveillance intervals among IBD-PSC patients.
1.5 Thesis Objectives and Hypothesis
The overall aim of this dissertation was to address important gaps on the
epidemiology of C. difficile infections and colorectal neoplasia among UC patients in
order to identify areas or ways of further decreasing the risk of colectomy among UC
patients. Below are the specific objectives of each chapter along with their respective
hypotheses.
20
1.5.1 Chapter 2: Clostridium difficile infection worsens the prognosis of ulcerative
colitis
1.5.1.1 Objective 1: Determine if C. difficile diagnosis in hospital or 90-days prior to
hospital admission among UC patients was associated with having an emergent
colectomy.
Hypothesis: UC patients hospitalized due to disease exacerbation with a diagnosis
of C. difficile have higher odds of having an emergency colectomy when
compared to patients who were C. difficile-negative.
1.5.1.2 Objective 2: Among UC patients who had an emergent colectomy, determine if C.
difficile diagnosis in hospital or 90-days prior to hospital admission among UC
patients was associated with an increased risk of postoperative complication.
Hypothesis: UC patients who had an emergent colectomy and were diagnosed
with C. difficile infection in-hospital or 90 days prior to hospital admission have
higher odds of developing postoperative complications when compared to those
UC patients who were C. difficile-negative.
1.5.2 Chapter 3: Clostridium difficile infection diagnosed early in ulcerative colitis
patients impacts disease related outcomes: A population-based inception cohort
1.5.2.1 Objective 1: To validate the ICD-10 code for C. difficile diagnosis among UC
patients.
Hypothesis: The ICD-10 code C. difficile diagnosis is valid in detecting UC
patients diagnosed with C. difficile.
1.5.2.2 Objective 2: Determine the 1-, 3-, and 5-year cumulative risk of a C. difficile
diagnosis after being diagnosed with UC.
Hypothesis: Among UC patients, the risk of acquiring a C. difficile infection
increases with time.
21
1.5.2.3 Objective 3: Determine the effect of a C. difficile diagnosis on the risk of
colectomy and mortality.
Hypothesis 1: UC patients with a C. difficile diagnosis have a higher risk of
colectomy when compared to UC patients without a C. difficile diagnosis.
Hypothesis 2: The overall mortality risk among UC patients diagnosed with C.
difficile is higher when compared to UC patients without a C. difficile diagnosis.
1.5.2.4 Objective 4: Determine the association between a C. difficile diagnosis and
developing post-operative complications.
Hypothesis: UC patients who had a colectomy and were diagnosed with C.
difficile infection have higher odds of developing postoperative complications in
hospital when compared to those UC patients who were C. difficile-negative.
1.5.3 Chapter 4: Changes in the annual incidence of colectomy for colorectal neoplasia
among patients with ulcerative colitis: A population-based cohort
1.5.3.1 Objective 1: To evaluate the risk profile of patients who had a colectomy for
dysplasia and cancer.
Hypothesis: UC patients who have a colectomy for colorectal dysplasia and
cancer have distinct characteristics compared to patients who had a colectomy for
medically refractory disease.
1.5.3.2 Objective 2: Determine if the incidence of colectomy for colorectal neoplasia has
decreased over time.
Hypothesis: The incidence of colectomy for colorectal dysplasia has decreased
over time.
22
1.5.4 Chapter 5: Colorectal cancer surveillance in patients with inflammatory bowel
disease and primary sclerosing cholangitis: An economic evaluation
1.5.4.1 Objective 1: To determine whether annual colonoscopy among patients with IBDPSC is cost-effective compared to less frequent intervals from a publicly funded
health care system perspective.
Hypothesis: Annual colonoscopy is the most cost-effective strategy compared to
less frequent intervals.
23
Chapter Two: Clostridium difficile infection worsens the prognosis of ulcerative
colitis
24
2.1 Abstract
The impact of Clostridium difficile infections among ulcerative colitis (UC)
patients is well characterized. However, there is little knowledge about the association
between C. difficile infections and postoperative complications among UC patients. The
objective of this study was to determine if C. difficile infection was associated with
having an emergent colectomy and postoperative complications. The present population
based study identified UC patients admitted to Calgary Health Zone hospitals for a flare
from 2000-2009. C. difficile toxin tests ordered in hospital or 90 days prior to hospital
admission were provided by Calgary Laboratory Services (Calgary, Alberta). Hospital
records were reviewed to confirm diagnoses and extract clinical data. Multivariate
logistic regression analyses were perfomed among individuals tested for C. difficile to
examine the association between C. difficile infection and emergent colectomy and
diagnosis of any postoperative complications and, secondarily, an infectious
postoperative complication. Estimates were presented as adjusted ORs with 95% CIs. C.
difficile was tested in 278 (58%) UC patients and 6.1% were positive. C. difficile
infection was associated with an increased risk for an emergent colectomy (adjusted
OR=3.39; 95% CI: 1.02-11.23). Additionally, a preoperative diagnosis of C. difficile was
significantly associated with the development of postoperative infectious complications
(OR=4.76; 95% CI: 1.10-20.63). C. difficile diagnosis worsened the prognosis of UC by
increasing the risk of colectomy and postoperative infectious complications following
colectomy. Future studies are needed to explore whether early detection and aggressive
management of C. difficile infection will improve UC outcomes.
25
2.2 Introduction
Ulcerative colitis (UC) is characterized by periods of remission followed by
periods of disease activity that decrease quality of life. In western nations, the prevalence
of UC was as high as 500 per 100,000 persons,1 and approximately 15% of UC patients
undergo surgical resection of the colon within the first 10 years of diagnosis.2,3
Furthermore, UC patients who undergo colectomy are at increased risk of postoperative
morbidity and mortality.4,5
The increased incidence and severity of Clostridium difficile infections during the
last decade,6,7 along with outbreaks of more virulent strains,8 have increased public and
practitioner awareness of the importance of this pathogen. While antibiotic exposure is
the primary risk factor for C. difficile infection, UC has become recognized as an
independent risk factor.9,10 C. difficile infection risk among UC patients have increased
over time, with C. difficile prevalence doubling from 26.6 to 51.2 per 1,000 discharges
from 1998-2004.11 C. difficile infection may worsen the prognosis of UC because the
infection has been associated with higher morbidity and increased risk of surgery up to
one year after diagnosis of infection.11-13 Consequently, C. difficile diagnosis is associated
with higher hospital costs among inflammatory bowel disease patients.11 These studies
assessed only inpatient C. difficile test results and, thus, missed the impact of C. difficile
infections diagnosed before hospital admission.11,14
We studied whether C. difficile diagnosis in hospital or 90 days before hospital
admission among UC patients was associated with an emergent colectomy and,
furthermore, the development of postoperative complications.
26
2.3 Materials and Methods
2.3.1 Data sources
The Data Integration, Measurement and Reporting Hospital Discharge Abstract
Database was used to capture hospitalizations of UC patients in the Calgary Health Zone
(CHZ) in Alberta. The CHZ is a population-based health authority that provides health
care to Calgary residents and >20 nearby cities under a public, single-payer system.15
This database includes patients’ demographic data, admission and discharge date, and 42
diagnostic and 25 procedural fields using the International Classification of Disease,
Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM & ICD-10-CM) and the
Canadian Classification of Health Intervention (CCI).4,16
The population-based Calgary Laboratory Services database was used to identify
UC patients who underwent stool testing for C. difficile. Calgary Laboratory Services
confirmed the C. difficile infection using a two-step approach: an enzyme immunosorbent
assay (EIA) for toxins A and B (TechLab C. difficile TOX A/B II, TechLab Enteric
Diagnostics, USA) was used as a screening method; and the stool sample was tested with
a second EIA (Triage Clostridium difficile panel, Biosite Diagnostics, USA) if the results
of the first EIA were positive. A sample was considered to be C. difficile positive if both
tests were positive.
2.3.2 Study population
The study population consisted of adults (≥ 18 years) admitted emergently to a
CHZ hospital between January 1, 2000 and December 31, 2009 for a UC flare. The
approach for identifying the study population was previously validated.16 First, the Data
Integration, Measurement and Reporting Hospital Discharge Abstract database was used
27
to identify patients admitted to hospital with a diagnosis of UC (ICD-9-CM 556.X or
ICD-10-CA K51.X) in any diagnostic position and a procedural code for colectomy
(ICD-9-CM 45.7, 45.8 or CCI 1.NM.87, 1.NM.89, 1.NM.91, 1.NQ.89, 1.NQ.90). The
colectomy admission was recorded as the index date. Second, from the remaining
discharge abstracts, UC patients admitted to hospital for a flare were identified from
admissions with a UC code in the primary diagnostic position. Among patients with UC
who were admitted to hospital for a flare, but did not undergo a colectomy, all hospital
admissions that occurred within the study period (2000-2009) were recorded and one
admission was randomly selected as the index date.16 Patients who were not tested for C.
difficile in hospital or within 90 days of hospitalization were not included in the study
population. Figure 2.1 illustrates is the inclusion and exclusion criteria that defined the
study population. Medical charts of all UC patients identified were reviewed to confirm
that the UC patients were admitted to hospital emergently for a UC flare and to collect
patients’ surgical and medical history relevant to the index admission.
2.3.3 Selection of cases and controls
The primary case definition was having an emergent colectomy during the index
admission. Colectomy was defined as emergent if the decision to perform colectomy was
made during the admission and after failing to respond to medical management, or
because the patient experienced a complication. Controls were the remaining UC patients
discharged from hospital without a colectomy after responding to medical management.
The secondary case definition was the development of any postoperative
complication, and, specifically, an infectious postoperative complication among UC
patients who underwent an emergent colectomy. Postoperative complications were
28
defined as an unexpected medical event that occurred between the start of the operation
and discharge from the hospital. These complications were classified into seven
complication categories including postoperative infection (Appendix 2.A). Complications
were stratified by severity using the Clavien classification of surgical complications.17
The development of postoperative complications was recorded if the patient experienced
at least one complication described under any of the categories graded as Clavien II or
higher (ie, requiring medical or surgical intervention or leading to death). C. difficile
infection was not considered to be a postoperative complication.4 The definition of
postoperative complication has been previously validated for UC.16
2.3.4 Exposure
The primary exposure was a diagnosis of C. difficile infection in hospital or 90
days before the index admission. Ninety days before hospital admission was defined a
priori to ensure that patients defined as a negative test for C. difficile were not treated for
a C. difficile infection prior to hospital admission. A patient was classified as diagnosed
with C. difficile if at least one test result was positive during the predetermined period.
The date of the first positive test was recorded as the index date of infection. C. difficile
diagnosis was confirmed by medical chart review. Patients were classified as C. difficile
negative if all test results during the predetermined period were negative.
2.3.5 Covariates
Additional demographic and clinical data extracted from chart review included:
age (stratified as: 18-32, 33-47, ≥ 48 year based on the tercile of the cohort); sex; disease
extent (left-sided versus pancolitis); disease duration (defined as the interval between UC
diagnosis and admission date), length of flare (< 2, 2 to 8, > 8 weeks); and smoking status
29
(current, ex-smoker, never). Inflammatory bowel disease medications (5-aminosalicylic
acid or sulfasalazine, azathioprine, corticosteroids, and infliximab) taken at time of
admission and/or administered in-hospital were recorded. Patients were classified as
having comorbidities (i.e., health conditions occurring before hospitalization) if they had
at least one of the comorbidities listed in Appendix 2.B. The definition of comorbidity
has been previously validated for UC.16
2.3.6 Statistical analysis
The associations between the outcome and categorical variables were tested using
the Fisher’s exact test or the X2 test. Continuous variables were expressed as medians
with interquartile ranges (IQR) and compared using the Wilcoxon rank-sum test.
Multivariate logistic regression analysis was performed to determine the association
between the need for an emergent colectomy and diagnosis of C. difficile (defined a
priori) after adjusting for other covariates. Age was a priori forced into the regression
model. For the other covariates, a backwards elimination approach was used to examine
independent effects of additional variables on the need of emergent surgery with an entry
P-value of < 0.20. Variables were kept in the model if: the two-sided P-value was < 0.05;
or there was evidence of confounding because their removal resulted in a 30% change in
the estimate of the primary exposure. The variance inflation factor was used to measure
multicollinearity among the independent variables. Multicollinearity was considered
negligible if the variance inflation factor was < 10. Interactions between C. difficile
positivity and variables that were indepedently associated with colectomy were tested and
included in the model if the likelihood ratio test was statistically significant (P < 0.05).
Point estimates were presented as adjusted ORs with 95% CIs.
30
The association between C. difficile infection and the development of
postoperative complications was also evaluated. Multivariate logistic regression was
performed to examine the association between postoperative complications (and then
postoperative infection separately) and C. difficile diagnosis (defined a priori) after
adjusting for other covariates. A backwards elimination approach was used to examine
the independent effects of variables on the development of postoperative complications
(and postoperative infection) using the same procedure as described above.
A sensitivity analysis was performed to determine whether the timing of C.
difficile diagnosis affected the association with the outcomes. For this, logistic regression
models were recalculated with C. difficile diagnosis defined as having at least one
positive test result in hospital or 14 days before the index admission. Patients who were
not tested for C. difficile in hospital or 90 days before admission were included in a
second sensitivity analysis. Logistic regression models were recalculated for all three
outcomes: emergent colectomy, any postopertative complication and infectious
complication.
All analyses were performed using STATA version 11 (STATA Corp, USA). The
study was approved by the Conjoint Health Research Ethics Board at the University of
Calgary (Calgary, Alberta). The present study was conducted in accordance with the
strengthening of the reporting of observational studies in epidemiology (STROBE)
statement.18
31
2.4 Results
A total of 278 UC patients met the inclusion criteria. Table 2.1 summarizes the
baseline characteristics of the study population. The indications for an emergent
colectomy were bowel complication (n = 9), cancer/dysplasia (n = 1), and failed medical
management in hospital (n = 92). C. difficile diagnosis was recorded in 11 (11%) of UC
patients who underwent an emergent colectomy compared with six (3%) patients who
responded to medical management (P = 0.01). Patients diagnosed with an infection in
hospital or up to 90 days before hospitalization had higher odds of undergoing emergent
surgery when admitted to hospital (adjusted OR=3.39; 95% CI: 1.02-11.23) (Table 2.2).
Table 1 summarizes patients’ characteristics of the 102 patients who underwent
an emergent colectomy and were preoperatively tested for C. difficile. The median time
from C. difficile diagnosis to surgery was 12 days (interquartile range 14 days). At least
one postoperative complication was recorded in 30% of UC patients and 20% developed
an infectious postoperative complication. Infectious postoperative complications
experienced among UC patients with C. difficile included: sepsis (n = 3), abscess (n = 2),
urinary tract infection (n = 1), pneumonia (n = 3) and infected central line (n = 1)
(Appendix 2.C). C. difficile was diagnosed preoperatively in 24% of patients who
experienced an infectious postoperative complication, compared with 7% of UC patients
who did not develop an infectious postoperative complication ( P= 0.046) (Table 2.1).
Preoperative diagnosis of C. difficile was not significantly associated with
developing any postoperative complication (adjusted OR=3.16; 95% CI: 0.89-11.23)
(Table 2.3). However, individuals diagnosed with C. difficile preoperatively had higher
32
odds of developing a new infectious postoperative complication (adjusted OR=4.76; 95%
CI: 1.10-20.63) (Table 2.3).
The sensitivity analysis that restricted the exposure definition to only patients
tested for C. difficile in hospital or 14 days before admission resulted in similar
associations between C. difficile infection and emergent colectomy (adjusted OR=3.70;
95% CI: 1.06-12.89) (Appendix 2.D), whereas the associations were strengthened for any
complication (adjusted OR=6.11; 95% CI: 1.32-28.21) and infectious complications
(adjusted OR=7.93; 95% CI: 1.54-40.75) (Appendix 2.E).
The sensitivity analysis that included patients not tested for C. difficile resulted in a
non-significant association between C. difficile infection and emergent colectomy
(OR=2.80; 95% CI: 0.86-9.11) (Appendix 2.F). However, individuals not tested for C.
difficile had higher odds of undergoing an emergent surgery when compared to those
with a negative C. difficile test result (OR=1.69; 95% CI: 1.09-2.63) (Appendix 2.F).
Individuals diagnosed with C. difficile preoperatively had higher odds of developing a
new infectious postoperative complication (OR=4.67; 95% CI: 1.17-18.47) (Appendix
2.G). Similar to the author’s primary analysis, a preoperative diagnosis of C. difficile was
not significantly associated with developing any postoperative complication (adjusted
OR=3.55; 95% CI: 0.95-13.21) (Appendix 2.G). Patients with an unknown C. difficile
status preoperatively were not at increased risk of developing any complication
(OR=1.56; 95% CI: 0.80-3.07) or a new infectious postoperative complication (OR=1.49;
95% CI: 0.67-3.29) (Appendix 2.G).
33
2.5 Discussion
UC patients diagnosed with C. difficile in hospital or 90 days before admission
were more likely to undergo an emergent colectomy after controlling for factors such as
age, disease extent, and corticosteroid use. Additionally, a preoperative diagnosis of C.
difficile increased the risk of developing an infectious postoperative complication.
Consequently, C. difficile infection is an important clinical outcome for UC patients
admitted to hospital with a flare.
UC patients have higher mortality and surgery risk at one and five years following
a C. difficile diagnosis when compared to UC patients without C. difficile.19,20 However,
reports describing the impact of C. difficile diagnosis on in-hospital and short-term risk of
colectomy are inconsistent. Two single-center studies21,22 failed to find an association
between C. difficile and the need of colectomy at index admission or three months
following C. difficile diagnosis. In addition, a large, nationwide study using in-hospital
data reported a negative association between C. difficile diagnosis and colectomy, even
after taking into consideration patients who were admitted electively.11 The conflicting
results between these previous studies and our study may be attributed to methodological
differences from our study. Previous studies were potentially limited because of selection
bias arising from data collected from tertiary care centers and/or misclassification bias
from using administrative databases.23 In contrast, our study was population-based, and
used chart reviews to confirm exposures and outcomes. Moreover, prior studies did not
account for C. difficile testing that occurred before admission to hospital. Furthermore,
surgery thresholds may differ between centers.23 Also, different C. difficile ribotypes8,25
and lack of consensus on the treatment of C. difficile infections among UC patients might
34
have influenced infection outcomes.26,27 Finally, over time, better surveillance and
diagnostic tests along with more aggressive treatment of C. difficile infections may
account for the differences observed.26-30
Several factors may explain the increased risk of postoperative infection among
patients with a C. difficile infection. First, patients with a C. difficile infection may have
greater UC disease severity, immunosuppression, or systemic toxicity.28 Second,
antibiotic exposure prior to surgery may have increased susceptibility for acquisition of
antibiotic-resistant nosocomial infections.29 Finally, C. difficile toxin worsens gut
permeability30,31 and promotes bacterial migration, which may have increased the risk of
septic complications.32 Our sensitivity analysis that restricted the study population to
individuals tested within 14 days of hospital admission or during hospital admission
suggested that the timing of C. difficile infection influenced postoperative morbidity.
We studied a large population-based cohort of UC patients.16 The diagnosis of UC
was confirmed in all cases, which improved the accuracy of our data. Administrative
databases misclassify the diagnosis of UC, colectomy, and postoperative complications
thereby influencing magnitude of risk estimates and the precision of CIs.16 Additionally,
we were able to determine the reason for admission (flare versus elective colectomy) and
only included individuals admitted as a flare in our analysis. Also, C. difficile test results
were obtained from a population-based laboratory database that records both in-hospital
and outpatient testing. Thus, we captured all tests performed on these patients during the
study period. Using a centralized laboratory database that captures all C. difficile testing
is essential because administrative coding of C. difficile includes a misclassification error
and could miss C. difficile diagnosis before admission. Finally, patients were tested for C.
35
difficile using a sequential testing approach, which optimized the positive predictive
value of detecting diseases with low prevalence.
Several limitations to the present study should be considered. First, we excluded
UC patients who were not tested for C. difficile in hospital or within 90 days of index
admission. Consequently, some UC patients may have been C. difficile positive but were
never detected. This gives rise to a possible differential misclassification bias where
patients with a more severe form of colitis were more likely to be tested for C. difficile,
reflecting a population with a more severe form of UC. Second, we were unable to report
C. difficile incidence rates among UC patients.33 Third, patient information was obtained
by reviewing patients’ charts, relying on various clinicians for their completeness and
accuracy. Due to the retrospective nature of the data, we were not able to control disease
severity by calculating a Mayo Score. Additionally, C-reactive protein levels were not
reliably measured in all patients during the early study years. Fourth, we were unable to
study other patient-related factors, such as antibiotic administration and response,
because they were not reliably recorded on the patients’ charts. Antibiotic treatment of C.
difficile (e.g., timing and first-line agent) may have influenced disease course. Fifth,
approximately 42% of the patients were not tested for C. difficile, which could introduce
a selection bias. Prior studies using administrative database have assumed that a negative
code for C. difficile is a negative test for C. difficile.11 However, our sensitivity analysis
demonstrated that individuals not tested for C. difficile were also at increased odds of
colectomy when compared with those who tested negative. Finally, the number of
patients with UC who tested positive for C. difficile was small, reducing the precision of
our findings. Furthermore, the small number of outcomes in the postoperative
36
complications analysis may affect the generalizability of our results. Consequently, our
findings should be independently replicated.
C. difficile diagnosis was associated with undergoing an emergent colectomy and
developing postoperative infectious complications. These findings have important
clinical implications. First, physicians should carefully assess for C. difficile infection
among all UC patients presenting with a flare of disease activity. Second, UC patients
with a C. difficile infection who undergo a colectomy should be monitored closely and
precautions should be taken to prevent infections. Third, increased surveillance for C.
difficile infections with early identification and aggressive treatment provides potential
avenues for improving outcomes among UC patients. Future studies are necessary to
assess interventions that may reduce the morbidity of C. difficile infections among UC
patients; and to determine whether timing of colectomy for UC patients with C. difficile
infection should be adjusted to minimize postoperative infectious complications. At
minimum, these patients require careful surveillance for infections postoperatively.
2.6 Acknowledgements
The authors acknowledge the DIMR department for providing data from the CHZ.
Dr. Kaplan is supported through a New Investigator Award from the Canadian Institute
of Health Research and a Population Health Investigator Award from Alberta Innovates Health Solutions.
37
2.7 Financial Disclosures
This project was funded by the Alberta Inflammatory Bowel Disease Consortium,
which is funded by an AHFMR Interdisciplinary Team Grant. AHFMR is now Alberta
Innovates - Health Solutions.
38
2.8 References
1.
Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of
the inflammatory bowel diseases with time, based on systematic review.
Gastroenterol 2012;142:46-54.
2.
Kaplan GG, Seow CH, Ghosh S, et al. Decreasing colectomy rates for ulcerative
colitis: A population-based time trend study. Am J Gastroenterol 2012;12:187987.
3.
Frolkis AD, Dykeman J, Negron ME, et al. Risk of surgery for inflammatory
bowel diseases has decreased over time: a systematic review and meta-analysis of
population-based studies. Gastroenterol 2013;145:996-1006.
4.
de Silva S, Ma C, Proulx MC, et al. Postoperative complications and mortality
following colectomy for ulcerative colitis. Clin Gastroenterol Hepatol
2011;11:972-80.
5.
Soon IS, Wrobel I, deBruyn JC, et al. Postoperative complications following
colectomy for ulcerative colitis in children. J Pediatr Gastroenterol Nutr
2012;54:763-8.
6.
Ricciardi R, Ogilvie JW, Roberts PL, Marcello PW, Concannon TW, Baxter NN.
Epidemiology of Clostridium difficile colitis in hospitalized patients with
inflammatory bowel diseases. Dis Colon Rectum 2009;52:40-5.
7.
Chandler RE, Hedberg K, Cieslak PR. Clostridium difficile associated disease in
Oregon: increasing incidence and hospital-level risk factors. Infect Control Hosp
Epidemiol 2007;28:116-122.
39
8.
Hubert B, Loo VG, Bourgault AM, et al. A portrait of the geografic dissemination
of the Clostridium difficile North American pulsed-field type 1 strain and the
epidemiology of C. difficile-associated disease in Quebec. Clin Infect Dis
2007;44:238-44.
9.
Ananthakrishnan AN. Clostridium difficile infection: epidemiology, risk factors
and management. Nat Rev Gastroenterol Hepatol 2011;8:17-26.
10.
Berg AM, Kelly CP, Farraye FA. Clostridium difficile infection in the
inflammatory bowel disease patient. Inflamm Bowel Dis 2013;19:194-204.
11.
Nguyen GC, Kaplan GG, Harris ML, Brant SR. A national survey of the
prevalence and impact of Clostridium difficile infection among hospitalized
inflammatory bowel disease patients. Am J Gastroenterol 2008;103:1443-50.
12.
Issa M, Vijayapal A, Graham MB, et al. Impact of Clostridium difficile on
inflammatory bowel disease. Clin Gastroenterol Hepatol 2007;5:345-51.
13.
Ananthakrishnan AN, McGinley EL, Saeian K, Binion DG. Temporal trends in
disease outcomes related to Clostridium difficile infection in patients with
inflammatory bowel disease. Inflamm Bowel Dis 2011;17:976-83.
14.
Ananthakrishnan AN, McGinley EL, Binion DG. Excess hospitalisation burden
associated with Clostridium difficile in patients with inflammatory bowel disease.
Gut 2008;57:205-10.
15.
Alberta Health Services. Alberta Health Services Annual Report, April 1, 2009March
31,
2010.
http://www.albertahealthservices.ca/Publications/ahs-pub-
annual-rpt.pdf. (Version current at November 1, 2012).
40
16.
Ma C, Crespin M, Proulx MC, et al. Postoperative complications following
colectomy for ulcerative colitis: A validation study. BMC Gastroenterol
2012;12:39.
17.
Dindo D, Demartines N, Clavien P-A. Classification of Surgical Complications.
Ann Surg 2004;240:205-13.
18.
Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the reporting of
observational studies in epidemiology (STROBE): explanation and elaboration.
PLoS Med 2007;4:e297.
19.
Murthy SK, Steinhart AH, Tinmouth J, et al. Impact of Clostridium difficile colitis
on 5-year health outcomes in patients with ulcerative colitis. Aliment Pharmacol
Ther 2012; 36:1032-9.
20.
Navaneethan U, Mukewar S, Venkatesh PG, et al. Clostridium difficile infection
is associated with worse long term outcome in patients with ulcerative colitis. J
Crohns Colitis 2012;6:330-6.
21.
Kariv R, Navaneethan U, Venkatesh PG, Lopez R, Shen B. Impact of Clostridium
difficile infection in patients with ulcerative colitis. J Crohns Colitis 2011;5:3440.
22.
Kaneko T, Matsuda R, Taguri M, et al. Clostridium difficile infection in patients
with ulcerative colitis: investigations of risk factors and efficacy of antibiotics for
steroid refractory patients. Clin Res Hepatol Gastroenterol 2011;35:315-20.
23.
Molodecky NA, Panaccione R, Ghosh S, Barkema HW, Kaplan GG. Challenges
associated with identifying the environmental determinants of the inflammatory
bowel diseases. Inflamm Bowel Dis 2011;17:1792-9.
41
24.
Kaplan GG, McCarthy EP, Ayanian JZ, Korzenik J, Hodin R, Sands BE. Impact
of hospital volume on postoperative morbidity and mortality following a
colectomy for ulcerative colitis. Gastroenterol 2008;134:680-7.
25.
Sundram F, Guyot A, Carboo I, Green S, Lilaonitkul M, Scourfield A.
Clostridium difficile ribotypes 027 and 106: clinical outcomes and risk factors. J
Hospl Infect 2009;72:111-8.
26.
Goodhand JR, Alazawi W, Rampton DS. Systematic review: Clostridium difficile
and inflammatory bowel disease. Aliment Pharmacol Ther 2011;33:428-41.
27.
Yanai H, Nguyen GC, Yun L, et al. Practice of gastroenterologists in treating
flaring inflammatory bowel disease patients with clostridium difficile: Antibiotics
alone
or
combined
antibiotics/immunomodulators?
Inflamm
Bowel
Dis
2011;17:1540-6.
28.
Kaplan GG, Hubbard J, Panaccione R, et al. Risk of comorbidities on
postoperative outcomes in patients with inflammatory bowel disease. Arch Surg
2011;146:959-64.
29.
Tacconelli E, De Angelis G, Cataldo MA, et al. Antibiotic usage and risk of
colonization and infection with antibiotic-resistant bacteria: a hospital populationbased study. Antimicrob Agents Chemother 2009;53:4264-9.
30.
Moore R, Pothoulakis C, LaMont JT, Carlson S, Madara JL. C. difficile toxin A
increases intestinal permeability and incduces Cl- secretion. Am J Physiol
Gastrointest Liver Physiol 1990;259:G165-G72.
42
31.
Hetch G, Charalabos P, LaMont JT, Madara JL. Clostridium difficile toxin A
perturbes cytoskeletal sturcture and tight junction permeability of cultured human
intestinal epithelial monolayers. J Clin Invest 1988;82:1516-24.
32.
F Feltis BA, Kim AS, Kinneberg KM, et al. Clostridium difficile toxins may
augment bacterial penetration of intestinal epithelium. Arch Surg 1999;134:123542.
33.
Schulz KF, Grimes DA. Case-control studies: research in reverse The Lancet
2002;359:431-4.
43
Table 2.1. Characteristics of ulcerative colitis patients admitted to hospital stratified by flare (medically responsive)
and emergent colectomy. Emergent colectomy patients were further stratified into development of any postoperative
complication and development of infectious complication
Characteristics
Gender, %(n)
Male
Female
All UC Patients (n = 278)
Emergent Colectomy
No
Yes
P-Value
(n = 176)
(n = 102)
52.3 (92)
47.7 (84)
62.8 (64)
37.2 (38)
Emergent Colectomy Patients (n = 102)
Any complication
Infectious complication
No
Yes
P-Value
No
Yes
P-Value
(n = 71)
(n = 31)
(n = 81)
(n = 21)
0.090
54.9 (39)
45.1 (32)
80.6 (25)
19.4 (6)
0.015
56.8 (46)
43.2 (35)
85.7 (18)
14.3 (3)
0.021
43.0
50.0
65.0
0.028
31.0
43.0
55.0
46.0
50.0
72.4
0.022
0.085
92.6 (75)
7.4 (6)
76.2 (16)
23.8 (5)
0.046
0.474
0
2
7
0
1
4
n=3
Age at admission,
y*
25th percentile
27.0
Median
36.0
75th percentile
47.0
31.0
47.0
58.0
< 0.001
30.0
40.0
55.0
C. difficile, %(n)
No
Yes
96.6 (170)
3.4 (6)
89.2 (91)
10.8 (11)
0.013
93.0 (66)
7.0 (5)
80.7 (25)
10.3 (6)
Disease duration,
y*
25th percentile
Median
75th percentile
Missing
0
1.0
6.0
n=5
0
2.0
6.0
n=3
0.401
0
2
7
0
1
4.5
n=3
44
0.549
Smoking, %(n)
Current
Ex-smoker
Never
Missing
10.8 (18)
28.9 (48)
60.3 (100)
n = 10
9.8 (10)
35.3 (36)
54.9 (56)
Flare duration,
%(n)
<2 wks
2-8 wks
>8 wks
Missing
18.3 (32)
49.1 (86)
32.6 (57)
n=1
25.5 (26)
47.1 (48)
27.4 (28)
n=1
Disease extent,
%(n)
Left-sided
Pancolitis
Missing
46.4 (71)
53.6 (82)
n = 23
17.7 (17)
82.3 (79)
n=6
0.550
11.3 (8)
33.8 (24)
54.9 (39)
6.4 (2)
38.7 (12)
54.8 (17)
0.332
25.4 (18)
50.7 (36)
23.9 (17)
25.8 (8)
38.7 (12)
35.5 (11)
< 0.001
15.4 (10)
84.6 (55)
22.3 (7)
77.4 (24)
n=6
51.6 (16)
48.4 (15)
Comorbidity, %(n)
No
68.8 (121)
Yes
31.2 (55)
60.8 (62)
39.2 (40)
0.191
64.8 (46)
35.2 (25)
Prednisone at
admission, %(n)
No
Yes
20.6 (21)
79.4 (81)
< 0.001
23.9 (17)
76.1 (54)
12.9 (4)
87.1 (27)
0.805
28.2 (20)
71.8 (51)
35.5 (11)
64.5 (20)
47.3 (84)
52.3 (92)
Five-ASA†, %(n)
No
31.8 (56)
Yes
68.2 (120)
30.4 (31)
69.6 (71)
45
0.814
9.9 (8)
33.3 (27)
56.8 (46)
9.5 (2)
42.9 (9)
47.6 (10)
0.721
0.426
25.9 (21)
49.4 (40)
24.7 (20)
23.8 (5)
38.1 (8)
38.1 (8)
0.456
17.3 (13)
82.7 (62)
n=6
19.1 (4)
80.9 (17)
1.0
0.210
64.2 (52)
35.8 (29)
47.6 (10)
52.4 (11)
0.166
0.205
23.5 (19)
76.5 (62)
9.5 (2)
90.5 (19)
0.229
0.460
28.4 (23)
71.6 (58)
28.1 (8)
69.9 (13)
0.389
0.403
Azathioprine†,
%(n)
No
Yes
67.0 (118)
33.0 (58)
69.6 (71)
30.4 (31)
Infliximab†, % (n)
No
81.3 (143)
Yes
18.7 (33)
86.3 (88)
13.7 (14)
IV-steroids in
hospital, % (n)
No
Yes
1.0 (1)
99.0(101)
12.5 (22)
87.5 (154)
0.659
70.4 (50)
29.6 (21)
67.7 (21)
32.6 (10)
0.281
88.7 (63)
11.3 (8)
80.7 (25)
19.3 (6)
< 0.001
0 (0)
100 (71)
3.2 (1)
96.7 (30)
* y- year; † medication taken at admission or in-hospital
46
0.787
70.4 (57)
29.6 (24)
66.7 (14)
33.3 (7)
0.742
0.275
86.4 (70)
13.6 (11)
85.7 (18)
14.3 (3)
1.0
0.304
1.2 (1)
98.8 (80)
0 (0)
100 (21)
1.0
Table 2.2. Logistic regression results of emergent colectomy with C. difficile
diagnosis as primary exposure.
All UC Patients
(n = 278)
Adjusted analysis
OR
95% CI
C. difficile diagnosis
No
Yes
1.00
3.39
Referent
(1.02-11.23)
Age
18-32
33-47
≥ 48
1.00
1.21
5.05
Referent
(0.06-2.44)
(2.44-10.47)
Disease distribution
Left-side/Undetermined
Pancolitis
1.00
4.41
Referent
(2.33-8.33)
Prednisone at admission
No
Yes
1.00
2.75
Referent
(1.45-5.20)
IV-steroids in hospital
No
Yes
1.00
15.61
Referent
(1.77-137.18)
47
Table 2.3. Logistic regression results for emergent colectomy patients having any
post-operative complication and infections post-operative complication with C.
difficile diagnosis as primary exposure.
Emergent Colectomy Patients
(n = 102)
Any complication
Infectious complication
Adjusted analysis
Adjusted analysis
OR
95% CI
OR
95% CI
C. difficile diagnosis
No
Yes
1.00
3.70
Referent
(0.89-15.3)
1.00
4.76
Referent
(1.10-20.63)
Age
18-32
33-47
≥ 48
1.00
1.35
2.77
Referent
(0.24-5.30)
(0.77-9.96)
1.00
1.56
3.43
Referent
(0.31-7.93)
(0.8-14.68)
Gender
Male
Female
1.00
0.28
Referent
(0.09-0.82)
1.00
0.23
Referent
(0.6-0.88)
Comorbidity
No
Yes
1.00
1.52
Referent
(0.53-4.35)
NS*
*Not significant (NS): Was not significant in final model.
48
Figure 2.1. Flow diagram illustrating the inclusion and exclusion criteria for
identifying patients admitted with a flare for ulcerative colitis and tested Clostridium
difficile infection in hospital and up to 90 days prior to admission.
49
2.9 Appendix 2.A Post-operative complications classification
Category
Gastrointestinal
Complications
Small bowel obstruction, pouch leak or pouch failure, bowel
perforation, ileus, ischemic bowel and gastrointestinal bleeding
Wound
Fistula, hematoma or seroma, wound dehiscence and delayed wound
healing and iatrogenic injuries including foreign body accidentally left
during procedure
Infectious
Sepsis and bacteremia, abscess, wound infection, urinary tract
infection, pneumonia and empyema.
Renal and
Endocrine
Acute renal failure, fluid and electrolyte disorders (e.g. hypokalemia)
and adrenal disorders
Cardiovascular
Thrombosis or embolism, myocardial infarction, cardiac arrest,
hypotension or shock, cardiac arrhythmias and congestive heart failure
Pulmonary
Acute respiratory failure, hypoxemia, pleural effusion and pulmonary
edema, pneumothorax and atelectasis, asthma and COPD exacerbation
Neurological
Neurological disease, cerebrovascular disease, psychoses, delirium,
seizures and neuropathies
50
2.10 Appendix 2.B Comorbidity classifications
Category
Coronary Artery Disease
Cancer
Comorbidity
Coronary artery disease, ischemic heart disease,
myocardial infarction, peripheral vascular disease
Lymphoma, metastatic tumour, solid tumour without
metastases
Other cardiovascular
Cardiac arrhythmia, valvular disorder
CHF
Congestive heart failure
Diabetes
Diabetes with complications, diabetes without
complications
Venous
Thromboembolism
GI
Deep vein thrombosis
CMV infection, pancreatitis, peptic ulcer disease
HTN
Blood loss anaemia, coagulopathy, cyclical
neutropenia, deficiency anaemia
Hypertension
Liver
Fatty liver, primary sclerosing cholangitis, liver disease
Neurological
Cerebrovascular disease, hemiplegia and paraplegia
Pulmonary
Renal
Asthma, COPD, sarcoidosis
Renal failure (acute or chronic)
Rheumatoid
Ankylosing spondylitis, episcleritis, uveitis and iritis,
Gout, sacroiliitis, rheumatoid arthritis
Haematological
51
2.11 Appendix 2.C Postoperative complications observed among patients tested for
C. difficile.
Complications categories*
Gastrointestinal
No complications
At least one complication
Pouch leak
Bowel perforation
Ileus
Ischemic bowel
GI bleed
C. difficile-negative
C. difficile-positive
82
9
2
1
4
0
3
8
3
0
0
0
1
2
Wound
No complications
At least one complication
Hematoma/seroma
Dehiscence
87
10
0
2
10
1
1
1
Infectious
No complications
At least one complication
Sepsis
Abscess
Wound infection
Urinary tract infection
Pneumonia
Infected central line
Perianal infection
75
16
7
6
3
2
4
0
1
6
5
3
2
0
1
3
1
0
Renal and Endocrine
No complications
At least one complication
Acute renal failure
Fluid/electrolyte disorders
Adrenal
87
4
1
2
1
9
2
2
1
0
52
Cardiovascular
No complications
At least one complication
Thrombosis
Myocardial infarction
Cardiac arrest
Hypotension/shock
Cardiac arrhythmia
83
8
5
3
1
3
5
8
3
2
1
0
1
0
Pulmonary
No complications
At least one complication
Acute respiratory failure
Hypoxia
Pleural effusion/Pulmonary edema
Pneumothorax and atelectasis
84
9
4
2
4
1
7
2
2
1
1
0
91
0
11
0
Neurological
No complications
At least one complication
* Patients had ≥ 1 postoperative complication
53
2.12 Appendix 2.D Sensitivity analysis: Logistic regression results of emergent
colectomy with C. difficile diagnosis in hospital or 14 days prior to admission as
primary exposure.
All UC Patients
(n = 278)
Adjusted analysis
OR
95% CI
C. difficile diagnosis
No
Yes
1.00
3.70
Referent
(1.06-12.89)
Age
18-32
33-47
≥ 48
1.00
1.14
5.06
Referent
(0.56-2.34)
(2.39-10.74)
Disease distribution
Left-sided/undetermined
Pancolitis
1.00
5.04
Referent
(2.39-10.74)
Prednisone at admission
No
Yes
1.00
2.54
Referent
(1.32-4.85)
IV-steroids in hospital
No
Yes
1.00
11.51
Referent
(1.36-97.46)
54
2.13 Appendix 2.E Sensitivity analyses: Logistic regression results of any postoperative complication and infectious post-operative complication with C. difficile
diagnosis in hospital or 14 days prior to admission as primary exposure.
Emergent Colectomy Patients
(n = 102)
Any complication
Infectious complication
Adjusted analysis
Adjusted analysis
OR
95% CI
OR
95% CI
C. difficile diagnosis
No
Yes
1.00
6.11
Referent
(1.32-28.21)
1.00
7.93
Referent
(1.54-40.75)
Age
18-32
33-47
≥ 48
1.00
1.86
3.61
Referent
(0.42-8.29)
(0.88-14.96)
1.00
2.98
5.18
Referent
(0.43-20.60)
(0.8-33.77)
Gender
Male
Female
1.00
0.29
Referent
(0.1-0.89)
1.00
0.21
Referent
(0.57-0.86)
Comorbidity
No
Yes
1.00
1.75
Referent
(0.58-5.26)
1.00
2.05
Referent
(0.57-7.45)
55
2.14 Appendix 2.F Sensitivity analysis: Logistic regression results of emergent
colectomy including those not tested for C. difficile.
All UC Patients
(n = 481)
Adjusted analysis
OR
C. difficile diagnosis
No
Yes
Not tested
1.00
2.80
1.69
95% CI
Referent
(0.86-9.11)
(1.09-2.63)
Age
18-32
33-47
≥ 48
1.00
1.27
3.52
Referent
(0.75-2.15)
(2.05-6.04)
Disease distribution
Left-sided/Undetermined
Pancolitis
1.00
4.51
Referent
(2.83-6.04)
Prednisone at admission
No
Yes
1.00
3.11
Referent
(1.95-4.99)
IV-steroids in hospital
No
Yes
1.00
6.21
Referent
(2.33-16.53)
56
2.15 Appendix 2.G Sensitivity analyses: Logistic regression results of any postoperative complication and infectious post-operative complication including those
not tested for C. difficile.
Emergent Colectomy Patients
(n = 186)
Any complication
Infectious complication
Adjusted analysis
Adjusted analysis
OR
95% CI
OR
95% CI
C. difficile diagnosis
No
Yes
Not tested
1.00
3.55
1.56
Referent
(0.95-13.21)
(0.80-3.07)
1.00
4.67
1.49
Referent
(1.17-18.47)
(0.67-3.29)
Age
18-32
33-47
≥ 48
1.00
1.16
2.81
Referent
(0.47-2.89)
(1.24-6.41)
1.00
1.03
3.61
Referent
(0.32-3.37)
(2.04-9.81)
57
Chapter Three: Clostridium difficile infection diagnosed early in ulcerative colitis
patients impacts disease related outcomes: A population-based inception cohort
study
58
3.1 Abstract
Ulcerative colitis (UC) patients diagnosed with Clostridium difficile infection
have a higher overall risk of colectomy. Still, previous research has been performed on
prevalent UC cases rather than UC incident cases. The objectives of this study were to: 1)
validate the ICD-10 code for C. difficile diagnosis among UC patients; 2) determine the
1, 3, and 5-year cumulative risk of a C. difficile diagnosis after their UC diagnosis; 3)
determine the effect of a C. difficile diagnosis on the risk of colectomy and mortality; and
4) determine the association between a C. difficile diagnosis and developing
postoperative complications in a population-based inception cohort of patients with UC.
ICD-10 codes for C. difficile were compared to toxin tests from Calgary Laboratory
Services and sensitivity, specificity, positive predictive value (PPV) and negative
predictive value (NPV) with 95% CI were calculated. The 1, 3 and 5-year cumulative risk
of being diagnosed with C. difficile infection following UC diagnosis date was calculated
with a life table. Next, the effect of a C. difficile diagnosis on colectomy for UC and
mortality was modelled using an adjusted competing risk survival regression model and a
Cox proportional hazard model, respectively. Finally, the effect of a C. difficile diagnosis
on postoperative complications was assessed using a mixed effects logistic regression
model. The sensitivity, specificity, PPV and NPV of the ICD-10 diagnostic code of C.
difficile diagnosis were 82.1% (95% CI: 71.7-89.9%), 99.4% (95% CI: 99.1-99.7%),
88.4% (95% CI: 82.9-92.3%) and 99.1% (95% CI: 98.5-99.4%), respectively. The
cumulative risk of C. difficile diagnosis at 1, 3, and 5 years after UC diagnosis was 1.6%
(95% CI: 1.1-2.3), 2.5% (95% CI: 1.9-3.4), and 3.2% (95% CI: 2.2-4.2), respectively.
Patients with a C. difficile diagnosis were more likely to have colectomy compared to
59
those who had no prior C. difficile diagnosis (SHR=2.36; (95% CI: 1.47-3.80) after
adjusting for age, sex, and comorbidities. The multilevel logistic regression model shows
that C. difficile diagnosis in hospital ≤ 90 days prior to colectomy admission was
associated with developing at least one postoperative complication (adjusted OR=4.84;
95% CI: 1.28-18.35). C. difficile diagnosis worsened the prognosis of UC by increasing
the risk of colectomy and postoperative infectious complications following colectomy.
Future studies are needed to explore whether early detection and aggressive management
of C. difficile infection will improve UC outcomes.
60
3.2 Introduction
Ulcerative colitis (UC) is an inflammatory bowel disease that results in chronic
inflammation of the large intestine. The clinical course of UC is highly variable ranging
from mild disease severity treated effectively with mesalamine to an aggressive disease
course that requires treatment with immunosuppressants and/or biologics. While
advances in medical management have led to decreased rates of colectomy in patients
with UC, 16% of patients with UC require a colectomy within 10 years of their
diagnosis.1 Colectomy for UC is associated with significant postoperative mortality
(3.2%) and morbidity (33.3%).2,3 Thus, identifying modifiable risk factors that influence
disease course is a clinical priority.
Over the last 15 years, special attention has been given to the effect of
Clostridium difficile infections on the clinical course of UC. Patients with UC have a high
risk of acquiring C. difficile when compared to Crohn’s patients or the general
population.4 Specifically, Nguyen et al. demonstrated that the prevalence rate of C.
difficile among UC patients was approximately 8 and 3 times greater when compared to
the non-IBD and CD patients, respectively.5 In addition, among UC patients C. difficile
infections are associated with an increased risk of colectomy after C. difficile diagnosis.512
As demonstrated in Chapter 2, C. difficile infection prior to colectomy among UC
patients admitted to hospital for a flare increased the risk of postoperative complications;
however, this study was limited by small sample size.13
Most of the studies that assessed the risk of developing C. difficile infection and
its effect on colectomy among UC patients used prevalent cohorts admitted to hospital.
61
These prevalent cohorts are mainly patients followed by gastroenterologists, most likely
with severe form of UC.14 Thus, patients with an indolent course of UC may be missed.
Further, many of these studies used administrative databases without validating the code
to identify C. difficile infection.5,6,12,15,16 Thus, we cannot estimate the impact of
misclassifying patients based on C. difficile exposure status.
The objectives of this study were to: 1) validate the International Classification of
Diseases-10 (ICD-10) diagnostic code for C. difficile diagnosis among UC patients; 2)
determine the 1, 3, and 5-year cumulative risk of a C. difficile diagnosis after their UC
diagnosis; 3) determine the effect of a C. difficile diagnosis on the risk of colectomy and
mortality; and 4) to determine the association between a C. difficile diagnosis and
developing postoperative complications using a population-based inception cohort of UC
patients.
3.3 Materials and Methods
3.3.1 Study population
3.3.1.1 Validation cohort
The study population for the validation of the ICD-10 code for C. difficile
consisted of all adult UC patients (≥ 18 years of age) who were admitted to Calgary
Health Zone (CHZ) hospitals with a diagnosis of UC between 2002 and 2009. The CHZ
in Alberta, Canada, is a population-based health authority providing health care to
residents in Calgary and over 20 nearby cities under a public single-payer system.17
62
3.3.1.2 Inception cohort of UC patients
The study population consisted of all adults (≥ 18 years of age) diagnosed with
UC and living in Alberta, Canada, between 2003 and 2010 while registered under the
Alberta Health Care Insurance Plan (AHCIP). The AHCIP is publicly funded and
provides hospital and health care services to >99% of Alberta’s population (~3 million
residents). Every Alberta resident registered under the AHCIP has a unique personal
health identification number, which can be used to track the patient through time and to
link the patient to different health data sources in Alberta.18
3.3.2 Data sources
3.3.2.1 Validation of the ICD-10 code for C. difficile diagnosis among UC patients
The Data Integration, Measurement and Reporting (DIMR) Hospital Discharge
Abstract Database is a population-based database that includes patient’s demographical
data, admission and discharge date, 42 diagnostic coding fields using the International
Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-10CM), and 25 procedural codes using the Canadian Classification of Health Intervention
(CCI). The DIMR was used to capture all adults (≥ 18 years) admitted to hospital with a
diagnosis of UC (ICD-10-CA K51.X) from 2002 to 2009. Charts were reviewed on all of
these patients to confirm the diagnosis of UC and C. difficile infection. The ICD-10 codes
for UC hospitalization and for colectomy were previously validated.19
Calgary Laboratory Services (CLS) is a medical diagnostic laboratory that
provides services to the Calgary region and Southern Alberta. CLS is responsible for the
majority of laboratory tests (microbiology, anatomic pathology and cytopathology)
performed in hospital settings in Calgary. Calgary Laboratory Services provided
63
information on all C. difficile test results (date, type of test, result) performed on all UC
patients admitted to hospitals in the CHZ. A patient was considered C. difficile-positive if
at least one test result was positive. All stool toxin test for C. difficile captured by CLS
were linked by a unique identifying variable to patients with UC who were identified in
the DIMR Hospital Discharge Abstract Database.
3.3.2.2 Inception cohort of patients with UC
The Alberta-wide UC inception cohort was derived from four linked data sources
from AHCIP: 1) the Registry Database, contains participants’ demographic and
geographic information for each year; 2) the Alberta Physicians’ Claims (PC) Database,
contains outpatient claims for all people seen by a practitioner in Alberta. Information
consists of demographic data, service date, 3 diagnostic codes (ICD-9) and one procedure
code (CCI); 3) the Alberta Inpatient Hospital Discharge Abstract Database (DAD)
contains demographic information, fiscal year, service dates, 25 diagnostic codes (ICD-9:
1995 to 2001; ICD-10-CA: 2002 to present) and 20 procedure codes (CCI); and 4) the
Ambulatory Care Classification System (ACCS), contains information on facility-based
ambulatory care ranging from emergency visits to outpatient procedures (e.g.
colonoscopy). Data recorded in ACCS consists of demographic data, fiscal year, service
dates, 10 diagnostic codes (ICD-9: 1998 to 2001; ICD-10-CA: 2002-present), and 10
procedure codes (CCI). Information from all databases was available from April 1, 1995
to March 31, 2010 with the exception of ACCS that was available from April 1, 1997 to
March 31, 2010. Information provided to investigators is de-identifiable.
64
3.3.3 Development of the UC inception cohort
We used the four linked data sources from AHCIP to identify all IBD patients
living in Alberta using a previously validated IBD case definition.20 This validated IBD
algorithm involves the identification of diagnostic codes for IBD (i.e. ICD-9: 555; 556;
ICD-10: K50; K51) in the DAD database (≥ 2 hospitalizations within a 2-year period), or
ACCS database (≥ 2 ambulatory services contacts within a 2-year period), or PC database
(≥ 4 physician contacts within a 2-year period). Date of the first contact with an IBD code
was recorded on those patients who fulfilled the case definition. Next, a previously
validated IBD scoring system was used to distinguish UC from Crohn’s disease (CD).20
A +1 score was assigned to any related admission/visit with an ICD-9/10 code for UC
and a -1 score for any related admission/visit with an ICD-9/10 code for CD.20 A
cumulative score was calculated and those with a score ≥ 2 were considered UC patients
and those with a score ≤ -2 were considered CD patients. Patients with a score between -2
and 2 were classified as IBD unknown. Patients with CD and IBD unknown were
excluded from the UC inception cohort.
Validation studies have shown that an 8-year washout period is necessary to
differentiate prevalent from incident cases.20 Thus, a UC patient was considered to be an
incident case only if the patient was an Alberta resident for at least 8 years prior to the
date of their first UC contact. Patients who were < 18 years of age at UC diagnosis were
excluded. In order to minimize misclassification errors patients who had a diagnosis of C.
difficile prior to the diagnosis of UC were excluded (n = 32) (Figure 3.1). Though,
patients who underwent a colectomy prior to their diagnosis of UC (n = 24) were
65
excluded. Patients that had a C. difficile diagnosis and/or colectomy 14 days prior to their
diagnosis date were included in the study.
3.3.4 Outcome and predictor variables
The primary outcome was having a colectomy. A patient was defined as a
colectomy case if a colectomy procedure code (CCI: 1.NM.87, 1.NM.89, 1.NM.91,
1.NQ.89, 1.NQ.90) was identified in the DAD. The first colectomy was recorded in
situations where more than one admission had a colectomy procedure code. Patients that
were identified as having a colectomy up to 14 days prior to their diagnosis date were
included in the analysis as some of these patients were diagnosed with UC based on
histopathology of the resected colon.
Secondary outcomes included mortality and the development of in hospital
postoperative complications. A patient was recorded as having a postoperative
complication if any of the pre-defined ICD-10 codes for postoperative complications
occurred within the same admission as the colectomy procedure (Appendix 3.A).2,12,21,22
These postoperative complications were further classified into seven categories including
infectious complications (Appendix 3.A).2,12,21,22 Postoperative complications codes
following colectomy for UC have been previously validated. A diagnosis of C. difficile
within the same colectomy admission was not considered to be an infectious
complication.
The primary exposure was the diagnosis of C. difficile in either the DAD or
ACCS database. The Physician Claims database cannot identify C. difficile because the
diagnostic code is limited to 3 characters and the ICD-9 code for C. difficile (008.45).
The date of the first positive test was recorded as the index date of C. difficile infection.
66
Additional demographic and clinical data was extracted from the Registry, PC, DAD and
ACCS datasets and included: age, sex, death, disease duration (defined as the interval
between UC diagnosis and colectomy or death), type of surgery procedure (defined as
open or laparoscopic approach), type of admission on colectomy admission (defined as
elective or emergent) and a de-identifiable hospital identification number. The Charlson
comorbidity index was used to identify patients’ comorbidities (i.e. health conditions
occurring before hospitalization).23 The diagnostics codes used for differentiating
between types of surgical procedures and the Charlson comorbidities are in Appendices
3.B and 3.C.
3.3.5 Analyses
All analyses was done using STATA statistical software version 11 (STATA
Corp, College Station, TX, USA). The study was approved by the Conjoint Health
Research Ethics Board of the University of Calgary
3.3.5.1 Validation of the ICD-10 code for C. difficile diagnosis among UC patients
Sensitivity, specificity, positive predictive value (PPV) and negative predictive
value (NPV) with 95% CI were calculated for the ICD-10 code for C. difficile during
admission to hospital. This was done by comparing the presence of an ICD-10 code for
C. difficile during hospital admission to the CLS provided stool toxin test result for C.
difficile performed during hospital admission and 7 days prior to hospital admission (gold
standard). A dichotomous variable was created for the presence of an ICD-10 code for C.
difficile for the hospital discharge abstract database.
67
3.3.5.2 Effect of C. difficile on UC incident cases
A life table was constructed to calculate the 1-, 3-, and 5-year cumulative risk of
being diagnosed with their first C. difficile infection following UC diagnosis. For the
primary outcome, we modelled the cumulative incidence of colectomy in the presence of
competing mortality using competing risk regression. The primary exposure was being
diagnosed with C. difficile any time between diagnosis of UC and colectomy. The
following variables were included a priori in to the model: sex, age at diagnosis of UC,
and comorbidities (0 = no, 1 = one comorbidity, 2 = ≥ 2 comorbidities). Patients were
censored if they moved out of Alberta, Canada. Interactions between C. difficile and the
presence of comorbidities, sex and age (continuous) were explored and included in the
model if the likelihood ratio test was statistically significant (P < 0.05). Statistical
significance was defined as a two-sided P-value < 0.05. Point estimates were presented as
sub hazard ratio (SHR) with 95% confidence interval.
A secondary analysis using a Cox proportional hazard model was used to assess
the effect of a C. difficile infection on the overall mortality. The following variables were
included a priori in to the model: sex, age at diagnosis of UC, and comorbidities (0 = no,
1 = one comorbidity, 2 = ≥ 2 comorbidities). Patients were censored if they moved out of
Alberta, Canada. Interactions between C. difficile and the presence of comorbidities, sex
and age (continuous) were explored and included in the model if they were tested and
included in the model if the likelihood ratio test was statistically significant (P < 0.05).
Statistical Significance was defined as a two-sided P-value < 0.05. Point estimates were
presented as a hazard ratio (HR) with 95% confidence interval.
68
The association between a C. difficile diagnoses on postoperative complications
was evaluated in a subset of the UC population who underwent colectomy. For this
analysis, C. difficile was stratified into 3 categories: no C. difficile diagnosis prior to
colectomy admission, C. difficile diagnosis captured > 90 d prior to colectomy admission
and C. difficile diagnosis < 90 d prior to colectomy admission. A mixed effects logistic
regression model was used to evaluate the risk of C. difficile on postoperative
complication including hospital as a random effect (to account for clustering within
hospital). The following covariates were a priori forced in the model because they have
been previously shown to influence the risk of postoperative complications following
colectomy for UC: age, sex, comorbidities, type of colectomy admission (i.e. emergent
versus elective) and type of surgery procedure (i.e. open versus laparoscopic).2
Interactions between C. difficile and variables independently associated with colectomy
were tested and included in the model if the likelihood ratio test was statistically
significant (P < 0.05). Point estimates were presented as adjusted odds ratios (OR) with
95% confidence interval (CI).
A sensitivity analyses was performed to determine whether C. difficile had an
effect on developing a postoperative infectious complication using the same procedure as
described above.
3.4 Results
3.4.1 Validation of the ICD-10 code for C. difficile diagnosis among UC patients
Between 2002 and 2009, 1,686 UC patients with 3,603 admissions to CHZ
hospitals were identified. A C. difficile test was performed in 37.0% (n = 1,333) of the
69
admissions. Among these, a C. difficile positive test result was identified in 2.3% (n = 84)
of the admissions. An ICD-10 code for C. difficile diagnosis was recorded in 2.2% (n =
78) of the admissions. Sensitivity, specificity, PPV and NPV the ICD-10 code for C.
difficile diagnosis was 82.1% (95% CI: 71.7-89.9%), 99.4% (95% CI: 99.1-99.7%),
88.4% (95% CI: 82.9-92.3%) and 99.1% (95% CI: 98.5-99.4%), respectively.
3.4.2 Effect of C. difficile on UC incident cases
Between April 1, 1994 and March 31, 2010, 8,011 people with a UC diagnosis
received medical services in Alberta (Figure 3.1). From these, between April 1, 2003 and
March 31, 2010, 1,754 patients met our inclusion criteria of an incident case.
Table 1 shows the characteristics of the UC incident cohort stratified by whether
they had a colectomy or not. The median time to follow-up for the inception cohort was
3.8 years. A C. difficile diagnosis was recorded in 4.6% of the patients. The cumulative
risk of the C. difficile diagnosis at 1-, 3-, and 5-years after UC diagnosis was 1.8% (95%
CI: 1.2-2.5%), 2.8% (95% CI: 2.0-3.7%), and 3.4% (95% CI: 2.5-4.6%), respectively.
The 1-, 3-, and 5-year cumulative colectomy risk on the entire inception cohort was 5.7%
(95% CI: 4.6-6.9%), 12.0% (95% CI: 10.4-13.8%), and 14.0% (95% CI: 12.3-16.1%),
respectively. The 1-, 3- and 5-year cumulative colectomy risk among those with no prior
C. difficile diagnosis was 5.0% (95% CI: 4.1-6.2%), 11.3% (95% CI: 9.7-13.1%), and
13.3% (95% CI: 12.3-17.2%), respectively. The 1-, 3- and 5-year cumulative colectomy
risk among UC patients with a C. difficile infection was 18.7% (95% CI: 11.5-29.6%),
27.2% (95% CI: 18.1-39.5%), and 29.3% (95% CI: 19.7-42.1%), respectively. The results
of the competing risk regression demonstrate that C. difficile increases the incidence of
colectomy or death (Figure 3.2). Patients with a C. difficile diagnosis were more likely to
70
have colectomy compared to those who had no prior C. difficile diagnosis (SHR=2.36;
(95% CI: 1.47-3.80) after adjusting for age, sex, and comorbidities (Table 3.2 & Figure
3.2). We further explored the timing of C. difficile and colectomy among these patients
and found that those who had a C. difficile diagnosis ≤ 90 days prior to colectomy had an
earlier disease course (median time: 32 days, 25th percentile: 2.5 days, 75th percentile:
180 days) compared to those who had a C. difficile diagnosis > 90 prior to (median time:
441 days, 25th percentile: 273.5 days, 75th percentile: 1004 days) and those without a C.
difficile diagnosis (median time: 287 days, 25th percentile: 25 days, 75th percentile: 667
days).
Overall 5-year mortality risk was higher among patients diagnosed with C. difficle
infection (5-year mortality= 17.5%; 95% CI: 9.2-31.7) compared to those with no C.
difficile diagnosis (5-year mortality=3.3%; 95% CI: 2.4-4.5). The results of the Cox
proportional hazard model shows that a C. difficile diagnosis increases the hazard of
dying by 2.56 times (95% CI: 1.28-5.10) after adjusting for age, sex, and comorbidities
(Table 3.2).
Table 3.3 shows patients characteristics among those who had a colectomy
stratified by having any postoperative complication. At least one postoperative
complication was recorded in 41% of UC patients (see Appendix 3.D for further
information on the types of postoperative complications observed). The multilevel
logistic regression model shows that C. difficile diagnosis in hospital ≤ 90 days prior to
colectomy admission was associated with developing at least one postoperative
complication (adjusted OR=4.84; 95% CI: 1.28-18.35) (Table 3.4).
71
The sensitivity analysis shows that a diagnosis of C. difficile in hospital ≤ 90 days
prior to colectomy admission was not associated with developing an infectious
postoperative complication (adjusted OR=1.53; 95% CI: 0.46-5.09) (Appendix 3.E and
3.F).
3.5 Discussion
This is the first study confirming the validity of ICD-10 code for C. difficile
among UC patients. Using this validated code, we showed that within 5 years of being
diagnosed with UC 3.2% of UC patients were diagnosed with C. difficile infection.
Patients with UC who were diagnosed with C. difficile were significantly more likely to
undergo a colectomy or die. Further, C. difficile infection reduced the interval from
diagnosis to colectomy when compared to UC patients without a C. difficile diagnosis.
Finally, patients with UC who were diagnosed with a C. difficile infection were at
increased risk of developing a postoperative complication. Collectively, these data
highlight that a diagnosis of C. difficile has a major impact on the course of UC.
Our population-based UC inception cohort demonstrated that within 5 years of
diagnosis 3.4% of UC patients are diagnosed with C. difficile infection and nearly 1 in 3
of these patients undergo colectomy within 5 years of their diagnosis of C. difficile
infection. These data suggest that either C. difficile infection directly worsens the
prognosis of UC or that patients with an aggressive severe phenotype of UC are more
likely to be diagnosed with C. difficile infection. While we controlled for comorbidities
and disease duration, our study could not account for disease severity or extent, and we
did not control for different C. difficile ribotypes that may have influenced the
72
relationship between C. difficile infection and UC prognosis. Further, we adjusted for
clustering within hospitals, but could not directly account for variation in treatment for C.
difficile infection between hospitals and practitioners.11,24-26 Future prospective studies
should be designed to identify a risk-profile at diagnosis of UC that predicts acquisition
of C. difficile infection after adjusting for disease severity.
The increased risk of acquiring C. difficile among UC patients has been
consistently shown in epidemiological studies.11 Further, our study and others,
demonstrated that UC patients with a C. difficile diagnosis had an increased risk of
colectomy, postoperative complications and mortality.5-10,12,13,15,16,27,28 The reason why C.
difficile is more predominant in UC is likely multifactorial. First, the altered gut
microbiota, observed among UC patients provides an optimal environment for C. difficile
to proliferate.29 Second, patient related factors such as the ability to develop an adequate
innate and adaptive immune response have been shown to influence the sequelae
following C. difficile colonization. For example, the innate immune response, which
minimizes mucosal damage and inflammation, is dependent on commensal bacteria.29
Thus, UC patients might have increased mucosal damage as the result the observed gut
dysbiosis characteristic of UC. Also, the lack of an adequate adaptive immune response
following C. difficile colonization has been associated with developing C. difficileassociated diarrhoea among the non-IBD population.30 In UC patients, genetic
polymorphisms, which are involved in the regulation of innate or adaptive immunity,
have been found to be associated with an increased risk of acquiring C. difficile
infection.31 While these genetic polymorphisms were not associated with a more
aggressive phenotype of UC,31 the presence of multiple risk loci was associated with
73
developing C. difficile infection early in the UC disease course.31 This could explain our
findings that patients who had a colectomy within 90 days of a C. difficile diagnosis were
earlier in their UC disease course.
Our data demonstrated that a C. difficile diagnosis prior to colectomy increased
the risk of postoperative complications, but not specifically postoperative infection. This
finding was in contrast to a recent study that demonstrated a diagnosis of C. difficile
infection
was
associated
with
the
development
of
infectious
postoperative
complications.13 The previous study confirmed postoperative complications through
medical chart review, whereas the current study relied on the accuracy of administrative
codes. A prior validation study demonstrated that the sensitivity of identifying any major
postoperative complication in administrative data was 85%, but dropped to 66% for
postoperative infectious complications.19 The lower sensitivity likely led to a higher
number of unidentified patients with an infectious postoperative complication, which
decreased the power and precision of our estimates.
We studied a large population-based incident cohort of UC patients using
administrative data. However, there are several limitations that should be considered with
the use of administrative databases.14 First, misclassification of UC and C. difficile
diagnosis and the development of postoperative complications might have influenced the
magnitude of the risk estimates and the precision of the confidence intervals. However,
the IBD case definition and the UC scoring were validated within the data obtained from
AHCIP with a sensitivity and specificity of 83.2% (95% CI: 82.1-84.3) and 86.3% (+/SD: 1.9), respectively. Additionally, we validated the diagnostic validity of the ICD-10
code for C. difficile among UC patients using a subset of UC patients living in Alberta,
74
Canada. However, during the C. difficile validation phase we found that only one third of
the patients had a test done for C. difficile. This could give rise to a possible differential
misclassification bias where patients with a severe form of UC were more likely tested
for C. difficile, inflating the risk estimate. On the other hand, assuming that patients with
no C. difficile code were in fact negative may have shifted the risk estimates towards the
null. Additionally, we were unable to control for disease severity and other markers for
C. difficile outcomes, disease extent and the use of IBD medication and antibiotics. Thus,
our findings should be independently replicated.
These findings have important clinical implications. First, physicians should
carefully assess for C. difficile infection among all UC patients. However, special
attention should be given to those presenting with C. difficile infection early on the UC
disease course and carefully monitor them as they are at increased risk of colectomy and
postoperative complications. Future studies are necessary to: 1) determine the impact of
host immunity on C. difficile outcomes among UC patients and 2) clinical trials on which
treatment course improves the outcomes among those with C. difficile infections.
75
3.6 References
1.
Frolkis AD, Dykeman J, Negron ME, et al. Risk of surgery for inflammatory bowel
diseases has decreased over time: a systematic review and meta-analysis of
population-based studies. Gastroenterology 2013;145:996-1006.
2.
Frolkis AD, Kaplan GG, Patel A, et al. Short-term emergent readmission and
postoperative complications in children and adults with inflammatory bowel
disease who undergo intestinal resection – A population-based study. Accepted:
Inflamm Bowel Dis 2014.
3.
de Silva S, Ma C, Proulx MC, et al. Postoperative Complications and Mortality
Following Colectomy for Ulcerative Colitis. Clin Gastroenterol Hepatol
2011;9:972-80.
4.
Nguyen GC, Tuskey A, Dassopoulos T, et al. Rising hospitalization rates for
inflammatory bowel disease in the United States between 1998 and 2004. Inflamm
Bowel Dis 2007;13:1529-35.
5.
Nguyen GC, Kaplan GG, Harris ML, et al. A national survey of the prevalence and
impact of Clostridium difficile infection among hospitalized inflammatory bowel
disease patients. Am J Gastroenterol 2008;103:1443-50.
6.
Murthy SK, Steinhart AH, Tinmouth J, et al. Impact of Clostridium difficile colitis
on 5-year health outcomes in patients with ulcerative colitis. Aliment Pharm Ther
2012;36:1032-9.
7.
Ananthakrishnan AN, McGinley EL, Saeian K, et al. Temporal trends in disease
outcomes related to Clostridium difficile infection in patients with inflammatory
bowel disease. Inflamm Bowel Dis 2011;17:976-83.
76
8.
Issa M, Vijayapal A, Graham MB, et al. Impact of Clostridium difficile on
inflammatory bowel disease. Clin Gastroenterol Hepatol 2007;5:345-51.
9.
Navaneethan U, Mukewar S, Venkatesh PG, et al. Clostridium difficile infection is
associated with worse long term outcome in patients with ulcerative colitis. J
Crohn's Colitis 2012;6:330-6.
10.
Jen MH, Saxena S, Bottle A, et al. Increased health burden associated with
Clostridium difficile diarrhoea in patients with inflammatory bowel disease.
Aliment Pharm Ther 2011;33:1322-31.
11.
Goodhand JR, Alazawi W, Rampton DS. Systematic review: Clostridium difficile
and inflammatory bowel disease. Aliment Pharm Ther 2011;33:428-41.
12.
Khan NA, Quan H, Bugar JM, et al. Association of postoperative complications
with hospital costs and length of stay in a tertiary care center. J Gen Intern Med
2006;21:177-80.
13.
Negron ME, Barkema HW, Rioux K, et al. Clostridium difficile infection in
ulcerative colitis: Increased risk of colectomy and postoperative infectious
complications. Am J Gastroenterol 2011;106:S481-2.
14.
Molodecky NA, Pannacione R, Ghosh S, et al. Challenges associated with
identifying the environmental determinants of the inflammatory bowel diseases.
Inflamm Bowel Dis 2011;17:1792-9.
15.
Ananthakrishnan AN, McGinley EL, Binion DG. Excess hospitalisation burden
associated with Clostridium difficile in patients with inflammatory bowel disease.
Gut 2008;57:205-10.
77
16.
Ricciardi R, Ogilvie JW, Roberts PL, et al. Epidemiology of Clostridium difficile
colitis in hospitalized patients with inflammatory bowel diseases. Dis Colon
Rectum 2009;52:40-5.
17.
Alberta Health Services. Alberta Health Services Annual Report, April 1,2009March 31, 2010. http://www.albertahealthservices.ca/Publications/ahs-pub-annualrpt.pdf. Accessed: June 1, 2014.
18.
Alberta Health. Alberta health care insurance plan. 2014. Alberta Health Services.
http://www.health.alberta.ca/health-care-insurance-plan.html. Accessed: June 1,
2014.
19.
Ma C, Crespin M, Proulx MC, et al. Postoperative complications following
colectomy for ulcerative colitis: A validation study. BMC Gastroenterology
2012;12:39.
20.
Rezaie A, Quan H, Fedorak R, et al. Development and validation of an
administrative case definition for inflammatory bowel diseases. Can J
Gastroenterol 2012;26:711-8.
21.
Ma C, Crespin M, Proulx MC, et al. Accuracy of administrative data in identyfying
ulcerative colitis patients presenting with acute flare and undergoing colectomy.
Ame J Gastroenterol 2010;105:A1270.
22.
Kaplan GG, McCarthy EP, Ayanian JZ, et al. Impact of hospital volume on
postoperative morbidity and mortality following a colectomy for ulcerative colitis.
Gastroenterology 2008;134:680-7.
78
23.
Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining
comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care
2005;43:1130-9.
24.
Hubert B, Loo VG, Bourgault AM, et al. A portrait of the geografic dissemination
of the Clostridium difficile North American pulsed-field type 1 strain and the
epidemiology of C. difficile-associated disease in Quebec. Clin Infect Dis
2007;44:238-244.
25.
Sundram F, Guyot A, Carboo I, et al. Clostridium difficile ribotypes 027 and 106:
clinical outcomes and risk factors. J Hosp Infect 2009;72:111-118.
26.
Yanai H, Nguyen GC, Yun L, et al. Practice of gastroenterologists in treating
flaring inflammatory bowel disease patients with Clostridium difficile: Antibiotics
alone
or
combined
antibiotics/immunomodulators?
Inflamm
Bowel
Dis
2011;17:1540-6.
27.
Jodorkovsky D, Young Y, Abreu MT. Clinical outcomes of patients with ulcerative
colitis and co-existing Clostridium difficile infection. Dig Dis Sci 2010;55:415-20.
28.
Murthy SK, Steinhart AH, Tinmouth J, et al. Impact of Clostridium difficile colitis
on 5-year health outcomes in patients with ulcerative colitis. Aliment Pharm Ther
2012;36:1032-9.
29.
Buffie CG, Pamer EG. Microbiota-mediated colonization resistance against
intestinal pathogens. Nat Rev Immunol 2013;12:790-801.
30.
Kyne L, Warny M, Qamar A, et al. Asymptomatic carriage of Clostridium difficile
and serum levels of IgG antibody against Toxin A. New Engl J Med 2000;342:3907.
79
31.
Ananthakrishnan AN, Oxford EC, Nguyen DD, et al. Genetic risk factors for
Clostridium difficile infection in ulcerative colitis. Aliment Pharmacol Ther
2013;38:522-3
80
Table 3.1. Characteristics of patients diagnosed with ulcerative colitis in Alberta, Canada, between April 1st, 2003 and
March 31st, 2010 stratified by colectomy.
Characteristics
All
(n = 1,754)
No Colectomy
(n = 1,518)
Colectomy
(n = 234)
P-Value
Gender, % (n)
Male
Female
50.4 (884)
49.6 (870)
49.1 (746)
50.9 (774)
59.0 (138)
41.0 (96)
0.005
Age at diagnosis, y*
25th percentile
Median
75th percentile
28
41
55
28
41
55
30
46
57
N/A
N/A
24
275.5
648
N/A
Comorbidities, % (n)
0
1
≥2
40.8 (715)
29.2 (515)
30.0 (527)
42.2 (642)
29.9 (454)
27.9 (424)
31.2 (73)
24.8 (58)
44.0 (103)
< 0.001
C. difficile, % (n)
No
Yes
95.4 (1,673)
4.6 (81)
96.25 (1,463)
3.75 (57)
89.7 (210)
10.3 (24)
< 0.001
Time to first C. difficile, d†
25th percentile
0
0
0
0.588
Time to surgery, d†
25th percentile
Median
75th percentile
0.051
81
Median
75th percentile
14
274
16
286
13
228.5
Death
No
Yes
96.9 (1,699)
3.1 (55)
97.1 (1,476)
2.9 (44)
95.3 (223)
4.7 (11)
* years, † days
82
0.156
Table 3.2. Results of the competing risk regression and the Cox proportional hazard
models for time to colectomy and time to death since UC diagnosis with prior C.
difficile diagnosis as the primary predictor
Variables
Time to Colectomy
SHazard ratio (95% CI)
Time to death
Hazard ratio (95% CI)
C. difficile
No
Yes
Baseline
2.36 (1.47-3.80)
Baseline
2.56 (1.28-5.10)
Gender
Female
Men
Baseline
1.65 (1.24-2.19)
Baseline
0.89 (0.52-1.52)
Age at diagnosis, y*
0.99 (0.98-1.0)
1.09 (1.07-1.12)
Comorbidities
0
1
≥2
Baseline
1.10 (0.75-1.61)
2.30 (1.60-3.32)
Baseline
0.68 (0.09-4.94)
5.96 (1.36-26.04)
* years
83
Table 3.3. Characteristics of colectomy patients stratified by developing any
postoperative complication in hospital
Characteristics
No
(n = 138)
Any complication
Yes
(n = 96)
P-Value
Gender, % (n)
Male
Female
57.25 (79)
42.75 (59)
61.5 (59)
38.5 (37)
0.589
Age at admission, y*
25th percentile
Median
75th percentile
28
40
56
30
50
64.5
0.001
Time to surgery, d†
25th percentile
Median
75th percentile
0
52
358
5
193
648.5
0.038
Comorbidities
0
1
≥2
65.9 (91)
18.2 (25)
15.9 (22)
59.4 (57)
24.0 (23)
16.7 (16)
0.511
Time between first C. difficile and
colectomy,
No C. difficile
94.9 (131)
†
> 90 d
2.2 (3)
≤ 90 d†
2.9 (4)
82.3 (79)
5.2 (5)
12.5 (12)
0.005
Type of Surgery
Endoscopic
Open
18.1 (25)
81.9 (113)
10.4 (10)
89.6 (86)
0.136
Type of admission
Elective
Emergent
43.5 (60)
56.5 (78)
20.8 (20)
79.2 (76)
< 0.001
* years
†
days
84
Table 3.4. Results of multilevel regression models for the development of any
postoperative complication and a postoperative infectious complication with C.
difficile as primary predictor (facility was included as a random effect in the
models)
Variables
Any Complication
OR
(95%CI)
Time between first C. difficile and colectomy,
No C. difficile
> 90 d†
≤ 90 d†
Baseline
2.48
4.84
(0.42-14.65)
(1.28-18.35)
Age at admission, y*
1.03
(1.01-1.05)
Gender
Female
Male
Baseline
1.58
(0.83-2.99)
Colectomy admission
Elective
Emergent
Baseline
3.36
(1.68-6.71)
Type of procedure
Endoscopic
Open
Baseline
2.90
(1.13-7.45)
Comorbidities
0
1
≥2
Baseline
1.19
0.63
(0.54-2.61)
(0.26-1.54)
* years, † days
85
Figure 3.1. Flow diagram of patient selection criteria
Alberta Health Care Insurance Plan
Registry
DAD1
ACCS2
PC3
IBD case definition4: 2-4-2-2
23,681 IBD patients identified
Crohn’s disease patients (n=12,153) &
IBDU5 patients (n=3,517)
8,011 UC patients
Not registered as an Alberta resident
(n=38)
7,973 UC patients
Prevalent cases(n=5,965)
2,008 UC incident6 patients
A colectomy code prior to UC
diagnosis (n=24)
Diagnosis of C. difficile prior
to UC diagnosis (n=32)
Age at UC diagnosis <18 years
(n=203)
1,754 UC incident patients
1
Alberta Inpatient Hospital Discharge Abstract Database
Ambulatory Care Classification System Database
3
Alberta Physicians’ Claims Database
4
A diagnostic code for IBD in ≥2 hospitalizations within a 2-year period (DAD) or ≥4 ambulatory contact within a 2-year period
(ACCS) or ≥2 physician contacts within a 2-year period (PC)
5
IBD unknown based on scoring system
6
Incident cases where those who were Alberta residents for at least 8 years prior to the date of the first UC contact
2
86
30
25
20
15
10
5
0
Cumulative incidence of colectomy (%)
Figure 3.2. Survival curves for the proportion of colectomy free UC patients (a) and
survival of UC patients (b) stratified by having C. difficile diagnosis (red line) and
no C. difficile diagnosis (blue line)
0
365
730
1095
1460
1825
Time since UC diagnosis (days)
cdif_1=0
87
cdif_1=1
2190
2555
3.7 Appendix 3.A List of postoperative complications and the respective ICD-10
code by category
Category
Gastrointestinal
Code
K22.8
K25
K26
K27
K29.0
K55.0
K56.0
K56.6
K56.7
K62.5
K63.1
K63.8
K66.0
K72.0
K72.9
K85
K92
K56.6
K91.3
K91.4
K91.8
K91.9
T76.2
T81.0
Renal and Endocrine
E15
E27.2
E86
E87
N13.9
Complication
Other specified diseases of esophagus or haemorrhage
of esophagus NOS
Gastric ulcer
Duodenal ulcer
Peptic ulcer, site unspecified
Acute haemorrhagic gastritis
Acute vascular disorders of intestine, unspecified
Paralytic ileus
Other and unspecified intestinal obstructions
Ileus, unspecified
Haemorrhage of anus and rectum
Perforation of intestine (nontraumatic)
Other specified diseases of intestine
Peritoneal adhesions
Acute and subacute hepatic failure
Hepatic failure, unspecified
Acute pancreatitis
Other diseases of digestive system
Intestinal adhesion [bands] with obstruction
Postoperative intestinal obstruction
Colostomy and enterostomy malfunction
Other postprocedural disorders of digestive system, not
elsewhere classified
Postprocedural disorder of digestive system,
unspecified
Traumatic secondary and recurrent haemorrhage
Haemorrhage and haematoma complicating a
procedure, not elsewhere classified
Non diabetic hypoglaecemic coma
Addisonian crisis; adrenal crisis; adrenocortical crisis
Volume depletion
Other disorders of fluid, electrolyte and acid-base
balance
Obstructive and reflux uropathy, unspecified: urinary
88
N17
N19
N31.2
R33
N10
N12
N99.0
N99.9
Cardiovascular
I21
I26
I46
I48
I49
I50
I74
I80
I81
I92
I95.0
I95.2
I95.9
R57
I97.8
I97.9
T79.0
T80.0
T80.1
T81.1
T81.7
tract obstruction NOS
Acute renal failure
Unspecified kidney failure
Flaccid neuropathic bladder, not elsewhere classified
Retention of urine
Acute tubulo-interstitial nephritis
Tubulo-interstitial nephritis, not specified as acute or
chronic
Postprocedural renal failure
Postprocedural disorder of genitourinary system,
unspecified
Acute myocardial infarction
Pulmonary embolism
Cardiac arrest
Atrial fibrillation and flutter
Other cardiac
Heart failure
Arterial embolism and thrombosis
Phlebitis and thrombophlebitis
Portal vein thrombosis
Other venous embolism and thrombosis
Idiopathic hypotension
Hypotension due to drugs
Hypotension, unspecified
Shock, not elsewhere specified
Other postprocedural disorders of the circulatory
system, not elsewhere classified
Post procedural disorder of circulatory system,
unspecified
Air embolism (traumatic)
Air embolism following infusion, transfusion and
therapeutic injection
Vascular complications following infusion, transfusion
and therapeutic injection
Shock during or resulting from a procedure, not
elsewhere classified
Vascular complications following a procedure, not
89
Pulmonary
Neurological
T88.2
J80
J81
J90
J91
J93
J96.0
J96.9
J98.1
R09
J95.5
J95.8
F05
F13
F15
F19
G45
G46
G81
G82
G83
G93.1
G93.6
G97.0
G97.1
163
165
G97.8
elsewhere classified
Shock due to anaesthesia
Adult respiratory distress syndrome
Pulmonary oedema
Pleural effusion, not elsewhere classified
Pleural effusion in conditions classified elsewhere
Pneumothorax
Acute respiratory failure
Respiratory failure, unspecified
Pulmonary collapse
Other symptoms and signs involving the circulatory
and respiratory systems
Postprocedural subglottic stenosis
Other postprocedural respiratory disorders
Delirium, not induced by alcohol or other psychoactive
substances
Mental and behavioural disorders due to use of
sedatives or hypnotics
Mental and behavioural disorders due to use of other
stimulants, including caffeine
Mental and behavioural disorders due to multiple drug
use and use of other psychoactive substances
Transient cerebral and ischaemic attacks and related
syndromes
Vascular syndromes of brain in cerebrovascular
diseases
Hemiplegia
Paraplegia and tetraplegia
Other paralytic syndromes
Anoxic brain damage, not elsewhere classified
Cerebral oedema
Cerebrospinal fluid leak from spinal puncture
Other reaction to spinal and lumbar puncture
Cerebral infarction
Occlusion and stenosis of precerebral arteries, not
resulting in cerebral infarction
Other postprocedural disorders of nervous system
90
Wounds
G97.9
K60.3
K60.4
K60.5
K63.2
K82.9
K83.2
L89
N36.0
N82.4
T81.2
T81.3
T81.5
T81.8
Infections
T81.9
A40
A41
B95
B96
J10.0
J11.0
J12
J13
J14
J15
J16
J17
J18
J69.0
J85
J86
Postprocedural disorder of nervous system, unspecified
Anal fistula
Rectal fistula
Anorectal fistula
Fistula of intestine
Disease of gallbladder, unspecified
Perforation of bile duct
Decubitus ulcer and pressure
Urethral fistula
Other female intestinal-genital tract fistulae
Accidental puncture and laceration during a procedure,
not elsewhere classified
Disruption of operation wound, not elsewhere classified
Foreign body accidentally left in body cavity or
operation wound following a procedure
Other complications of procedures, not elsewhere
classified
Unspecified complication of procedure
Streptococcal sepsis
Other sepsis
Streptococcus and staphylococcus as the cause of
disease classified to the other chapters
Influenza with pneumonia, other influenza virus
identified
Influenza with pneumonia, virus not identified
Viral pneumonia, not elsewhere classified
Pneumonia due to Streptococcus pneumoniae
Pneumonia due to Haemophilus influenzae
Bacterial pneumonia, not elsewhere classified
Pneumonia due to other infectious organisms, not
elsewhere classified
Pneumonia in diseases classified elsewhere
Pneumonia, organism unspecified
Pneumonitis due to food and vomit
Abscess of lung and mediastinum
Pyothorax
Abscess of anal and rectal regions
91
K61
K63
K65
L03
L04
N15.1
N15.9
N30.9
N39.0
T80.2
A49
T79.3
T81.4
T81.6
T82.7
T83.6
T85.7
Other
T88.3
T88.5
T88.6
T88.7
R78.8
T88.8
T88.9
Abscess of intestine
Peritonitis
Cellulitis
Acute lymphadenitis
Renal and perinephric abscess
Renal tubule-interstitial disease, unspecified
Acute cystitis
Cystitis, unspecified
Urinary tract infection, site not specified
Infections following infusion, transfusion and
therapeutic injection
Bacterial infection of unspecified site
Post-traumatic wound infection, not elsewhere
classified
Infection following a procedure, not elsewhere
classified
Acute reaction to foreign substance accidentally left
during a procedure
Infection and inflammatory reaction due to other
cardiac and vascular devices, implants and grafts
Infection and inflammatory reaction due to prosthetic
device, implant and graft in genital tract
Infection and inflammatory reaction due to other
internal prosthetic devices, implants, and grafts
Malignant hyperthermia due to anaesthesia
Other complications of anaesthesia
Anaphylactic shock due to adverse effect of correct
drug or medicament properly administered
Unspecified adverse effect of drug or medicament
Finding of other specified substances, not normally
found in blood
Complication of surgical and medical care, unspecified
Other specified complications of surgical and medical
care, not elsewhere classified
92
3.8 Appendix 3.B Charlson comorbidities
Comorbidity
Codes
Acute myocardial infarction
410, 412, I21, I22, I252
Congestive heart failure
428, I43, I50, 39891, 40201, 40211, 40291, 40401, 4254, 4255, 4257,4258, 4259, I099,
I110, I130, I426, I427, I428, I429, P290, I132, I255, I420, I425, 40403, 40411, 40413,
40491, 40493
4439, V434, 7854, 5571, 5579, V434, 0930, 4373, 4431, 4432, 4438, 4439, 4471, 441,
I70, I71, I731, I738, I739, I771, I790, I792, K551, K558, K559, Z958, Z959
430, 431, 432, 433, 434, 435, 436, 437, 438, 36234, 430, 431, 432, 433, 434, 435, 436,
437, 438, G45, G46, I60, I61, I62, I63, I64, I65, I66, I67, I68, I69, H340
290, 2941, 3312, F00, F01, F02, F03, G30, F051, G311
Peripheral vascular disease
Cerebrovascular disease
Dementia
Chronic pulmonary disease
490, 491, 492, 493, 494, 495, 496, 500, 501, 502, 503, 504, 505, 5064, 4168, 4169, 5064,
5081, 5088, 490, 491, 492, 493, 494, 495, 496, 500, 501, 502, 503, 504, 505, J40, J41,
J42, J43, J44, J45, J46, J47, J60, J61, J62, J63, J64, J65, J66, J67, I278, I279, J684, J701,
J703
Rheumatologic disease (connective 7100, 7101, 7104, 7140, 7141, 7142, 725, 71481, 4465, 7100, 7101, 7102, 7103, 7104,
tissue disease)
7140, 7141, 7142, 7148, 725, M05, M32, M33, M34, M06, M315, M351, M353, M360
Peptic ulcer disease
531, 532, 533, 534, 531, 532, 533, 534, K25, K26, K27, K28
Mild liver disease
5712, 5714, 5715, 5716, 57140, 57141, 57149, 07022, 07023, 07032, 07033, 07044,
07054, 0706, 0709, 5733, 5734, 5738, 5739, V427, 570, 571, B18, K73, K74, K700,
K701, K702, K703, K709, K713, K714, K715, K717, K760, K762, K763, K764, K768,
K769, Z944
93
Moderate or severe liver disease
Metastatic carcinoma
5722, 5723, 5724, 5728, 4560, 4561, 4562, 45620, 45621, 4560, 4561, 4562, 5722, 5723,
5724, 5728, K704, K711, K721, K729, K765, K766, K767, I850, I859, I864, I982
2500, 2501, 2502, 2503, 2507, 2500, 2501, 2502, 2503, 2508, 2509, E100, E101, E106,
E108, E109, E110, E111, E116, E118, E119, E120, E121, E126, E128, E129, E130,
E131, E136, E138, E139, E140, E141, E146, E148, E149
2504, 2505, 2506, 2504, 2505, 2506, 2507, E102, E103, E104, E105, E107, E112, E113,
E114, E115, E117, E122, E123, E124, E125, E127, E132, E133, E134, E135, E137,
E142, E143, E144, E145, E147
342, G81, G82, 3341, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3449, G041, G114,
G801, G802, G830, G831, G832, G833, G834, G839
5830, 5831, 5832, 5834, 5836, 5837, 582, 585, 586, 588, 582, 585, 586, V56, 5830,
5831, 5832, 5834, 5836, 5837, 5880, V420, V451, 40301, 40311, 40391, 40402, 40403,
40412, 40413, 40492, 40493, N052, N053, N054, N055, N056, N057, N250, I120, I131,
N032, N033, N034, N035, N036, N037, Z490, Z491, Z492, Z940, Z992, N18, N19
C00, C01, C02, C03, C04, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 172, 174,
175, 176, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
195, 200, 201, 202, 203, 204, 205, 206, 207, 208, C05, C06, C07, 2386, C08, C09, C10,
C11, C12, C13, C14, C15, C16, C17, C18, C19, C20, C21, C22, C23, C24, C25, C26,
C30, C31, C32, C33, C34, C37, C38, C39, C40, C41, C43, C45, C46, C47, C48, C49,
C50, C51, C52, C53, C54, C55, C56, C57, C58, C60, C61, C62, C63, C64, C65, C66,
C67, C68, C69, C70, C71, C72, C73, C74, C75, C76, C81, C82, C83, C84, C85, C88,
C90, C91, C92, C93, C94, C95, C96, C97, 174, 195, 200, 208, 140, 172
196, 197, 198, 199, 196, 197, 198, 199, C77, C78, C79, C80
HIV
042, 043, 044, 042, 043, 044, B20, B21, B22, B24
Diabetes without complication
Diabetes with chronic
complications
Hemiplegia or paraplegia
Renal disease
Cancer
94
3.9 Appendix 3.C Classification of colectomy approach and ICD-10 codes
Approach
Code
Technique
Open
Code
Endoscopic
Code
1.NM.87.DA
1.NM.87.BA
1.NM.87.LA
1.NM.87.^^
1.NM.89.^^
1.NM.91.^^
Partial excision of large
intestine
Total excision of large
intestine
Radical excision of large
intestine
1.NM.87.RN
1.NM.87.DF
1.NM.87.RD
1.NM.87.DE
1.NM.87.RE
1.NM.87.DN
1.NM.87.TF
1.NM.87.DX
1.NM.87.TG
1.NM.87.
1.NM.89.RN
1.NM.89.DF
1.NM.89.TF
1.NM.89.DX
1.NM.91.RN
1.NM.91.DF
1.NM.91.RD
1.NM.91.DE
1.NM.91.RE
1.NM.91.DN
95
Definition
simple excisional technique
colocolostomy anastomosis
technique
colorectal anastomosis
technique
enterocolostomy anastomosis
technique
stoma formation and distal
closure
stoma formation with creation
of mucous fistula
ileorectal anastomosis
technique
stoma formation with distal
closure
colocolostomy anastomosis
technique
colorectal anastomosis
technique
enterocolostomy anastomosis
technique
1.NQ.89.^^
1.NQ.90^^
Total excision of the
rectum
Total excision with
reconstruction of rectum
1.NM.91.TF
1.NM.91.DX
1.NM.91.TG
1.NM.91.DY
1.NQ.89.SF
1.NQ.89.KZ
1.NQ.89.SF-XX-G
1.NQ.89.KZ-XX-G
1.NQ.89.RS
1.NQ.89.LH
1.NQ.89.RS-XX-G
1.NQ.89.LH-XX-G
1.NQ90.LA-XX-G
96
1.NQ.89.GV
stoma formation and distal
closure
stoma formation with creation
of mucous fistula
coloanal anastomosis
technique
pouch formation
1.NQ.89.AB
stoma formation with distal
closure
continent ileostomy formation
pouch formation
3.10 Appendix 3.D Types of postoperative complications
Timing of C. difficile
Postoperative
complication
Gastrointestinal, % (n)
No C. difficile
(n=210)
Over 90 days prior
to colectomy
(n=8)
Up to 90 days
prior to colectomy
(n=16)
19.1 (40)
37.5 (3)
37.5 (6)
0 (0)
18.8 (3)
Renal and Endocrine, % 5.7 (12)
(n)
Cardiovascular, % (n)
9.5 (20)
0 (0)
31.3 (5)
Pulmonary, % (n)
5.2 (11)
0 (0)
37.5 (6)
Neurological, % (n)
1.4 (3)
0 (0)
6.3 (1)
Wound, % (n)
9.05 (19)
25.0 (2)
31.3 (5)
Infectious, % (n)
17.1 (36)
25.0 (2)
31.3 (5)
Other, % (n)
1. (2)
0 (0)
0 (0)
97
3.11 Appendix 3.E Characteristics of colectomy patients stratified by developing
infectious complications in hospital
Characteristics
No
(n = 191)
Infectious complication
Yes
P-Value
(n = 43)
Gender, % (n)
Male
Female
39.8 (76)
60.2 (115)
46.5 (20)
53.5 (23)
0.493
Age at admission, y*
25th percentile
Median
75th percentile
29
44
57
46
52
68
0.004
Time to surgery, days
25th percentile
Median
75th percentile
36
351
680
0
54
627
0.011
Comorbidities
0
1
≥2
63.9 (122)
19.4 (37)
16.7 (32)
69.5 (26)
25.6 (11)
13.9 (6)
0.651
Time between first C. difficile
and colectomy,
No C. difficile
91.1 (174)
> 90 days
3.1 (6)
≤ 90 days
5.8 (11)
83.7 (36)
4.7 (2)
11.6 (5)
0.244
Type of Surgery
Endoscopic
Open
16.7 (32)
83.3 (159)
6.7 (3)
93.0 (40)
0.153
Type of admission
Elective
Emergent
38.7 (74)
61.3 (117)
14.0 (6)
86.0 (37)
0.002
98
3.12 Appendix 3.F Results of multilevel logistic regression model for the
development of infectious complications with C. difficile as primary predictor
(hospital was included as a random effect in the model)
Variables
Infectious Complication
OR
(95% CI)
Time between first C. difficile and colectomy,
No C. difficile
> 90 days
≤ 90 days
Baseline
1.67
1.53
(0.26-10.59)
(0.46-5.09)
Age
1.03
(1.01-1.05)
Gender
Female
Male
Baseline
0.80
(0.39-1.65)
Colectomy admission
Elective
Emergent
Baseline
3.88
(1.50-10.00)
Type of procedure
Endoscopic
Open
Baseline
3.02
(0.83-11.01)
Comorbidities
0
1
2
Baseline
1.27
0.59
(0.52-3.08)
(0.20-1.73)
99
Chapter Four: Changes in the annual incidence of colectomy for colorectal neoplasia
among patients with ulcerative colitis: A population-based cohort
100
4.1 Abstract
Incidence of surgery among ulcerative colitis (UC) patients has decreased over
time. This decrease in incidence has been attributed to medications (i.e.
immunomodulators and biologics) that have improved disease management. However,
the reduced risk of colectomy could also be attributed to a lower incidence of colectomy
for colorectal dysplasia or cancer. Thus, a multi-method approach was performed to
determine whether the incidence of colectomy for colorectal dysplasia or cancer has
changed over time. Adult UC patients who had a colectomy between 1997 and 2009 were
identified. First, a logistic regression analysis examined the association between year and
indication for colectomy (dysplasia or cancer vs. medically refractory disease) after
controlling for potential confounders. Second, the annual incidence of colectomy for
colorectal dysplasia or cancer and medically refractory disease was calculated for each
year. The referent population was all patients with UC living in the Calgary Health Zone
and at risk for undergoing a colectomy. A generalized linear model was built to assess
changes in colectomy rates during the study period. Of the 638 patients admitted to
hospital who underwent a colectomy, 10% (n = 58) had a colectomy due to colorectal
dysplasia or cancer. The incidence of colectomy for colorectal neoplasia was stable in
both analyses, whereas colectomy rates for medically refractory disease decreased from
1997 to 2009. Future studies are needed to evaluate modifiable factors that may reduce
the risk of dysplasia or cancer among patients with UC.
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4.2 Introduction
Ulcerative colitis (UC) is a chronic disease characterized by inflammation of the
large intestine. Most patients with UC require medications to induce and maintain
remission. Colectomy is warranted among those with medically refractory disease and/or
complications, and for those with colorectal dysplasia or cancer. Colectomy is an
important outcome among patients with UC due to the morbidity and mortality associated
with this procedure.1 A meta-analysis demonstrated that 16% of patients with UC
underwent a colectomy within 10 years of diagnosis.2 Furthermore, the risk of colectomy
within 10 years of diagnosis decreased significantly across time. Advances in medical
management of UC including the advent of mesalamine, immunomodulators, and
biologics have led to a decrease in the risk of colectomy for medically refractory disease.
UC patients have a higher risk of developing colorectal cancer when compared to
the non-IBD population. A meta-analysis reported that the 10 and 20-year risk of
developing dysplasia or cancer was 2 and 8%, respectively.3 However, advances in the
medical management of UC may have affected the risk of dysplasia and colorectal cancer
among patients with UC. More recent estimates from a meta-analysis of population-based
studies reported a 20-year risk of dysplasia and cancer between 1.1-5.3%.4 Some of the
factors that increase the risk of colorectal cancer are: age and gender, greater disease
extent, long disease duration, primary sclerosing cholangitis (PSC), familial history of
colorectal cancer.4-7 Experimental studies have shown that both 5-ASA and biologics
could be used as chemoprotectants.8,9 However, a recent meta-analysis concluded that 5ASA does not confers a protective effect against the development of colorectal cancer.10
102
Studies that have evaluated whether the risk of dysplasia or cancer has decreased
over time are contradictory. Only one of these studies reported that the incidence of
colorectal cancer is increasing with an overall incidence rate of 14 cases per 1,000 patient
years (95%CI: 5-34 per 1,000 patient years).11 Two studies reported a decrease in the
incidence of colorectal cancer with only one performing a meta-regression but the results
were not statistically significant.12,13 These differences may be explained by selection
biases of the target population (i.e. tertiary referral centers vs. population-based studies),
the methodology used to assess changes over time, and geographic differences in clinical
practice.14
Thus, we evaluated the risk profile for the development of dysplasia and
colorectal cancer, and determined if the incidence of colectomy for colorectal dysplasia
or cancer has change over time.
4.3 Materials and Methods
4.3.1 Data sources and study population
4.3.1.1 Data Integration, Measurement, and Reporting Hospital Discharge Abstract
The Calgary Health Zone (CHZ) in Alberta, Canada is a population-based health
authority providing health care to residents in Calgary and over 20 nearby cities under a
public single-payer system.15 In 2009 the estimated population of the CHZ in 2009 was
1,326,115.16 The DIMR database was used to identify all adult UC patients (≥ 18 years of
age) who were admitted to CHZ hospitals with a diagnosis of UC who underwent a
colectomy between 1 January 1997 and 31 December 2009. Medical chart review
confirmed all patients with UC who underwent a colectomy.
103
4.3.1.2 Alberta Health Care Insurance Plan (AHCIP)
The AHCIP provides universal health coverage to more than 99% of Alberta’s
population (~3 million residents).17 Each Alberta resident has a unique personal health
identification number, which can be used to track the patient through time and to link the
patient to different health data sources in Alberta. Information provided to investigators is
de-identifiable. Four data sources from AHCIP were used in this study to identify UC
patients at risk for a colectomy. The AHCIP Registry database contains participants’
demographic and geographic information for each fiscal year. The Alberta Inpatient
Hospital Discharge Abstract Database (DAD) contains demographic information, year,
service dates, 25 diagnostic codes (ICD-9: from 1995 to 2002; ICD-10-CA: from 2003 to
2010), 20 procedure codes. The Alberta Physicians’ Claims (PC) Database contains
claims for all people seen by a practitioner in Alberta. Information consists of
demographic data, service date, 3 diagnostic codes (ICD-9) and one procedure code. The
Ambulatory Care Classification System (ACCS) Database collects information on
facility-based ambulatory care ranging from emergency visits and outpatient procedures
to education treatment and rehabilitation programs. Recorded data consists of
demographic data, year, service dates, 10 diagnostic codes (ICD-9: from 1997 to 2002;
ICD-10-CA: from 2003 to 2010), and 10 procedure codes. The AHCIP databases were
used to identify all adult UC patients (≥ 18 years of age) who lived in the CHZ between 1
January 1997 and 31 December 2009.
4.3.2 Identification of UC colectomy patients in CHZ hospitals and data collection
The DIMR database was used to identify hospitalizations of UC patients who had
a colectomy in CHZ hospitals between 1997 and 2009. The approach of identifying the
104
study population was previously validated.18 Briefly, the DIMR was used to identify: 1)
patients admitted to hospital with a UC diagnosis (ICD-9-CM 556.X or ICD-10-CA
K51.X) in any diagnostic position and a procedural code for colectomy (ICD-9-CM 45.7,
45.8 or CCI 1.NM.87, 1.NM.89, 1.NM.91, 1.NQ.89, 1.NQ.90).
The primary outcome was the indication for colectomy: colorectal dysplasia or
cancer versus medically refractory. Colorectal dysplasia or cancer was defined by review
of the pathology report for the colectomy specimen. Medically refractory disease was
defined as patients who underwent colectomy because of failed medical management or
complication (e.g. toxic megacolon) requiring surgery. Data extracted during chart
review for the primary variables of interests was based on a priori defined risk factors4-7
for colorectal cancer and included: age, (defined as age at colectomy admission, stratified
as: 18-34, 35-64, and ≥ 65 years); sex; disease extent (left-sided versus pancolitis);
disease duration (defined as the interval between UC diagnosis and colectomy date), and
smoking (current, ex-smoker, never) at time of colectomy. IBD medications (5-ASA or
sulfasalazine, azathioprine/6-mercaptopurine) taken at time of admission and/or
administered in-hospital were recorded. Patients were classified as having comorbidities
(i.e. health conditions occurring before hospitalization) if they had at least one of the
comorbidities listed in Appendix 4.A (stratified as no comorbidities, 1-2 comorbidities or
> 2 comorbidities). Of note, PSC was not included as comorbidity as it was included as a
separate variable on the analysis. The definition of comorbidities have been previously
validated for UC.18
105
4.3.3 Identification of UC patients at risk for colorectal cancer in CHZ using AHCIP
The AHCIP’s Registry, PC, DAD, and ACCS databases were used to identify
those UC patients at risk of having a colectomy in the CHZ. First, we identified all IBD
patients living in Alberta using a previously validated case definition of IBD.19 The
validated Alberta algorithm for identifying a prevalent case of IBD involves the
identification of diagnostic codes for IBD (i.e. ICD-9: 555; 556; ICD-10: K50; K51) in
the DAD database (≥ 2 hospitalizations within a 2-year period), or the PC database (≥ 4
physician contacts within a 2-year period), or the ACCS database (≥ 2 physician contacts
within a 2-year period). This algorithm has a sensitivity of 83.4% and a specificity of
99.8%.19
Next, we used a previously validated IBD scoring system to distinguish UC from
CD on those patients that fulfilled the prevalent case definition. A +1 score was assigned
to any hospital admission or physician contact with an ICD code for UC (556 or K51)
and a -1 score for an ICD code for CD (555 or K51).19 A cumulative score was calculated
and those with a score > 2 were considered UC patients and those with a score < -2 were
considered CD patients. Patients with a score between -2 and 2 were classified as IBD
unknown. This scoring system has a sensitivity of 86.3% and a specificity of 99.7%.19
Prevalent cases of CD and IBD unknown were excluded. Next, prevalent UC cases were
identified for each fiscal year between 1997 and 2009. UC patients with a procedural
code for colectomy (ICD-9-CM 45.7, 45.8 or CCI 1.NM.87, 1.NM.89, 1.NM.91,
1.NQ.89, 1.NQ.90) prior to the fiscal year being assessed were excluded.
106
4.3.4 Analyses
4.3.4.1 Risk factors for colectomy for colorectal dysplasia or cancer
Descriptive statistics were performed using the Fisher’s exact test or the X2 test.
Continuous variables were expressed as medians with interquartile ranges (IQR) and
compared using the Wilcoxon rank sum test. We performed a multivariate logistic
regression model to determine differences in patients’ characteristics between UC
patients who had a colectomy because of colorectal dysplasia or cancer as compared to
those who had a colectomy because of medically refractory disease with year of
admission as the primary exposure. Known risk factors associated with colorectal
dysplasia or cancer among UC patients that were a priori forced into the regression
model included: age, sex, disease duration, disease extent, PSC, smoking, and the
presence of comorbidities. A temporal analysis was conducted by entering year into the
model as a continuous variable. For medications, a backwards elimination approach was
used to examine independent effects of additional variables on the need of surgery with
an entry P-value of < 0.20 and were kept in the model if the two-sided P-value was <
0.05 or there was evidence of confounding because their removal resulted in a 30%
change in the estimate of the primary exposure. Statistical Significance was defined as a
two-sided P-value < 0.05. Point estimates were presented as adjusted odds ratios (OR)
with 95% confidence interval (CI).
4.3.4.2 Incidence of colectomy for colorectal dysplasia or cancer and medically
refractory disease
The annual incidence of colectomy for colorectal dysplasia or cancer and the
annual incidence of colectomy for medically refractory disease was calculated by
107
dividing the total number of colectomies by the estimated number of UC patients at risk
of having a colectomy each year within the CHZ. A generalized linear model (GLM) was
created to test whether colectomy rates for colorectal dysplasia or cancer have changed
over time using year of colectomy admission (continuous) as the primary predictor while
adjusting for age and sex. An additional variable was included in the model to account for
annual differences on the size of the population at risk living in CHZ. A scale parameter
was included in the model to account for overdispersion. Significance was defined as a
two-sided P-value < 0.05. Point estimates were presented as adjusted risk ratios (RR)
with 95% CI. A second GLM was created to determine whether colectomy rates for
medically refractory disease have changed over time using the same procedure as before.
Statistical analyses were conducted using STATA statistical software version 11
(STATA Corp, College Station, TX, USA). The study was approved by the Conjoint
Health Research Ethics Board of the University of Calgary. Our study was conducted in
accordance with the strengthening the reporting of observational studies in Epidemiology
(STROBE).20
4.4 Results
4.4.1 Risk factors for colectomy for colorectal dysplasia or cancer
Between 1997 and 2009 we identified a population-based cohort of 638 UC
patients who had a colectomy at a CHZ hospital. From these, 9.1% (n = 58) had surgery
because of colorectal dysplasia or cancer. The median age at surgery among those who
had surgery for colorectal dysplasia or cancer was 53.9 years (range: 18.1-86.6 years),
while 77.6% were males (Table 4.1). The median age at surgery among those who had
108
surgery because of medically refractory UC was 39.1 years (range: 21.8-86 years), while
58.45% were males (Table 4.1).
Year of admission was not significantly associated with having a colectomy for
colorectal dysplasia or cancer (adjusted OR=1.04; 95% CI: 0.96-1.14) after adjusting for
clinical risk factors of colorectal dysplasia or cancer (Table 4.2). Patients with UC who
underwent a colectomy due to colorectal dysplasia or cancer were more likely to be men
(OR=2.55; 95%: 1.20-5.41) with longer disease duration (OR= 1.12; 95% CI: 1.09-1.15).
Prescription of mesalamine (OR=0.43; 95% CI: 0.22-0.86) and azathioprine/6mercaptopurine (OR=0.33; 95% CI: 0.12-0.91) were associated with a decreased risk of
colectomy due to dysplasia or cancer when compared to colectomy for medically
refractory disease.
4.4.2 Incidence of colectomy for colorectal neoplasia and medically refractory disease
Among patients with UC who were at risk for a colectomy between 1997 and
2009, the risk of having a colectomy due to medically refractory disease decreased (Year:
RR=0.97; 95% CI: 0.94-0.99), whereas the risk of having a colectomy for dysplasia or
cancer was stable (Year: RR=1.03; 95% CI: 0.94-1.11) (Figure 4.1). Additionally, the
risk of colectomy for colorectal dysplasia or cancer was stable after adjusting for age (3564 versus 18-34 years: RR=9.77, 95% CI: 1.55-61.54; >65 years versus 18-34 years:
RR=11.62, 95% CI: 1.79-75.59) and sex (males versus females: RR 3.42; 95% CI: 1.667.05).
109
4.5 Discussion
In this population-based study, we used a multi-method approach to determine
whether the incidence of colectomy for colorectal dysplasia or cancer has changed over
time. In both approaches the incidence of colectomy for neoplasia remained stable. In
contrast, the risk of colectomy for medically refractory patients (i.e. excluding patients
with colectomy for dysplasia or cancer) dropped significantly by 3% per year between
1997 and 2009. This finding is consistent with previously conducted population-based
studies demonstrating that the risk of colectomy has been decreasing across time.2,21 In
addition, those who had a colectomy for colorectal dysplasia and cancer were older males
with longer disease duration as compared to patients who underwent a colectomy due to
medically refractory UC.
Colorectal cancer is an established complication of chronic UC.3,4,11-13,22 In this
study, the risk of colectomy for dysplasia or colorectal cancer remained stable between
1997 and 2009. The annual incidence of colectomy for dysplasia in Calgary varied
between 0.2 and 1.1 cases per 1,000 UC patients. We used two control populations to
evaluate the risk of colectomy for dysplasia of cancer. The referent population in the first
analysis was patients with UC who underwent a colectomy due to failure of medical
management. The advantage of using this referent population was that the control
population had a complete chart review, which extracted clinically relevant information
that may have influenced the risk of colectomy. This analysis demonstrated that the risk
of colectomy for dysplasia and colorectal cancer was stable after adjusting for age, sex,
disease duration, disease extent, primary sclerosing cholangitis and other comorbidities,
smoking, and the use of medications. However, the conclusion that colectomy rates were
110
stable overtime is relative to a control population that only included patients with severe
UC requiring colectomy. This analysis did not account for patients with milder UC or
those with worse prognosis, but responded to medical management (e.g. biologics).
In response, we conducted a second analysis whereby the denominator was
defined as all patients with UC at risk for colectomy. This referent population was
derived from administrative health databases in Alberta using a previously validated
case-definition.19 This temporal analysis included all UC patients at risk for colectomy
and accounted for the increasing prevalence of UC overtime.23 This analysis also
demonstrated a stable rate of colectomy for dysplasia and cancer; however, the temporal
analysis was only adjusted for age and sex because these databases do not include
clinically relevant information on risk factors for colectomy. Despite the differences in
study design between both of these analyses, the risk estimates for temporal analyses
were nearly identical (year as continuous variable: OR=1.03 and RR=1.04) and nonsignificant. Thus, this multi-method study suggests that despite the advances to medical
management during this time period, colectomy for dysplasia or cancer remained
constant between 1997 and 2009.
We found that patients who had a colectomy for colorectal dysplasia and cancer
were more likely to be men with longer disease duration compared to those who had a
colectomy due to medically refractory disease. The role of IBD medications as
chemoprotectants remains controversial with differences observed between published
meta-analyses attributed to study inclusion criteria.10,24 A meta-analysis by Velayos et al.,
using population-based and tertiary center studies, demonstrated a protective association
between 5-ASA and colorectal dysplasia or cancer (OR=0.51; 95% CI: 0.38-0.69).24 On
111
the other hand, Nguyen et al., using only population-based studies, demonstrated that 5ASA did not protect against developing colorectal cancer (OR=0.95; 95% CI: 0.541.26).10 In our study, both mesalamine and azathioprine/6-mercaptopurine decreased the
risk of colectomy for dysplasia or cancer. However, because the control population was
UC patients with colectomy for medically refractory disease, the negative associations
may reflect risk adjustment for disease severity rather than a protective effect of these
medications. Additionally, we did not evaluate the effect of anti-TNF therapies on the
risk of neoplasia because the time between its introduction (2005)25 and our study end
point (2009) was insufficient to evaluate a reduction in the risk of dysplasia or cancer (i.e.
>10 years of follow-up required26,27). Future studies are needed to assess the effect of
anti-TNF therapies on the risk of colorectal dysplasia or cancer.
Both methodological approaches have distinct limitations.28 The first approach
extracted patient information through medical chart review that relied on clinicians for
the accuracy and completion of patients’ medical history. In addition, because of the
retrospective nature of the chart review, we did not include some important disease
characteristics like severity of inflammation.29 Also, the prevalence of dysplasia or cancer
was rare in our study population (n=58 over 13 years). For example, a diagnosis of PSC
doubled the odds of colectomy for colorectal neoplasia; however, the study lacked power
to demonstrate a significant effect. In addition, the study was limited to patients with UC
who underwent colectomy for dysplasia or cancer. UC patients with dysplasia that was
not treated with colectomy (e.g. unifocal low-grade dysplasia)30 and patients with cancer
not treated with colectomy (e.g. died) would not have been captured. For our second
approach, we used administrative data to identify patients with UC who were at risk for
112
colectomy for dysplasia or cancer. Despite using a validated case-definition for UC,
misclassification bias is inherent (e.g. 3% of UC cases are false-positive)19 and we were
not able to adjust for important confounders like medication and disease duration.
Finally, we could not adjust for PSC in the control population due to the low accuracy of
algorithms for PSC patient identification in administrative databases.31
Both methodological approaches with different referent populations consistently
demonstrate that the incidence of colectomy for colorectal dysplasia or cancer remained
stable between 1997 and 2009. Thus, the reported reduction in the overall rates of
colectomy for UC21 is most likely driven by a decrease in the incidence of colectomy due
to medically refractory disease. Our findings suggest that those at risk for a colectomy for
colorectal dysplasia and cancer are males with longer disease duration. Future studies are
needed to evaluate modifiable factors that may reduce the risk of dysplasia or cancer
among patients with UC.
113
4.6 References
1.
Kaplan GG, McCarthy EP, Ayanian JZ, et al. Impact of hospital volume on
postoperative morbidity and mortality following a colectomy for ulcerative colitis.
Gastroenterology 2008;134:680-7.
2.
Frolkis AD, Dykeman J, Negron ME, et al. Risk of surgery for inflammatory
bowel diseases has decreased over time: a systematic review and meta-analysis of
population-based studies. Gastroenterology 2013;145:996-1006.
3.
Eaden JA, Abrams KR, Mayberry JF. The risk of colorectal cancer in ulcerative
colitis: a meta-analysis. Gut 2001;48:526-35.
4.
Jess T, Rungoe C, Peyrin-Biroulet L. Risk of colorectal cancer in patients with
ulcerative colitis: a meta-analysis of population-based cohort studies. Clin
Gastroenterol Hepatol 2012;10:639-45.
5.
Triantafillidis JK, Nasioulas G, Kosmidis PA. Colorectal cancer and
inflammatory bowel disease: Epidemiology, risk factors, mechanisms of
carcinogenesis and prevention strategies. Anticancer Res 2009;29:2727-38.
6.
Velayos FS, Loftus EV, Jr., Jess T, et al. Predictive and protective factors
associated with colorectal cancer in ulcerative colitis: A case-control study.
Gastroenterology 2006;130:1941-9.
7.
Collins PD, Mpofu C, Watson AJ, et al. Strategies for detecting colon cancer
and/or dysplasia in patiens with inflammatory bowel disease. Cochrane Database
Syst Rev 2006;CD000279.
114
8.
Onizawa M, Nagaishi T, Kanai T, et al. Signaling pathway via TNF-alpha/NFkappaB in intestinal epithelial cells may be directly involved in colitis-associated
carcinogenesis. Am J Physiol Gastrointest Liver Physiol 2009;296:G850-9.
9.
Yan F, Polk DB. Aminosalicylic acid inhibits IkB kinase a phosphorylation of
IkBa in mouse intestinal epithelial cells. Gastroenterology 1999;116:602-9.
10.
Nguyen GC, Gulamhusein A, Bernstein CN. 5-aminosalicylic acid is not
protective against colorectal cancer in inflammatory bowel disease: a metaanalysis of non-referral populations. Am J Gastroenterol 2012;107:1298-304.
11.
Thomas T, Abrams KA, Robinson RJ, et al. Meta-analysis: cancer risk of lowgrade dysplasia in chronic ulcerative colitis. Aliment Pharmacol Ther
2007;25:657-68.
12.
Lutgens MW, van Oijen MG, van der Heijden GJ, et al. Declining risk of
colorectal cancer in inflammatory bowel disease: an updated meta-analysis of
population-based cohort studies. Inflamm Bowel Dis 2013;19:789-99.
13.
Castano-Milla C, Chaparro M, Gisbert JP. Systematic review with meta-analysis:
the declining risk of colorectal cancer in ulcerative colitis. Aliment Pharmacol
Ther 2014;39:645-59.
14.
Andersen NN, Jess T. Has the risk of colorectal cancer in inflammatory bowel
disease decreased? World J Gastroenterol 2013;19:7561-8.
15.
Alberta Health Services. Alberta Health Services: Calgary Zone. 2014.
http://www.albertahealthservices.ca/calgary-zone.asp. Accessed: June 1, 2014.
115
16.
Alberta Health Services. Alberta Health Services Annual Report, April 1,2009March
31,
2010.
http://www.albertahealthservices.ca/Publications/ahs-pub-
annual-rpt.pdf. Accessed: June 1, 2014.
17.
Alberta Health. Alberta health care insurance plan. 2014. Alberta Health Services.
http://www.health.alberta.ca/health-care-insurance-plan.html. Accessed: June 1,
2014.
18.
Ma C, Crespin M, Proulx MC, et al. Postoperative complications following
colectomy for ulcerative colitis: A validation study. BMC Gastroenterology
2012;12:39.
19.
Rezaie A, Quan H, Fedorak R, et al. Development and validation of an
administrative case definition for inflammatory bowel diseases. Can J
Gastroenterol 2012;26:711-8.
20.
Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of
Observational Studies in Epidemiology (STROBE): Explanation and elaboration.
PLoS Med 2007;4:e297.
21.
Kaplan GG, Seow CH, Ghosh S, et al. Decreasing colectomy rates for ulcerative
colitis: a population-based time trend study. Am J Gastroenterol 2012;107:187987.
22.
Crohn BB, Rosenberg H. The sigmoidoscopic picture of chronic ulcerative colitis
(non-specific). Am J Med Sci 1925;170:220-7.
23.
Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of
the inflammatory bowel diseases with time, based on systematic seview.
Gastroenterology 2012;142:46-54.
116
24.
Velayos FS, Terdiman JP, Walsh JM. Effect of 5-aminosalicylate use on
colorectal cancer and dysplasia risk: a systematic review and metaanalysis of
observational studies. Am J Gastroenterol 2005;100:1345-53.
25.
Stidham RW, Lee TC, Higgins PD, et al. Systematic review with network metaanalysis: the efficacy of anti-tumour necrosis factor-alpha agents for the treatment
of ulcerative colitis. Aliment Pharmacol Ther 2014;39:660-71.
26.
Kornbluth A, Sachar DB. Ulcerative colitis practice guidelines in adults:
American College Of Gastroenterology, Practice Parameters Committee. Am J
Gastroenterol 2010;105:501-23.
27.
Farraye FA, Odze RD, Eaden J, et al. AGA medical position statement on the
diagnosis and management of colorectal neoplasia in inflammatory bowel disease.
Gastroenterology 2010;138:738-45.
28.
Molodecky NA, Kaplan G. Environmental risk factors for inflammatory bowel
disease. Gastroenterol Hepatol 2010;6:339-46.
29.
Rubin DT, Huo D, Kinnucan JA, et al. Inflammation is an independent risk factor
for colonic neoplasia in patients with ulcerative colitis: a case-control study. Clin
Gastroenterol Hepatol 2013;11:1601-8.
30.
Siegel CA, Schwartz LM, Woloshin S, et al. When should ulcerative colitis
patients undergo colectomy for dysplasia? Mismatch between patient preferences
and physician recommendations. Inflamm Bowel Dis 2010;16:1658-62.
31.
Molodecky NA, Myers RP, Barkema HW, et al. Validity of administrative data
for the diagnosis of primary sclerosing cholangitis: a population-based study.
Liver Int 2011;31:712-20.
117
Table 4.1. Characteristics of ulcerative colitis patients admitted to Calgary Health
Zone hospitals that had a colectomy for colorectal dysplasia or cancer versus
colectomy for medically refractory UC.
Colectomy indication
Characteristics
Colorectal
neoplasia
(n = 58)
Medical refractory
(n = 580)
Age at operation, y*
25th Percentile
Median
75th Percentile
46.5
53.9
63.3
28.8
39.1
51.7
Gender, % (n)
Male
Female
77.6 (45)
22.4 (13)
58.45 (339)
41.55 (241)
Disease duration, y*
25th Percentile
Median
75th Percentile
Missing (n)
10
18
30
3
1
3
9
24
Comorbidity, % (n)
0
1-2
>2
39.7 (23)
48.3 (28)
12.1 (7)
51.4 (298)
40.5 (235)
8.1 (47)
Primary sclerosing cholangitis,
% (n)
No
Yes
91.4 (53)
8.6 (5)
97.9 (568)
2.1 (12)
Smoking status, % (n)
Current
Ex-smokers
Never
Missing (n)
3.7 (2)
33.3 (18)
63.0 (34)
4
8.2 (46)
34.8 (195)
57.0 (320)
19
Disease Extent, % (n)
118
P-Value
<0.001
0.005
<0.001
0.178
0.014
0.513
Left-sided
Pancolitis
Missing (n)
27.8 (15)
72.2 (39)
4
21.7 (121)
78.3 (437)
22
Mesalamine/sulfasalazine†, %
(n)
No
Yes
Missing, (n)
36.4 (20)
63.6 (35)
3
26.25 (152)
73.75 (427)
1
Azathioprine/6mercaptopurine†, % (n)
No
Yes
Missing, (n)
90.9 (50)
9.1 (5)
3
72.9 (422)
27.1 (157)
1
y*- year, † Medication taken at admission or in-hospital
119
0.306
0.114
0.002
Table 4.2. Logistic regression results comparing colectomy patients’ characteristics
(colorectal dysplasia or cancer vs. medically refractory) with year of colectomy as
primary exposure.
Variables
Final model
Odds Ratio (95% CI)
Year
1.04 (0.96-1.14)
Age
18-34
35-64
≥ 65
Baseline
2.14 (0.96-4.77)
2.91 (0.99-8.59)
Gender
Female
Male
Baseline
2.55 (1.20-5.41)
Disease duration (years)
1.12 (1.09-1.15)
Disease extent
Pancolitis
Left sided
Baseline
1.85 (0.88-3.87)
Comorbidities
0
1-2
>2
Baseline
1.47 (0.73-2.97)
1.58 (0.50-4.96)
Primary sclerosing cholangitis
No
Yes
Baseline
2.03 (0.46-8.88)
Mesalamine/sulfasalazine
No
Yes
Baseline
0.43 (0.22-0.86)
Azathioprine/6-mercaptopurine
No
Yes
Baseline
0.33 (0.12-0.91)
120
Smoking
Current
Ex-smokers
Never
Baseline
0.96 (0.17-4.98)
1.36 (0.28-6.61)
121
13
11
9
.3 .6 .9 1.
2
7
5
Colectomies per 1,000 UC patients at risk
15
Figure 4.1. Annual incidence of colectomy for colorectal dysplasia or cancer (green
line), and medically refractory disease (red line) among UC patients living in the
Calgary Health Zone between 1997 and 2009. The graph presents annual adjusted
incidence of colectomy per 1,000 UC patients at risk*
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Year
Colorectal neoplasia
Medically refractory
*Adjusted for the UC prevalent population at risk of colectomy in Calgary, Canada.
122
4.7 Appendix 4.A Comorbidity classifications
Category
Coronary Artery Disease
Comorbidity
Coronary artery disease, Ischemic heart disease, Myocardial
infarction, Peripheral vascular disease
Cancer
Lymphoma, Metastatic tumor, Solid tumor without metastases
Other cardiovascular
Cardiac arrhythmia, Valvular disorder
CHF
Congestive heart failure
Diabetes
Diabetes with complications, Diabetes without complications
Venous Thromboembolism
Deep vein thrombosis
GI
CMV infection, Pancreatitis, Peptic ulcer disease
Haematological
Blood loss anaemia, Coagulopathy, Cyclical neutropenia,
Deficiency anaemia
HTN
Liver
Hypertension
Fatty liver
Neurological
Cerebrovascular disease, Hemiplegia and paraplegia
Pulmonary
Renal
Asthma, COPD, Sarcoidosis
Renal failure (acute or chronic)
Rheumatoid
Ankylosing spondylitis, Episcleritis, Uveitis and iritis, Gout,
Sacroiliitis, Rheumatoid arthritis
123
Chapter Five: Colorectal cancer surveillance in patients with inflammatory bowel
disease and primary sclerosing cholangitis: An economic evaluation
124
5.1 Abstract
The cost-effectiveness of annual colonoscopy for detection of colorectal neoplasia
among patients with inflammatory bowel disease (IBD) and primary sclerosing
cholangitis (PSC) is uncertain. The aim of this study was to determine whether annual
colonoscopy among patients with IBD-PSC is cost-effective compared to less frequent
intervals from a publicly funded health care system perspective. A cost-utility analysis
using a Markov model was used to simulate a 35 year-old patient with a 10-year history
of well-controlled IBD and a recent diagnosis of concomitant PSC. The following
strategies were compared: no surveillance, colonoscopy every 5 years, biennial
colonoscopy and annual colonoscopy. Outcome measures included: costs, number of
cases of dysplasia found, number of cancers found and missed, deaths, quality-adjusted
life-years (QALYs) gained and the incremental cost per QALY gained. In the base-case
analysis, no surveillance was the least expensive and least effective strategy. Compared
to no surveillance, the cost per QALY of surveillance every 5 years was CAN $15,021.
The cost per QALY of biennial surveillance compared to surveillance every 5 years was
CAN $37,522. Annual surveillance was more effective than biennial surveillance, but at
an incremental cost of CAN $174,650 per QALY gained compared to biennial
surveillance. More frequent colonoscopy screening intervals improves effectiveness (i.e.
detects more cancers and prevents additional deaths), but at higher cost. Health systems
must consider the opportunity costs associated with different surveillance colonoscopy
intervals when deciding which strategy to implement among patients with IBD-PSC.
125
5.2 Introduction
Colorectal cancer (CRC) is the third most common type of cancer and accounts
for 12% of cancer related deaths among the general population.1 Compared to the general
population, patients with inflammatory bowel disease (IBD) are at increased risk of
developing CRC. Risk factors associated with development of CRC among IBD patients
include longer duration of disease, greater disease extent, familial history of CRC,
chronic active inflammation and being concurrently diagnosed with primary sclerosing
cholangitis (PSC).2
Primary sclerosing cholangitis is a chronic cholestatic liver disease, characterized
by chronic inflammation, and destruction and fibrosis of the intrahepatic and/or
extrahepatic biliary tree.3 Approximately 68% of PSC patients will also have a diagnosis
of IBD.4 A recent population-based study concluded that the risk of developing CRC
among IBD-PSC patients is 10 times higher and their diagnosis of cancer/dysplasia
occurs at a younger age when compared to the general IBD population.5 In addition,
cancers diagnosed among IBD-PSC patients are more often located in the right colon
with an overall worse prognosis.6
Contemporary guidelines for the management of IBD patients recommend
surveillance colonoscopy every 1-2 years, 8-10 years after diagnosis.7,8 In contrast,
annual colonoscopy is recommended for IBD-PSC patients immediately after both
diseases are concurrently recognized.7,8 However, the implications of annual surveillance
among IBD-PSC patients on the number of cancer cases detected, CRC-related mortality
as well as the cost-effectiveness of this strategy is unknown. Thus, the objective of this
126
study was to perform a cost-utility analysis to determine whether annual colonoscopy
surveillance is cost-effective compared to less frequent surveillance intervals in IBD
patients with new concurrent PSC.
5.3 Materials and Methods
5.3.1 Model overview
An incremental cost-utility analysis was performed from the perspective of the
publicly funded health care system according to contemporary guidelines.9 Our
hypothetical population consisted of young to middle-aged patients (35 years on average)
with a 10-year history of well-controlled IBD, recently diagnosed with PSC. Both IBD
and PSC are lifelong diseases with liver transplant being the only curative option for
advanced PSC. Furthermore, the risk of CRC persists following liver transplantation
among patients with an intact colon.10 Therefore, we considered the following CRC
screening strategies: no screening, annual colonoscopy, biennial colonoscopy, and
colonoscopy performed every 5 years over a lifetime horizon.
The Markov model was constructed using decision analysis software (TreeAge
Pro 2014, USA). A concise version depicting the flow of the model is presented in figure
5.1. All patients had compensated liver disease at the start of the model. As the model
progressed, patients were at risk of developing decompensated disease requiring liver
transplantation. During each year of the model (one-year cycle length), patients with
IBD-PSC with or without dysplasia or CRC could either remain in the same health state,
progress to another health state or die. Cancers were modeled to arise through both a
dysplasia-carcinoma sequence and through a faster pathway without any prior identifiable
127
dysplastic lesion. Dysplasia or CRC could be diagnosed during surveillance colonoscopy,
as part of the liver transplant workup when they developed decompensated liver disease
or when CRCs became clinically manifest in those missed by surveillance colonoscopy.
Patients who were noted during surveillance to have dysplasia or CRC were treated with
colectomy. Consistent with liver transplant guidelines, a history of any extra-hepatic
malignancy within the previous 5 years was considered a contraindication to
transplantation. Please refer to Appendix 5.A for a list of the health states included in our
model.
5.3.2 Model assumptions
The model structure assumed the following: 1) All patients had well-controlled
colitis in order to focus on the impact of surveillance rather than diagnosing and
managing flares. 2) We assumed 100% compliance in the base case across screening
strategies.11,12 3) Consistent with the literature, those diagnosed with CRC during a
surveillance colonoscopy had improved survival compared to patients presenting with a
missed cancer during colonoscopy or in the context of no program. This differential
survival was on the basis of a more favourable CRC stage distribution (i.e., more early
stage cancers) at diagnosis during surveillance with lower treatments costs.13-18 4) Based
on expert opinion, and consistent with other decision analyses, we assumed that cancers
missed during colonoscopy (or due to no colonoscopy performed in the natural history
arm) became clinically manifest and were diagnosed within a median time of two years.19
5) Consistent with clinical practice, some patients who underwent colectomy were
subsequently noted to not have dysplasia (i.e. false-positive test results). 6) Postcolectomy CRC patients alive after 5 years were considered to be cured of their cancer. 7)
128
All patients that had surgery because of dysplasia or CRC underwent a total colectomy
because of the high risk of pouchitis among IBD-PSC patients.20 8) We assumed a lower
risk of recurrent PSC and graft loss among those with a pre-/peri-liver transplant
colectomy compared to those with an intact colon after liver transplant.21 9) We assumed
that there was a one-year delay on average between the indication of liver transplant and
receipt of the transplant. Based on expert opinion, only one-third of patients requiring a
liver transplant survived and successfully had their procedure. 10) The mortality risk
among IBD-PSC patients is 3 times higher than the general IBD population,22 the latter of
which is equivalent to the general population without IBD.23 These assumptions were
applied across all strategies in the model and were varied in sensitivity analyses.
5.3.3 Model validation
A previously validated and published model was adapted for this study.16 First,
clinical specialists in IBD and PSC carefully reviewed the structure and flow of the
decision tree to ensure the face validity of the model. Following this, a series of
“debugging” exercises and Markov cohort analyses were performed to identify and
eliminate all syntactical errors. Lastly, a Monte Carlo simulation was performed to
calculate the median time to liver transplant or death, the number of subjects who
developed dysplasia and CRC, and the number of colectomy and CRC-related deaths.
These numbers were used to calibrate the model against a recent IBD-PSC populationbased study to ensure that the number of cancers developed in the model closely
approximated those reported in the study.5
129
5.3.4 Parameters
MEDLINE was used to retrieve all relevant studies published on CRC, IBD and
PSC and additional articles were identified from originally retrieved studies. Expert
opinion was used in circumstances were data was not available. A list of the base case
model inputs and ranges considered is presented in Table 1.
5.3.4.1 Dysplasia and cancer related risk
The cumulative 10-year risks of dysplasia and CRC from the general IBD
population were used to calculate the baseline prevalence of dysplasia and CRC at the
time of concurrent IBD-PSC diagnosis (year 0 of the model).24,25 Thereafter, the
incidence of colonic neoplasia was derived by pooling data from previous IBD-PSC
studies that enabled us to calculate the incidence rate upon coexistence of both IBD and
PSC.11,26-28 A weighted average of the annual probability of developing neoplasia and the
proportion of dysplasia and CRC cases was then calculated. The rate of transition from
dysplasia to CRC is uncertain among IBD-PSC patients. As such, the transition rate from
dysplasia to CRC was estimated from follow-up studies in the general IBD population.29
5.3.4.2 Median time to liver transplant and graft loss
In the base case the median time to liver transplant was considered to be 13
years.5 The annual risk of graft loss was calculated directly from studies describing the
rate of graft loss among IBD-PSC patients.30
5.3.4.3 Mortality
The annual risk of death varied according to the clinical state. Life tables and
published literature on mortality of IBD-PSC patients were used to obtain the annual
background mortality risk.31 The risk of death due to colectomy was derived from a
130
general IBD population-based study.32,33 The 5-year CRC mortality according to cancer
stage was obtained from the literature and a weighted average was calculated based on
cancer stage distribution among IBD patients under a CRC surveillance program versus
no surveillance program.15,17,18 Consistent with the literature, those diagnosed with CRC
under a surveillance program had improved survival over patients presenting with
symptomatic cancer, on the basis of a more favourable CRC stage distribution (i.e., more
early stage cancers) at diagnosis.15,17,18 Similar to other economic analyses, mortality
from colonoscopy was not considered, as this represents a negligible risk.19 Patient
survival following liver transplant was derived from the IBD-PSC literature.21
5.3.4.4 Colonoscopy performance characteristics.
Colonoscopy is the most widely used modality for diagnosing dysplasia and
cancer. Colonoscopy test performance characteristics were based on 33 biopsy samples as
recommended by CRC surveillance guidelines.7,8,34-36
5.3.4.5 Costs
All costs are reported in 2012 CAN$. Direct costs of colonoscopy and colectomy
were derived from published estimates within the Calgary Health Zone.11,16,37,38
Colonoscopy costs included: physician and non-physician costs (i.e. capital, nursing,
drugs) as well as biopsy costs and pathology fees.16 Nonmedical costs (patient +/caregiver time and travel costs) were also included according to guidelines and were
obtained from the literature.9,16
The costs of managing CRC included surgery and chemotherapy treatment costs
and were obtained from published studies.16 The average cancer stage costs were used to
131
calculate weighted average costs for overall cancer management based on cancer stage
distribution.16-18
The total cost of liver transplantation was taken from a report that estimated the
medical costs during the 30-day period prior to the transplant, graft procurement, hospital
admission for the transplant including physician costs, post-discharge facility and nonfacility services, and the mean costs resulting from hospital readmissions and other
evaluations and treatments during the first year post-transplant.39
All future costs and health benefits were discounted at 5% annually.40
5.3.4.6 Utilities
Utilities for most of the health states were obtained from studies among the
general IBD population because utilities for patients with IBD-PSC are unknown.41,42
When available, studies reporting utilities using a standard gamble exercise were used.
Weighted averages for cancer utilities were calculated based on cancer stage distribution
among IBD patients under a CRC surveillance program versus patients under no
surveillance program.17,18,43 Utilities post liver transplant were obtained from studies
looking at post liver transplant health-related quality of life among all liver transplant
patients.44,45
5.3.5 Analyses
Incremental analyses (expressed as the cost per QALY gained) were performed
by rank ordering all competing strategies by increasing cost after eliminating strategies
that were more costly and less effective (i.e. dominated). Outcome measures included:
costs, number of cases of dysplasia found, number of cancers found and missed, deaths,
quality-adjusted life-years (QALYs) gained and the incremental cost per QALY gained.
132
Multiple one-way sensitivity analyses were performed in order to assess the
extent to which the model results were affected by each parameter independently.
We performed a number of scenario analyses in order to evaluate the impact of
adherence to screening, median time to liver transplant, and adherence with biopsy
protocol guidelines during colonoscopy. We performed 3 scenario analyses taking into
consideration a surveillance program adherence rate of 25%, 50% and 75%, recognizing
that compliance rates to CRC screening programs can vary. Taking into consideration a
potential selection bias from median time to liver transplant reports, a scenario analysis
evaluated the potential impact of extending the median time to liver transplant to 21 years
based on a recent population based study.5. Finally, a scenario analysis was performed to
assess the impact of taking only 18 biopsy samples during surveillance colonoscopies.
For this we lowered the sensitivity for dysplasia and CRC to 60% and 85%, respectively.
A probabilistic sensitivity analysis (PSA) was performed to take into account the
overall model uncertainty by simultaneously varying all the model parameter estimates
within their range of uncertainty. The PSA incorporated 100,000 trials (iterations), and
statistical distributions were created around all parameters. The type of distribution and
values used are presented in Table 5.1. When possible, base case values were taken from
the median or mean of published data and the 95% confidence intervals were used as the
lower and upper values. Beta distributions were used for probabilities and utilities.
Gamma or normal distributions were used for costs. Triangular distributions were used in
cases where expert opinion was used. The sensitivity and specificity of colonoscopy was
not considered for the PSA because these values are correlated and we did not have
access to the receiver operator curves. PSA results are presented using incremental cost133
effectiveness scatterplots with 95% confidence ellipses. For this, each surveillance
strategy was compared to the next most expensive strategy, and the incremental cost and
effectiveness for each of the iterations was plotted in the cost-effectiveness plane. Also,
the probability that each intervention was cost-effective at multiple willingness-to-pay
(WTP) thresholds was calculated and presented on a cost-effectiveness acceptability
curve graph.
5.4 Results
5.4.1 Base-case analysis
For every 1,000 IBD-PSC patients followed over a lifetime, 255 cases of CRC
and 58 CRC related deaths would be expected with no surveillance at a cost of $101,663
per patient (Tables 5.2 & 5.3). When compared to no surveillance, the number of CRC
deaths could be reduced by 50% with surveillance every 5 years, 69% with biennial
colonoscopy and 81% with annual colonoscopy (Table 5.2). Under no surveillance, 255
dysplasia cases would be expected and 178 of them would have progressed to cancer
(Table 5.2). In contrast, with surveillance every 5 years, biennial and annual colonoscopy
surveillance the number of dysplasia cases that progressed to cancer could be reduced by
64%, 78% and 95%, respectively (Table 5.2).
Considering a lifetime horizon, the cost per patient was estimated to be $101,663,
$104,517, $107,894 and $114,880 for no surveillance, surveillance every 5 years,
biennial surveillance and annual surveillance respectively (Table 5.3). As expected, no
surveillance was the least costly, but also the least effective strategy. Colonoscopy every
5 years was the least costly screening strategy, but also generated the fewest QALYs at a
134
cost of $15,021 per QALY gained compared to no screening (Table 5.3). Compared to
surveillance every 5 years, biennial surveillance was more costly and generated more
QALYs at a cost of $37,522 per QALY gained (Table 5.3). Annual colonoscopy
produced additional QALYs, but at a cost of $174,650 per QALY gained compared to
biennial colonoscopy (Table 5.3).
5.4.2 Sensitivity analyses
All the one-way sensitivity analysis yielded similar rakings as the base case. No
surveillance remained the least expensive and effective strategy followed by surveillance
every 5 years, biennial and annual surveillance. As expected, as the annual incidence of
neoplasia increases, the cost per QALY of all the surveillance strategies decreases. For
example, when the annual incidence of neoplasia was 3.3% (the upper range of our
estimate) the cost per QALY of biennial and annual surveillance decreased to $28,499
and $125,985, respectively. However, under all circumstances, the ICER of annual
surveillance remained well above $100,000/QALY.
Adherence to surveillance influenced the cost per QALY of all surveillance
strategies. When we assumed a 25% adherence to the surveillance program, biennial
surveillance was eliminated due to extended dominance and annual surveillance was the
most effective strategy at a cost per QALY of $19,950 (Table 5.4). Assuming a 50%
adherence to the surveillance program, the cost per QALY for the biennial surveillance
program was similar to the base case but the cost per QALY for the annual surveillance
program decreased to $80,616 (Table 5.4). Compared to the base case, the cost per
QALY for biennial surveillance decreased to $21,836 assuming a 75% adherence rate.
135
However, with 75% adherence, the cost per QALY of annual surveillance remained
similar to the base case results ($180,300).
The cost per QALY for biennial surveillance was similar to the base case when
time to liver transplant was extended to 21 years ($34,025 per QALY compared to
colonoscopy every 5 years). However, the cost per QALY of annual surveillance
increased significantly to $403,300 compared to biennial surveillance (Table 5.4).
Lowering colonoscopy performance by decreasing the number of tissue biopsies
obtained led to an increase of the cost per QALY of biennial surveillance ($59,871) and
to a decrease of the cost per QALY of annual surveillance ($101,114) (Table 5.4).
5.4.3 Probabilistic sensitivity analysis
Figure 5.2 presents the incremental cost-effectiveness scatterplot comparing
annual surveillance to biennial surveillance. When we compared annual surveillance to
biennial surveillance, 94% of the iteration results were located in the top right quadrant
indicating that annual surveillance is more effective, but also more costly than biennial
surveillance. However, in 98% of the iterations, annual surveillance was not
recommended because the incremental cost-effectiveness ratio was above the WTP
threshold of $50,000 per QALY.
When we compared biennial surveillance to surveillance every 5 years, 98% of
the iteration results were located on the top right quadrant meaning that biennial
surveillance is more costly and more effective than surveillance every 5 years. In 57% of
the iterations, the incremental cost-effectiveness ratio for biennial surveillance was above
the generally accepted WTP threshold of $50,000 per QALY.
136
The cost-effectiveness acceptability curve shows that at a WTP threshold of
$50,000, biennial surveillance was likely to be cost-effective ~40% of the time while
annual surveillance was likely to be cost-effective only ~2% of the time. Surveillance
every 5 years is the most likely strategy to be cost-effective with a probability of ~55%
(Figure 5.3). Only at a WTP of $175,000, annual surveillance is likely to be cost-effective
50% of the time.
5.5 Discussion
This is the first cost-utility analysis to evaluate different CRC surveillance
intervals among IBD-PSC patients. Current CRC screening guidelines recommend annual
surveillance colonoscopy for IBD-PSC patients based only on existing observational data
that highlights the elevated CRC risk among these patients.7,8 Our study demonstrates
that, for a typical IBD-PSC patient with stable colitis and non-decompensated liver
disease, biennial surveillance was associated with a cost per QALY of $37,522 compared
to surveillance every 5 years. Biennial colonoscopy would reduce the number of cancer
cases and cancer-related deaths by 61% and 69%, respectively, compared to no
surveillance. Annual surveillance could prevent an additional 19% of cancer cases and
29% cancer-related deaths compared to biennial screening, but at an additional average
cost of $6,986 per patient or $174,650 per QALY. This information can be used by
clinicians and healthcare administrators to plan surveillance programs; particularly, in
recognition of the evolving incidence and risk of surgery for IBD in the 21st Century.46,47
Cost-effectiveness studies are often presented using a generally accepted WTP
threshold of $50,000/QALY (in the USA) or between £20,000-£30,000 (in the UK).
137
These WTP thresholds have been used to facilitate decision-making for the costeffectiveness of medical technologies and health interventions despite of not being
endorsed by the Panel on Cost-Effectiveness in Health and Medicine or NICE (UK).48,49
Some authors suggest that the use of these thresholds may have limited generalizability
for severe/chronic diseases and propose a WTP of $100,000/QALY as a less conservative
approach reflecting inflation and increased health care costs.50 It should be noted that
health care interventions are still being implemented despite being well above a WTP of
$50,000/QALY; for example, the use of biologics in IBD patients.51,52 In our study,
biennial surveillance was a reasonable strategy using both WTP thresholds of
$50,000/QALY and $100,000/QALY.
This model takes into consideration the progression of liver disease among this
patient population. The development of end stage liver disease and subsequent high
mortality limits the benefits of CRC prevention through surveillance. In addition, our
model suggests that approximately 30% of dysplasia cases missed during surveillance
colonoscopy are diagnosed during the liver transplant workup. Consequently, we
observed little difference between the effectiveness of biennial and annual surveillance
under the base case and under most of the scenario analyses with the exception of 25%
compliance rate and suboptimal surveillance biopsy protocol practices.
This study has several important strengths. First, we used a recently published
population-based study that described the natural history of CRC among IBD-PSC
patients to validate our model.5 This external validation process increases the confidence
that our model resembles real life natural history with respect to the number of CRC
cases developed and the median time to liver transplant or death. Second, our model
138
represents the average IBD-PSC patient with a median time to liver transplant of 13
years. However, we performed a scenario analysis extending time to liver transplant to 21
years. This provides further insight on the potential impact of surveillance among the
IBD-PSC population outside of large referral or liver transplantation centers.5 Fourth, this
study highlights the importance of standardizing the follow-up period and reporting of
dysplasia and CRC incidence. Finally, our study has identified important knowledge gaps
in the field that should be addressed to improve patient care.
However, several limitations to this study should be considered. First, this study
was limited by the data available for IBD-PSC patients. Some parameters of our model
are not well characterized (e.g. incidence of neoplasia/dysplasia/CRC, distinguishing
between indefinite, low or high grade dysplasia) or not described at all (e.g. probability of
progression from dysplasia to CRC and utilities) in the IBD-PSC population.
Heterogeneity was observed between studies that report dysplasia and cancer incidence
rates among IBD-PSC patients. Some studies report incidence rates of neoplasia while
others report incidence rates that distinguish between dysplasia and cancer. Furthermore,
the start of the follow-up period varies among studies. Some studies use age at diagnosis
of IBD as the beginning of the follow up period, while others use the age at diagnosis of
PSC or age at coexistence of both diseases. Also, lack of adherence to not only the
recommended screening intervals, but also the recommended 33 tissue biopsies per
colonoscopy would likely lead to underestimating the incidence rate of dysplasia and/or
CRC.11 Further, the diagnosis of indefinite dysplasia, low or high grade dysplasia will
lead to different clinical outcomes. However, the data differentiating practice between
grades of dysplasia is limited and thus, we only modeled dysplasia as a single entity. In
139
addition, the probability of progression from dysplasia to cancer was extrapolated from
IBD studies due to the small sample size and short duration of patient follow-up in IBDPSC studies.53 Health related utilities were extrapolated from the IBD population. Thus,
extrapolating some of the probabilities, utilities and costs from the general IBD
population most likely underestimated the benefit of the surveillance strategies being
evaluated. However, we presented a series of sensitivity and scenario analyses
demonstrating the impact of the uncertainty around these parameters. Second, this costutility model was based on the traditional random biopsy driven colonoscopy surveillance
practice that most community gastroenterologists currently apply. Novel techniques that
may improve the sensitivity of identifying colonic dysplasia are available in certain
academic
centers
including
mucosal
dye
spray
during
colonoscopy
(i.e.
chromoendoscopy) and confocal endomicroscopy.2 While these techniques may improve
the detection rates of dysplasia and early CRC, they are more time consuming, require
additional training, and the costs to integrate these modalities into an endoscopy unit are
higher. Third, our model assumed that the IBD disease status was stable. We did not
factor in additional colonoscopies or the added IBD related costs (e.g. medications and
surgery) during disease flares. Thus, CRC diagnosed during a flare would not have been
accounted for in our model.11 However, this bias is minimized because surveillance
colonoscopies are predominantly completed when IBD is in remission. Further, the
impact of additional colonoscopies would be the same across strategies and thus would
not have affected our conclusions. Finally, our model, did not account for the rare
occurrence of neoplasia in patients with an ileal pouch-anal anastomosis.54,55
140
Based on the best available data, this cost-utility model demonstrated that more
frequent surveillance improves effectiveness (detects additional cancers earlier and
improves survival) at a higher cost. Healthcare systems might choose different intervals
depending on what is affordable. Recognizing that the model was influenced by
uncertainty in key parameters, future studies that address these knowledge gaps would
aid in providing more robust estimates of cost-effectiveness for decision makers.
141
5.6 References
1.
Canadian Cancer Society's Advisory Committee on Cancer Statistics. Canadian
Cancer Statistics 2012. Toronto, ON: Canadian Cancer Society; 2012.
2.
Collins PD, Mpofu C, Watson AJ, et al. Strategies for detecting colon cancer
and/or dysplasia in patiens with inflammatory bowel disease. Cochrane Database
Syst Rev 2006:CD000279.
3.
Kaplan GG, Laupland KB, Butzner D, et al. The burden of large and small duct
primary sclerosing cholangitis in adults and children: a population-based analysis.
Am J Gastroenterol 2007;102:1042-9.
4.
Molodecky NA, Kareemi H, Parab R, et al. Incidence of primary sclerosing
cholangitis: a systematic review and meta-analysis. Hepatology 2011;53:1590-9.
5.
Boonstra K, Weersma RK, van Erpecum KJ, et al. Population-based
epidemiology, malignancy risk and outcome of primary sclerosing cholangitis.
Hepatology 2013;58:2045-55.
6.
Claessen MM, Lutgens MW, van Buuren HR, et al. More right-sided IBDassociated colorectal cancer in patients with primary sclerosing cholangitis.
Inflamm Bowel Dis 2009;15:1331-6.
7.
Kornbluth A, Sachar DB. Ulcerative colitis practice guidelines in adults:
American College Of Gastroenterology, Practice Parameters Committee. Am J
Gastroenterol 2010;105:501-23.
8.
Farraye FA, Odze RD, Eaden J, et al. AGA medical position statement on the
diagnosis and management of colorectal neoplasia in inflammatory bowel disease.
Gastroenterology 2010;138:738-45.
142
9.
Guidelines for the economic evaluation of health technologies: Canada [3rd
edition ed] Ottawa, Canadian Agency for Drugs and Technologies in Health;
2006.
10.
Singh S, Varayil JE, Loftus EV Jr., et al. Incidence of colorectal cancer after liver
transplantation for primary sclerosing cholangitis: a systematic review and metaanalysis. Liver Transpl 2013;19:1361-9.
11.
Kaplan GG, Heitman SJ, Hilsden RJ, et al. Population-based analysis of practices
and costs of surveillance for colonic dysplasia in patients with primary sclerosing
cholangitis and colitis. Inflamm Bowel Dis 2007;13:1401-7.
12.
Kottachchi D, Yung D, Marshall JK. Adherence to guidelines for surveillance
colonoscopy in patients with ulcerative colitis at a Canadian quaternary care
hospital. Can J Gastroenterol 2009;23:613-6.
13.
McArdle CS, Hole DJ. Emergency presentation of colorectal cancer is associated
with poor 5-year survival. Br J Surg 2004;91:605-9.
14.
Amri R, Bordeianou LG, Sylla P, et al. Impact of screening colonoscopy on
outcomes in colon cancer surgery. JAMA Surg 2013:1-7.
15.
O'Connell JB, Maggard MA, Ko CY. Colon cancer survival rates with the new
American Joint Committee on Cancer sixth edition staging. J Nat Cancer Inst
2004;96:1420-5.
16.
Heitman SJ, Hilsden RJ, Au F, et al. Colorectal cancer screening for average-risk
North Americans: an economic evaluation. PLoS One 2010;7:e1000370.
143
17.
Choi PM, Nugent FW, Schoetz DJ, et al. Colonoscopic surveillance reduces
mortality from colorectal cancer in ulcerative colitis. Gastroenterology
1993;105:418-24.
18.
Lutgens MW, Oldenburg B, Siersema PD, et al. Colonoscopic surveillance
improves survival after colorectal cancer diagnosis in inflammatory bowel
disease. Br J Cancer 2009;101:1671-5.
19.
Nguyen GC, Frick KD, Dassopoulos T. Medical decision analysis for the
management of unifocal, flat, low-grade dysplasia in ulcerative colitis.
Gastrointest Endosc 2009;69:1299-310.
20.
Gorgun E, Remzi FH, Manilich E, et al. Surgical outcome in patients with
primary sclerosing cholangitis undergoing ileal pouch-anal anastomosis: a casecontrol study. Surgery 2005;138:631-9.
21.
Alabraba E, Nightingale P, Gunson B, et al. A re-evaluation of the risk factors for
the recurrence of primary sclerosing cholangitis in liver allografts. Liver Transpl
2009;15:330-40.
22.
Ananthakrishnan AN, Cagan A, Gainer VS, et al. Mortality and extraintestinal
cancers in patients with primary sclerosing cholangitis and inflammatory bowel
disease. J Crohns Colitis 2014; http://dx.doi.org/10.1016/j.crohns.2014.01.019.
23.
Jess T, Loftus EV Jr., Harmsen WS, et al. Survival and cause specific mortality in
patients with inflammatory bowel disease: a long term outcome study in Olmsted
County, Minnesota, 1940-2004. Gut 2006;55:1248-54.
24.
Delvin J, O'Grady J. Indications for referral and assessment in adult liver
transplantation: a clinical guideline. Gut 1999;45:Supp6:VII-VI22.
144
25.
Carrithers RL. Liver transplantation: AASLD practice guidelines. Liver Transpl
2000;6:122-35.
26.
Weinstein MC, O'Brien B, Hornberger J, et al. Principles of good practice for
decision analytic modeling in health-care evaluation: report of the ISPOR Task
Force on good research practices-modeling studies. Value Health 2003;6:9-17.
27.
Eaden JA, Abrams KR, Mayberry JF. The risk of colorectal cancer in ulcerative
colitis: a meta-analysis. Gut 2001;48:526-535.
28.
Thomas T, Abrams KA, Robinson RJ, et al. Meta-analysis: cancer risk of lowgrade dysplasia in chronic ulcerative colitis. Aliment Pharmacol Ther
2007;25:657-68.
29.
Gurbuz AK, Giardiello FM, Bayless TM. Colorectal neoplasia in patients with
ulcerative colitis and primary sclerosing cholangitis. Dis Colon Rectum
1995;38:37-41.
30.
Navaneethan U, Kochhar G, Venkatesh PG, et al. Duration and severity of
primary sclerosing cholangitis is not associated with risk of neoplastic changes in
the colon in patients with ulcerative colitis. Gastrointest Endosc 2012;75:104554.
31.
Broome U, Lofberg R, Veress B, et al. Primary sclerosing cholangitis and
ulcerative colitis: Evidence for increased neoplastic potential. Hepatology
1995;22:1401-08.
32.
Ullman T, Croog V, Harpaz N, et al. Progression of flat low-grade dysplasia to
advanced neoplasia in patients with ulcerative colitis. Gastroenterology
2003;125:1311-29.
145
33.
Rowe IA, Webb K, Gunson BK, et al. The impact of disease recurrence on graft
survival following liver transplantation: a single centre experience. Transpl Int
2008;21:459-65.
34.
Complete life table, Canada, 2000-2002: males. Ottawa, Canada: Statistics
Canada: 2006.
35.
de Silva S, Ma C, Proulx MC, et al. Postoperative complications and mortality
following colectomy for ulcerative colitis. Clin Gastroenterol Hepatol
2011;9:972-80.
36.
Kaplan GG, McCarthy EP, Ayanian JZ, et al. Impact of hospital volume on
postoperative morbidity and mortality following a colectomy for ulcerative colitis.
Gastroenterology 2008;134:680-7.
37.
Rubenstein JH, Waljee AK, Jeter JM, et al. Cost-effectiveness of ulcerative colitis
surveillance in the setting of 5-aminosalicylates. Am J Gastroenterol
2009;104:2222-32.
38.
Pickhard PJ, Hassan C, Halligan S, et al. Colorectal cancer: CT colonography and
colonoscopy for detection: Systematic review and meta-analysis. Radiology
2011;259:393-405.
39.
Rubin CE, Haggitt RC, Burmer GC, et al. DNA aneuploidy in colonic biopsies
predicts future development of dysplasia in ulcerative colitis. Gastroenterology
1992;103:1611-20.
40.
Heitman SJ, Au F, Manns BJ, et al. Nonmedical costs of colorectal cancer
screening with the fecal occult blood test and colonoscopy. Clin Gastroenterol
Hepatol 2008;6:912-17.
146
41.
Coward S, Heitman S, Hubbard J, et al. Ulcerative colitis associated
hospitalization costs: A population-based study, In: Canadian Digestive Diseases
Week, 2012; Victoria, British Columbia.
42.
Milliman research report: 2008 U.S. organ and tissue transplant cost estimates and
discussion.Milliman Inc., Brookfield, WI, USA. 2008
43.
Poole CD, Connolly MP, Nielsen SK, et al. A comparison of physician-rated
disease severity and patient reported outcomes in mild to moderately active
ulcerative colitis. J Crohn's Colitis 2010;4:275-82.
44.
Muir AJ, Edwards LJ, Sanders LL, et al. A prospective evaluation of healthrelated quality of life after ileal pouch anal anastomosis for ulcerative colitis. Am
J Gastroenterol 2001;96:1480-85.
45.
Ness RM, Holmes AM, Klein R, et al. Utility valuations for outcome states fo
colorectal cancer. Am J Gastroenterol 1999;94:1650-1657
46.
Longworth L, Bryan S. An empirical comparison of EQ-5D and SF-6D in liver
transplant patients. Health Econ 2003;12:1061-7.
47.
Younossi ZM, Boparai N, McCormick M, et al. Assessment of utilities and
health-related quality of life in patients with chronic liver disease. Am J
Gastroenterol 2001;96:579-83.
48.
Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of
the inflammatory bowel diseases with time, based on systematic review.
Gastroenterology 2012;142:46-54.
147
49.
Frolkis AD, Dykeman J, Negron ME, et al. Risk of surgery for inflammatory
bowel diseases has decreased over time: a systematic review and meta-analysis of
population-based studies. Gastroenterology 2013;145:996-1006.
50.
Rawlins MD, Culyer AJ. National Institute for Clinical Excellence and its value
judgements. BMJ 2004;329:224-7.
51.
Weinstein MC, Siegel JE, Gold MR, et al. Recommendations of the Panel on
Cost-Effectiveness in Health and Medicine. JAMA 1996;276:1253-8.
52.
Lee JK, Tang DH, Mollon L, et al. Cost-effectiveness of biological agents used in
ulcerative colitis. Best Pract Res Clin Gastroenterol 2013;27:949-60.
53.
Park KT, Bass D. Inflammatory bowel disease-attributable costs and costeffective strategies in the United States: a review. Inflamm Bowel Dis
2011;17:1603-9.
54.
Dretzke J, Edlin R, Round J, et al. A systematic review and economic evaluation
fo the use of tumour necrosis factor-alpha (TNF-α) inhibitors, adalimumab and
infliximab, for Crohn's disease. Health Technol Assess 2011;15(6)
doi: 10.3310/hta15060.
55.
Eaton JE, Smyrk TC, Imam M, et al. The fate of indefinite and low-grade
dysplasia in ulcerative colitis and primary sclerosing cholangitis colitis before and
after liver transplantation. Aliment Pharmacol Ther 2013;38:977-987
56.
Borjersson L, Willen R, Haboubi N, et al. The risk of dysplasia and cancer in the
ileal pouch mucosa after restorative proctocolectomy for ulcerative proctocolitis
is low: A long-term follow-up study. Colorectal Dis 2004;6:494-8.
148
57.
Thompson–Fawcett MW, Marcus V, Redston M, et al. Risk of dysplasia in longterm ileal pouches and pouches with chronic pouchitis. Gastroenterology
2001;121:275-81.
149
Table 5.1. Base-case parameter estimates and distributions and ranges used in the probabilistic sensitivity analysis
Variables
Colonoscopy
Sensitivity for detecting dysplasia
Sensitivity for detecting cancer
Specificity
Baseline prevalence of dysplasia
Baseline prevalence of cancer
Incidence of neoplasia
Proportion of dysplasia cases
Transition from dysplasia to cancer
Probability of cancer becoming
symptomatic
Probability of developing decompensated
liver disease
Probability of graft loss (pre-/peritransplant colectomy)
Probability of graft loss (intact colon)
Baseline mortality
Mortality associated with cancer
Missed cancer‡
Early diagnosed cancer§
Mortality from colectomy
Mortality of end stage liver disease°
Mortality during first year of liver
transplant
Mortality after first year of liver transplant
Base-case
analysis
Distribution Range
Reference
0.90
0.95
0.999
0.021
0.002
0.023/yr
0.75
0.14/yr
0.6/yr
N/A
N/A
N/A
Beta
Beta
Triangular
Triangular
Beta
Triangular
(0.58-1.0)
(0.81-1.0)
(0.992-1.0)
(0.015-0.03)
(0.001-0.002)
(0.013-0.033)
(0.5-1.0)
(0.06-0.32)
(0.2-1.0)
37,39
0.0385/yr
Beta
(0.024-0.0385)
5
0
Beta
(0-0.15)
21
0.18/yr
Beta
(0.15-0.22)
(0.0011-0.00654)
21
0.09/yr
0.05/yr
0.015
0.70
0.20
Beta
Beta
Beta
Triangular
Beta
(0.02-0.4)
(0.02-0.4)
(0.005-0.023)
15,17,18
(0.159-0.2)
33
0.03/yr
Beta
(0.02-0.03)
33
150
38
38
28
27
11,29-31
11,29-31
32
EO*
22,34
15, 17,18
35,36
EO*
Costs (CAD)†
Cost of colonoscopy
Cost colonoscopy (medical)
Cost colonoscopy (non-medical)
Biopsy cost (per biopsy)
Pathologist fee
Cost of surgery
Cost of cancer
Missed cancer‡
Early diagnosed cancer§
Cost of liver transplant
Cost of pre-liver transplant workup
Utilities
Baseline
Following surgery no cancer
Dysplasia
Cancer
Missed cancer‡
Early diagnosed cancer§
First year of liver transplant
After first year of liver transplant
Discount rate
Time horizon
Mean (Range)
11,16,40
913
308
9
186
25,000
Gamma
Gamma
Gamma
Gamma
Gamma
913 (532 – 1,278)
328 (212 -479)
9 (7 - 18)
186 (175 - 372)
25,000 (15,000 - 50,000)
73,933
51,617
585,774
25,781
Gamma
Gamma
Normal
Normal
73,933 (26,688 – 142,784)
51,617 (26,688 – 142,784)
585,774 (+/- SD 73,222)
25,781 (+/- SD 3,222)
0.94
0.93
0.74
Beta
Beta
Beta
Range
(0.89-1.0)
(0.76-1.0)
(0.69-0.78)
0.58
0.67
0.62
0.84
5%
lifetime
Beta
Beta
Beta
Beta
(0.25-0.74)
(0.25-0.74)
(0.57-0.65)
(0.70-0.90)
41
,EO*
16-18,41
42
42
43
44
45
17,18,45
47
46
*EO- expert opinion
‡
Cancers missed during colonoscopy or because no colonoscopy was performed (in the case of the natural history arm, surveillance every 5 years and biennial
surveillance)
§
Cancers diagnosed at colonoscopy within a year
° Patients who developed decompensated disease and died prior undergoing the liver transplant
†
Cost were rounded to the nearest CAN$
151
Table 5.2. The number of patients who developed dysplasia and cancer, as well as the number of colonoscopies
performed over a lifetime horizon for a hypothetical 1,000 average IBD-PSC patient cohort
No surveillance
Dysplasia cases overall
Diagnosed
During surveillance
During LT* workup
Progressed to cancer
Other†
255
Surveillance every
5 years
255
Biennial surveillance
253
Annual
surveillance
252
0
56
178
21
158
28
63
6
212
17
22
2
236
13
3
0
Cancer cases overall
Diagnosed
During surveillance
During liver transplant
Symptomatic
Other‡
256
141
101
82
0
26
223
7
48
12
79
2
66
27
7
1
74
5
3
0
Cancer related deaths
Number of surveillance
colonoscopies
58
0
29
2,349
18
6,289
11
12,738
* Liver transplant
†
Dysplasia cases that died due to background mortality or decompensated liver disease by the end of the model remained undiagnosed
‡
Cancer cases that died due to background mortality or decompensated liver disease by the end of the model remained undiagnosed
152
Table 5.3. Base case-analysis results (in CAN$) for cost-utility analysis
Strategy
Average cost* (95%
CI) †
Average
effectiveness (95%
CI) †
Incremental
cost*
Incremental
Incremental
cost/effectiveness
effectiveness*
ratio*
No surveillance
101,663 (100,477102,849)
9.84 (9.81-9.88)
Surveillance every 5
years
104,517 (103,336105,687)
2,854
10.03 (10.00-10.06)
0.19
15,021
Biennial
surveillance‡
107,894 (106,713109,074)
3,377
10.12 (10.09-10.15)
0.09
37,522
Annual surveillance
114,880 (113,698116,061)
6,986
10.16 (10.13-10.19)
0.04
174,650
* Compared to the next most costly strategy
†
95% confidence interval
153
Table 5.4. Scenario-analysis results (in CAN$) for cost-utility analysis
Strategy cost
(CAN$)
Effectiveness
Incremental
cost/effectiveness
ratio*
Compliance 25%
No surveillance
Surveillance every 5 years
Biennial surveillance
Annual surveillance
101,123
102,327
103,276
104,322
9.83
9.94
9.98
10.04
10,945
extended dominance†
19,950
Compliance 50%
No surveillance
Surveillance every 5 years
Biennial surveillance
Annual surveillance
99,733
103,074
105,048
109,885
9.83
9.98
10.03
10.09
22,273
39,480
80,616
Compliance 75%
No surveillance
Surveillance every 5 years
Biennial surveillance
Annual surveillance
101,278
104,287
106,689
112,098
9.83
9.99
10.10
10.13
18,806
21,836
180,300
Time to liver transplant 21
years
No Surveillance
Surveillance every 5 years
Biennial surveillance
Annual surveillance
78,750
80,521
84,604
92,670
11.28
11.48
11.60
11.62
8,855
34,025
403,300
Suboptimal surveillance‡
No surveillance
Surveillance every 5 years
Biennial surveillance
Annual surveillance
101,714
104,342
108,533
115,611
9.84
10.00
10.07
10.14
16,425
59,871
101,114
*Compared to the next most costly strategy
†
The dominated strategy is both less effective and less costly compared to a mix of two other strategies
‡
Suboptimal surveillance was defined as taking only 18 biopsy samples during surveillance colonoscopy. The
sensitivity of dysplasia and cancer was assumed to be 60 and 85% respectively
154
Figure 5.1. Bubble diagram summarizing the structure of the Markov model, flow of patients through the health states,
and the outcomes considered in this cost-utility analysis.
Colonoscopy screening
strategy
Target population
No surveillance
Annual surveillance
Biennial surveillance
Surveillance every 5 years
35 year-old male diagnosed with inflammatory bowel disease and primary
sclerosing cholangitis
No dysplasia/Cancer
Dysplasia
Outcomes
Colectomy
Death
155
Invasive cancer
Figure 5.2. Incremental cost-effectiveness scatterplots with 95% confidence ellipses
for annual surveillance compared to biennial surveillance. The diagonal line
represents a willingness to pay threshold of CAN$50,000 per quality-adjusted lifeyear.
156
Figure 5.3. Cost-effectiveness acceptability curve.
157
5.7 Appendix 5.A Summary of health states included in the model
Health states
Prior to developing decompensated liver disease/undergoing liver transplant
1-No dysplasia
2- Missed dysplasia
3- Missed CRC
4- Post-colectomy dysplasia (true-positive/false-positive dysplasia)
5- Post-colectomy CRC (diagnosed during surveillance)
6- Post-colectomy CRC (missed during surveillance)
After first liver transplant
1- Post-liver transplant, intact colon/no dysplasia
2- Post-liver transplant, intact colon/missed dysplasia
3- Post-liver transplant, intact colon/ missed CRC
4- Post-liver transplant, pre/peri-transplant colectomy
5- Post-liver transplant, no colon (previous colectomy for CRC)
6- Post-colectomy CRC (missed during surveillance)
Other
1- Death
158
Chapter Six: Discussion of work presented
159
6.1 Overview of main findings
The primary focus of this thesis was to understand the epidemiology of Clostridium
difficile infection and colorectal dysplasia/cancer in order to identify ways of decreasing
the risk of colectomy among UC patients. Chapters 2 and 3 established the effect of C.
difficile infections on short- and long-term risk of colectomy. In particular, the
cumulative risk of acquiring C. difficile infection after the diagnosis of UC was
established. Patients with UC who were diagnosed with C. difficile were shown to have
an increased risk of colectomy. Further, both Chapters 2 and 3 demonstrated that C.
difficile infections increased the risk of postoperative complications following colectomy
for UC. Chapter 4 demonstrated that the incidence of colectomy for colorectal dysplasia
and cancer has remained stable over time, and showed that the observed decrease in
colectomy rates is most likely due to a decrease in colectomies due to medically
refractory disease. Finally, Chapter 5 determined the cost-effectiveness of different
surveillance colonoscopy intervals for colorectal dysplasia among a subgroup of
inflammatory bowel disease (IBD) patients with a higher risk of developing colorectal
dysplasia and cancer; i.e., patients with IBD and primary sclerosing cholangitis (PSC).
The aims of the study presented in Chapter 2 were to determine if C. difficile
infection was associated with having an emergent colectomy and developing
postoperative complications in a population-based cohort of UC patients who were
admitted to Calgary Health Zone (CHZ) hospitals between 2000 and 2009. The medical
charts of these patients were reviewed to extract clinical data and Calgary Laboratory
Services (CLS) provided patients’ C. difficile tests results done in hospital and 90 days
160
prior to hospital admission. This study demonstrated that UC patients diagnosed with C.
difficile infection in hospital or 90 days prior to admission were more likely to be
unresponsive to rescue therapy in hospital and need an emergent colectomy (adjusted
odds ratio (OR)=3.39; 95% confidence interval (CI): 1.02-11.23). Additionally, a
preoperative diagnosis of C. difficile infection increased the odds of developing an
infectious postoperative complication (adjusted OR=4.76; 95% CI: 1.10-20.63).
However, a preoperative diagnosis of C. difficile infection was not significantly
associated with developing any postoperative complication (adjusted OR=3.16; 95% CI:
0.89-11.23).
The study presented in Chapter 3 was a population-based inception cohort of
patients from Alberta who were diagnosed with UC between 2003 and 2010. This cohort
was identified using Alberta Health administrative databases. The objectives of this study
were to 1) validate the International Classifications of Diseases (ICD-10) code for C.
difficile diagnosis among UC patients; 2) determine the 1-, 3-, and 5-year cumulative risk
of a C. difficile diagnosis after UC diagnosis; 3) determine the effect of a C. difficile
diagnosis on the risk of colectomy and mortality; and 4) determine the association
between a C. difficile diagnosis and developing postoperative complications. The
sensitivity, specificity, PPV and NPV of the ICD-10 diagnostic code of C. difficile
diagnosis were 82.1% (95% CI: 71.7-89.9%), 99.4% (95%CI: 99.1-99.7%), 88.4% (95%
CI: 82.9-92.3%), and 99.1% (95% CI: 98.5-99.4%), respectively. The cumulative risk of
C. difficile diagnosis at 1-, 3-, and 5-years after UC diagnosis was 1.6%, 2.5%, and 3.2%,
respectively. Patients with a C. difficile diagnosis were more likely to have colectomy
compared to those who had no prior C. difficile diagnosis (sub-hazard ratio (SHR)= 2.36;
161
(95% CI: 1.47-3.80) after adjusting for age, sex, and comorbidities. Additionally, patients
with a C. difficile diagnosis had an overall increased risk of death compared to those with
no prior C. difficile diagnosis (hazard ratio (HR)= 2.56; 95% CI: 1.28-5.10). Finally, a C.
difficile diagnosis was associated with the development of post-operative complications
(adjusted OR=4.84; 95% CI: 1.28-18.35).
Chapter 4 used two different methodological approaches to determine whether the
incidence of colectomy for colorectal dysplasia or cancer has changed over time. This
study demonstrates that the incidence of colectomy for colorectal dysplasia and cancer
remained stable in the last decade. Thus, the observed reduced colectomy risk previously
reported by Kaplan et al. is most likely driven by a decrease in the incidence of
colectomy due to medically refractory disease and not colorectal dysplasia or cancer.7
Finally, the objective of Chapter 5 was to determine the cost-effectiveness of the
current recommendations for surveillance of colorectal dysplasia and cancer among
patients with IBD and PSC. A cost-utility analysis using Markov modelling was used to
simulate a cohort of IBD-PSC patients in order to compare the following strategies: no
surveillance, colonoscopy every 5 years, biennial surveillance and annual surveillance
(currently recommended). Annual surveillance was more effective than biennial
surveillance, but at an incremental cost of CAN $174,650 per QALY gained compared to
biennial surveillance. This means that more frequent colonoscopies improve effectiveness
(i.e. detect more dysplasia and cancer cases and prevents additional deaths), but at a
higher cost. Understanding that the willingness to pay will vary among different health
systems, this study made no recommendations on which strategy to choose rather, we
reported the costs and outcomes associated with each strategy. This information can be
162
used by clinicians and healthcare administrators to plan surveillance programs;
particularly, in recognition of the evolving incidence and risk of surgery for IBD in the
21st Century.
6.2 Limitations
Epidemiological research often encounters situations where the data used for
research is not perfect. Thus, it is important to acknowledge and discuss how these
limitations impact the study results and the generalizability of the findings.
This section expands on the limitations discussed on each chapter, and presents
additional methodological challenges. Because of the similarities in methods and
limitations, Chapters 2-4 are discussed together, and Chapter 5 is discussed separately.
6.2.1 Limitations of observational studies
The studies presented in Chapters 2-4 used secondary data (medical charts, CLS
for C. difficile test results, and administrative health databases). Secondary data are
defined as “data generated for different purposes that may overlap with the objective of
the study”.108 Some of the benefits of using secondary data are that they are readily
available and inexpensive. Since the information was not collected for research purposes,
secondary data have inherent limitations that researchers need to take into consideration.
For example, some of the limitations of the secondary data used in the studies presented
in this dissertation include accuracy of the data and completeness of the information.109
Another challenge for Chapters 2 to 4 was the potential for misclassification of
the disease (i.e. UC), the outcomes (i.e. colectomy and post-operative complications) and
the exposures (i.e. C. difficile infection). For example, in Chapter 3 the misclassification
163
of postoperative complications (i.e. outcome) in the Discharge Abstract Database (DAD)
most likely contributed to the differences in the results between Chapter 2 and Chapter 3.
In Chapter 2 a preoperative diagnosis of C. difficile increased the risk of postoperative
infectious complications. In contrast, in Chapter 3 a preoperative diagnosis of C. difficile
increased the risk of having at least one postoperative complication, but this effect was
not specific to infectious complications. In Chapter 2 postoperative complications were
confirmed by chart review, whereas in Chapter 3 postoperative complications were
derived from ICD codes for complications. Thus, in Chapter 3 the definition of
postoperative complication was subject to a misclassification bias. Ma et al. validated the
accuracy of postoperative complications following colectomy for UC.10 The sensitivity
and specificity of “any complications” (i.e. at least one complication) was more accurate
as compared to a group of complications (e.g. infectious complications).10 Further, the
accuracy of ICD coding for postoperative complications was correlated to the severity of
the complication.10 As a result, in Chapter 3, patients with less severe postoperative
complications were more often classified as controls when compared to those with severe
postoperative complications. This likely resulted in a non-differential misclassification
error that biased the risk estimates for postoperative infectious complications towards the
null.
Another example is the misclassification of the UC population in Chapter 3 and
Chapter 4 with the use of administrative databases. A validated case-definition to identify
UC patients (incident cases in Chapter 3 and prevalent cases in Chapter 4) in Alberta’s
administrative databases was used to identify the patient population.110 This algorithm
has a sensitivity of 83.4%, a specificity of 99.8%, a positive predictive value of 97.4%
164
and negative predictive value of 98.5%.110 The case-definition was shown to optimize the
balance between the specificity (few false-positives) and sensitivity (few false-negatives).
Still, a sensitivity of 83% means that ~17% of the IBD population in Alberta was not
captured. Misclassification is also introduced when stratifying IBD patients by UC versus
Crohn’s disease. A validated scoring system was used to differentiate UC from CD,
which had a sensitivity of 86.3% and a specificity of 99.7%.110 Patients who were not
classifiable to UC or CD by this scoring system were excluded. Finally, Chapter 3
required the development of an inception cohort, which has the potential of mixing
prevalent with incident cases. A previous validation study demonstrated that an 8-year
washout period is required to exclude prevalent cases; however, this long washout period
would have resulted in the exclusion of some true incident cases.
Completeness of the data is another limitation of secondary data sources. For
example, in Chapter 2, one third of hospitalized UC patients were not tested for C.
difficile infection. Surveillance of C. difficile infection among flaring UC patients has
improved overtime and thus, patients admitted to hospital in earlier years were less likely
to be tested. Another explanation for the low test rates is that diagnosing C. difficile
infection when an UC patients is flaring can be challenging due to the similarity of the
clinical symptoms of the two disorders. Among UC patients, the presence of microscopic
pseudomembranes, characteristic of C. difficile infection among the non-IBD population,
cannot be used to diagnose C. difficile due to low sensitivity and specificity.111
Additionally, a C. difficile diagnosis is further complicated by asymptomatic carriage,
which has been detected in 9% of UC patients.112 Because discriminating C. difficile
infection from UC flare is difficult, in addition to high numbers of asymptomatic
165
carriage, some UC patients with milder disease severity may have not been tested.113 Not
testing for C. difficile may have introduced a differential misclassification bias where
patients with a severe form of colitis are more likely to be tested for C. difficile infection,
which may overestimate the association between C. difficile infection and colectomy.
Additionally, the type of data that was collected was limited because of the
retrospective nature of the chart- reviews and the limited number of clinically relevant
variables in administrative health databases. As a result, there is information missing that
could be clinically relevant to these studies. For example, indicators of the disease
severity (e.g. C-reactive protein) and disease activity (e.g. Mayo Score) would have been
useful to better discern between a mild flare and severe colitis. In addition, medication
history did not reliably capture duration of use or dosage in the case of the chapters using
the medical chart-review data. Further, administrative databases did not provide any
information on patient medication. This is of importance in both Chapter 2 and 3 because
there is no clear consensus on treatment course for C. difficile infection in the setting of
UC and it has been demonstrated that the choice of treatment influences C. difficile
infection outcomes among UC patients.114, 115 In the case of Chapter 4, it was impossible
to determine for how long these patients were on those medications, which could
potentially influence the results.
Finally, the comparison groups used in the studies described in both Chapters 2
and 4 limited the interpretation and generalizability of the study findings. In the case of
Chapter 2, the study was limited to patients admitted to hospital due to a flare. Thus, we
cannot extrapolate the study results to those UC patients with a mild course of UC who
have never been in hospital. In Chapter 4, we identified patients who had a colectomy for
166
medically refractory disease as our comparison group. This patient group represents a
much more severe diseased population, potentially biasing some of the results. Like in
the case of the observed “protective effect” of 5-ASA and azathioprine/6-mercaptopurine.
The fact that our control group consisted of UC patients that were sicker than the general
UC population hinders any conclusion that the observed association was due to a true
protective effect on these medications.
6.2.2 Limitations of decision analysis study
A decision-analytic model was used in Chapter 5 in order to compare different
colonoscopy surveillance strategies in terms of outcomes and costs to determine the costeffectiveness of each strategy. In this model, mathematical relationships were used to
estimate a series of outcomes that would result from the strategies being evaluated.116
Thus, modelling was the best option in this situation given that the current evidence
needed for this analysis was incomplete, integrating information from primary and
secondary sources like clinical trials, meta-analyses, observational studies and/or expert
opinion. This decision tree was constructed based on what has been observed in the
population to represent a simplified version of real life situations. A key component of
this simplification process was deciding which events that are part of the disease process
in the real population should be included or excluded and understanding the impact these
assumptions had on the results. An example of an event excluded from the decision tree
was the occurrence of colonoscopies performed due to flares. In real life, some cancers
are diagnosed by chance at the time of a colonoscopy that is performed because of a flare.
Also, one of the main problems encountered during the design phase in this study
was the uncertainty around the model parameters. The lack of data available and the
167
heterogeneity between the studies hindered an attempt to conduct a meta-analysis.
Furthermore, most of the studies used to populate this model were of short duration and
assumptions were made in order to fit the lifetime horizon of this model. In addition,
assumptions were made based on expert opinion in circumstances were no information
was available. For example, the proportion of patients that survive the period between
being diagnosed with end-stage liver disease and having a liver available for transplant.
However, we performed a thorough external validation process and multiple scenario
analyses were performed to ensure the model outputs mimicked real population outcomes
reported in the literature in order to minimize biases introduced by assumptions and less
than ideal estimates. In addition, the impact on the model’s results of the uncertainty
around the parameters was assessed by probabilistic sensitivity analysis.
6.3 Implications on Clinical Management and Public Health
Epidemiology is crucial in medicine as it attempts to describe the variation in the
outcomes of illness and identify risk factors that could explain the outcomes observed.117
This is done in order to identify potential ways to modify risk factors that could result in
a reduction of the burden of disease. The results presented in this thesis will have an
impact on prevention of adverse outcomes (i.e. colectomy, post-operative complications,
mortality), and may influence guidelines on patient management and shape future
research development.
The research presented in this dissertation demonstrates that C. difficile infection
increases the risk of colectomy, post-operative complications and mortality. Thus,
reducing the risk of developing a C. difficile infection, by identifying UC patients at high
168
risk of acquiring C. difficile infection and managing them aggressively may result in a
reduction in risk of colectomy and postoperative complications and reducing overall
mortality. Additionally, C. difficile-positive patients who had a colectomy should be
monitored closely for early detection of post-operative complications. Also, this work
highlights the importance of testing patients who were admitted to hospital for a flare for
C. difficile, so that the appropriate treatment course can be given to these patients.
The findings of Chapter 5 will guide health care systems on choosing between
colorectal cancer surveillance strategies; based on what they are willing to pay.
Adherence to surveillance guidelines is a physician and patient issue.118 Thus, the results
of this study can be used to inform and educate both physicians and patients on the
benefits associated with adherence to surveillance recommendations. Finally, this study
paved the road for future directions of colorectal cancer research among IBD-PSC
patients.
6.4 Future Research
These studies answer knowledge gaps in the literature and also served generate
additional hypotheses. Further, the acquisition of the Alberta Health administrative
databases along with the validation of the case definitions provide numerous
opportunities for studies in the areas of C. difficile infection and colorectal cancer in UC.
For example, the Alberta health databases could be used to compare and contrast clusters
in space and time of C. difficile cases in IBD and non-IBD patients. These findings could
lead to studies that compare C. difficile strains affecting IBD and non-IBD patients.
Additionally, the Alberta Health databases could be used to determine the annual
169
incidence, mortality and geographic location of colorectal cancers among IBD patients
and compared it to that of the non-IBD population.
We demonstrated that a C. difficile diagnosis increases the risk of colectomy.
Clinical trials are needed to identify which treatment course (e.g. antibiotics and/or UC
flare treatment or fecal microbiota transplant) improves outcomes among these patients.
These findings could further help identify which of these treatments is the most costeffective way of managing these patients. Another area that needs further investigation is
the development of patient related factors (clinical profile, genetic susceptibility,
serology, etc.) that may predict the acquisition of C. difficile infection among UC
patients. Finally, we mentioned that C. difficile diagnosis is complicated because it
mimics the symptoms of a flare and because of asymptomatic carriage.112 Similarly
asymptomatic carriage is observed in children, and thus, future studies should evaluate
pediatric-onset UC. The presence of elevated fecal inflammatory cytokines has been used
to differentiate children with asymptomatic colonization from those with the disease.119
Thus, future studies should identify inflammatory biomarkers that distinguish C. difficile
colonization from disease among UC patients.
Based on Chapter 4 results, the incidence of colorectal dysplasia and cancer has not
changed in the last decade. The next step would be to investigate factors that may
influence the risk of colectomy for dysplasia or cancer; for example, lack of adherence to
surveillance practices or the use of chemoprotective IBD medications. While a recent
meta-analysis concluded that 5-ASA does not confer a protective effect against the
development of colorectal cancer, recent experimental colitis models and case-control
studies suggest that biologics may decrease the risk of colorectal cancer.104-106
170
Prospective studies to evaluate the effect of biologics on cancer risk should be conducted
on newly diagnosed patients to allow for enough lag time between the introduction of
these medications and the risk of colorectal cancer.
In Chapter 5 we identified several knowledge gaps in the area of colorectal
dysplasia and cancer among IBD-PSC patients. For example, more accurate information
is needed for the cumulative incidence of colorectal dysplasia and cancer among IBDPSC patients. Also, IBD-PSC patients’ utilities are missing from the literature. Thus, a
study should be conducted to determine patients’ utilities for colorectal cancer, liver
transplant, and colectomy. Also, newer surveillance techniques (e.g. chromoendoscopy or
confocal microscopy) have better detection rates of colorectal dysplasia than colonoscopy
with random biopsies. 98 These techniques were not incorporated into the model because
they are not readily used in practice. However, the cost-utility model in Chapter 5 may be
expanded to incorporate these surveillance techniques when wider utilization occurs in
the community.
6.5 Conclusions
Collectively, the 4 studies in this dissertation evaluate the impact of C. difficile
infection and colorectal neoplasia on colectomy for UC. In Chapters 2 and 3 we
demonstrated that C. difficile impacts the short- and long-term risk of colectomy. Further,
Chapters 2 and 3 are the first studies to demonstrate that C. difficile infection increases
the risk of postoperative complications among UC patients. Chapter 3 is the first study to
establish the 1-, 3- and 5-year risk of developing a C. difficile infection after UC
diagnosis. Chapter 4 demonstrated that the colectomy incidence for colorectal dysplasia
171
and cancer remained stable between 1997-2009. Thus, the previously reported decrease
in colectomy rates is most likely due to a decrease in colectomies for medically refractory
UC. Finally, Chapter 5 was the first cost-utility analysis to establish the cost-effectiveness
of the current guidelines for colorectal neoplasia surveillance among IBD-PSC patients.
While these studies answered many knowledge gaps in the literature, it also served as the
foundation for future research. So in the future, we will have a better understanding of
these modifiable risk factors in order to reduce the burden of UC.
1!
!
172
References
1.
Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of
the inflammatory bowel diseases with time, based on systematic review.
Gastroenterology 2012;142:46-54.
2.
Crohn’s & Colitis Foundation of Canada. The impact of inflammatory bowel
disease in Canada: 2012 final report and recommendations, 2012.
3.
Jess T, Gamborg M, Munkholm P, et al. Overall and cause-specific mortality in
ulcerative colitis: meta-analysis of population-based inception cohort studies. Am
J Gastroenterol 2007;102:609-17.
4.
Bewtra M, Kilambi V, Fairchild AO, et al. Patient preferences for surgical versus
medical therapy for ulcerative colitis. Inflamm Bowel Dis 2014;20:103-14.
5.
Ghosh S, Mitchell R. Impact of inflammatory bowel disease on quality of life:
Results of the European Federation of Crohn's and Ulcerative Colitis Associations
(EFCCA) patient survey. J Crohns Colitis 2007;1:10-20.
6.
Frolkis AD, Dykeman J, Negron ME, et al. Risk of surgery for inflammatory
bowel diseases has decreased over time: a systematic review and meta-analysis of
population-based studies. Gastroenterology 2013;145:996-1006.
7.
Kaplan GG, Seow CH, Ghosh S, et al. Decreasing colectomy rates for ulcerative
colitis: a population-based time trend study. Am J Gastroenterol 2012;107:187987.
8.
de Silva S, Ma C, Proulx MC, et al. Postoperative complications and mortality
following colectomy for ulcerative colitis. Clin Gastroenterol Hepatol
2011;11:972-80.
173
9.
Frolkis AD, Kaplan GG, Patel A, et al. Short-term emergent readmission and
postoperative complications in children and adults with inflammatory bowel
disease who undergo intestinal resection – A population-based study. Accepted:
Inflamm Bowel Dis 2014.
10.
Ma C, Crespin M, Proulx MC, et al. Postoperative complications following
colectomy for ulcerative colitis: A validation study. BMC Gastroenterol
2012;12:39.
11.
Nguyen GC, Kaplan GG, Harris ML, et al. A national survey of the prevalence
and impact of Clostridium difficile infection among hospitalized inflammatory
bowel disease patients. Am J Gastroenterol 2008;103:1443-50.
12.
Rodemann JF, Dubberke ER, Reske KA, et al. Incidence of Clostridium difficile
infection in inflammatory bowel disease. Clin Gastroenterol Hepatol 2007;5:33944.
13.
Issa M, Vijayapal A, Graham MB, et al. Impact of Clostridium difficile on
inflammatory bowel disease. Clin Gastroenterol Hepatol 2007;5:345-51.
14.
Bossuyt P, Verhaegen J, Van Assche G, et al. Increasing incidence of Clostridium
difficile-associated diarrhea in inflammatory bowel disease. J Crohn's Colitis
2009;3:4-7.
15.
Goodhand JR, Alazawi W, Rampton DS. Systematic review: Clostridium difficile
and inflammatory bowel disease. Aliment Pharmacol Ther 2011;33:428-41.
16.
Jen MH, Saxena S, Bottle A, et al. Increased health burden associated with
Clostridium difficile diarrhoea in patients with inflammatory bowel disease.
Aliment Pharmacol Ther 2011;33:1322-31.
174
17.
Jodorkovsky D, Young Y, Abreu MT. Clinical outcomes of patients with
ulcerative colitis and co-existing Clostridium difficile infection. Dig Dis Sci
2010;55:415-20.
18.
Murthy SK, Steinhart AH, Tinmouth J, et al. Impact of Clostridium difficile colitis
on 5-year health outcomes in patients with ulcerative colitis. Aliment Pharmacol
Ther 2012;36:1032-9.
19.
Ananthakrishnan AN, McGinley EL, Binion DG. Excess hospitalisation burden
associated with Clostridium difficile in patients with inflammatory bowel disease.
Gut 2008;57:205-10.
20.
Ricciardi R, Ogilvie JW, Roberts PL, et al. Epidemiology of Clostridium difficile
colitis in hospitalized patients with inflammatory bowel diseases. Dis Colon
Rectum 2009;52:40-5.
21.
Khan NA, Quan H, Bugar JM, et al. Association of postoperative complications
with hospital costs and length of stay in a tertiary care center. J Gen Intern Med
2006;21:177-80.
22.
Rubin DT, Huo D, Kinnucan JA, et al. Inflammation is an independent risk factor
for colonic neoplasia in patients with ulcerative colitis: a case-control study. Clin
Gastroenterol Hepatol 2013;11:1601-8.
23.
Rutter M, Saunders B, Wilkinson K, et al. Severity of inflammation is a risk
factor
for
colorectal
neoplasia
in
2004;126:451-9.
175
ulcerative
colitis.
Gastroenterology
24.
Nieminen U, Jussila A, Nordling S, et al. Inflammation and disease duration have
a cumulative effect on the risk of dysplasia and carcinoma in IBD: a case-control
observational study based on registry data. Int J Cancer 2014;134:189-96.
25.
Jess T, Rungoe C, Peyrin-Biroulet L. Risk of colorectal cancer in patients with
ulcerative colitis: a meta-analysis of population-based cohort studies. Clin
Gastroenterol Hepatol 2012;10:639-45.
26.
Boonstra K, Weersma RK, van Erpecum KJ, et al. Population-based
epidemiology, malignancy risk and outcome of primary sclerosing cholangitis.
Hepatology 2013;58:2045-55.
27.
Kornbluth A, Sachar DB. Ulcerative colitis practice guidelines in adults:
American College Of Gastroenterology, Practice Parameters Committee. Am J
Gastroenterol 2010;105:501-23.
28.
Farraye FA, Odze RD, Eaden J, et al. AGA medical position statement on the
diagnosis and management of colorectal neoplasia in inflammatory bowel disease.
Gastroenterology 2010;138:738-45.
29.
Targan SR, Shanahan F, Karp LC. Inflammatory Bowel Disease: Translating
Basic Science into Clinical Practice. West Sussex, UK: Blackwell Publishing,
2010.
30.
Odze R. Diagnostic problems and advances in inflammatory bowel disease. Mod
Pathol 2003;16:347-58.
31.
Cohen RD, Yu AP, Wu EQ, et al. Systematic review: the costs of ulcerative
colitis in Western countries. Aliment Pharmacol Ther 2010;31:693-707.
176
32.
Orholm M, Munkholm P, Lanhholz E, et al. Familial occurrence of inflammatory
bowel disease. N Engl J Med 1991;324:84-8.
33.
Jostins L, Ripke S, Weersma RK, et al. Host-microbe interactions have shaped the
genetic architecture of inflammatory bowel disease. Nature 2012;491:119-24.
34.
Thompson AI, Lees CW. Genetics of ulcerative colitis. Inflamm Bowel Dis
2011;17:831-48.
35.
Gent AE, Hellier MD, Grace RH, et al. Inflammatory bowel disease and domestic
hygiene in infancy. Lancet 1994;343:766-7.
36.
Stallmach A, Carstens O. Role of infections in the manifestation or reactivation of
inflammatory bowel diseases. Inflamm Bowel Dis 2002;8:213-18.
37.
Klement E, Cohen RV, Boxman J, et al. Breastfeeding and risk of inflammatory
bowel disease: a systematic review with meta-analysis. Am J Clin Nutr
2004;80:1342-52.
38.
Kaplan GG, Hubbard J, Korzenik J, et al. The inflammatory bowel diseases and
ambient air pollution: a novel association. Am J Gastroenterol 2010;105:2412-9.
39.
Hou JK, Abraham B, El-Serag H. Dietary intake and risk of developing
inflammatory bowel disease: a systematic review of the literature. Am J
Gastroenterol 2011;106:563-73.
40.
Salim SY, Kaplan GG, Madsen KL. Air pollution effects on the gut microbiota: A
link between exposure and inflammatory bowel disease. Gut Microbes
2014;5:215-19.
41.
Ng SC, Bernstein CN, Vatn MH, et al. Geographical variability and
environmental risk factors in inflammatory bowel disease. Gut 2013;62:630-49.
177
42.
Langholz E, Munkholm P, Davidsen M, et al. Course of ulcerative colitis:
analysis of changes in disease activity over years. Gastroenterology 1994;107:311.
43.
Vavricka SR, Brun L, Ballabeni P, et al. Frequency and risk factors for
extraintestinal manifestations in the Swiss inflammatory bowel disease cohort. Am
J Gastroenterol 2011;106:110-9.
44.
Kaplan GG, Laupland KB, Butzner D, et al. The burden of large and small duct
primary sclerosing cholangitis in adults and children: a population-based analysis.
Am J Gastroenterol 2007;102:1042-9.
45.
Molodecky NA, Kareemi H, Parab R, et al. Incidence of primary sclerosing
cholangitis: a systematic review and meta-analysis. Hepatology 2011;53:1590-9.
46.
Ananthakrishnan AN, Cagan A, Gainer VS, et al. Mortality and extraintestinal
cancers in patients with primary sclerosing cholangitis and inflammatory bowel
disease. J Crohns Colitis 2014; doi: 10.1016/j.crohns.2014.01.019. [Epub ahead
of print].
47.
Broome U, Olsson R, Loof L, et al. Natural history and prognostic factors in 305
Swedish patients with primary sclerosing cholangitis. Gut 1996;38:610-615.
48.
Silverberg MS, Satsangi J, Ahmad T, et al. Toward an integrated clinical,
molecular and serological classification of inflammatory bowel disease: report of
a Working Party of the 2005 Montreal World Congress of Gastroenterology. Can
J Gastroenterol 2005;19 Suppl A:5a-36a.
49.
Quezada SM, Cross RK. Association of age at diagnosis and ulcerative colitis
phenotype. Dig Dis Sci 2012;57:2402-7.
178
50.
Regueiro M, Loftus EV, Steinhart AH, et al. Medical management of Left-sided
ulcerative colitis and ulcerative proctitis: Critical evaluation of therapeutic trials.
Inflamm Bowel Dis 2006;12:979-94.
51.
Gionchetti P, Amadini C, Rizzello F, et al. Review article: treatment of mild to
moderate ulcerative colitis and pouchitis. Aliment Pharmacol Ther 2002;16:13-9.
52.
Feagan BG, MacDonald JK. Oral 5-aminosalicylic acid for induction of remission
in ulcerative colitis. Cochrane Database Syst Rev 2012; CD000543.
53.
Kane S, Huo D, Aikens J, et al. Medication nonadherence and the outcomes of
patients with quiescent ulcerative colitis. Am J Med 2003;114:39-43.
54.
Faubion WA, Loftus EV, Harmsen WS, et al. The natural history of corticosteroid
therapy
for
inflammatory
bowel
disease:
A
population-based
study.
Gastroenterology 2001;121:255-60.
55.
Lopez-Sanroman A, Bermejo F, Carrera E, et al. Efficacy and safety of
thiopurinic immunomodulators (azathioprine and mercaptopurine) in steroiddependent ulcerative colitis. Aliment Pharmacol Ther 2004;20:161-6.
56.
Stidham RW, Lee TC, Higgins PD, et al. Systematic review with network metaanalysis: the efficacy of anti-tumour necrosis factor-alpha agents for the treatment
of ulcerative colitis. Aliment Pharmacol Ther 2014;39:660-71.
57.
Costa J, Magro F, Caldeira D, et al. Infliximab reduces hospitalizations and
surgery interventions in patients with inflammatory bowel disease: a systematic
review and meta-analysis. Inflamm Bowel Dis 2013;19:2098-110.
179
58.
Kaplan GG, McCarthy EP, Ayanian JZ, et al. Impact of hospital volume on
postoperative morbidity and mortality following a colectomy for ulcerative colitis.
Gastroenterology 2008;134:680-7.
59.
Tulchinsky H, Averboukh F, Horowitz N, et al. Restorative proctocolectomy
impairs fertility and pregnancy outcomes in women with ulcerative colitis.
Colorectal Dis 2013;15:842-7.
60.
Cornish JA, Tan E, Teare J, et al. The effect of restorative proctocolectomy on
sexual function, urinary function, fertility, pregnancy and delivery: a systematic
review. Dis Colon Rectum 2007;50:1128-38.
61.
Solberg IC, Lyrgen I, Jahnsen J, et al. Clinical course during the first 10 years of
ulcerative colitis: Results from a population-based inception cohort (IBSEN
Study). Scand J Gastroenterol 2009;44:431-40.
62.
Chandler RE, Hedberg K, Cieslak PR. Clostridium difficile associated disease in
Oregon: increasing incidence and hospital-level risk factors. Infect Control Hosp
Epidemiol 2007;28:116-22.
63.
Hubert B, Loo VG, Bourgault AM, et al. A portrait of the geografic dissemination
of the Clostridium difficile North American Pulsed-Field Type 1 strain and the
epidemiology of C. difficile-associated disease in Quebec. Clin Infect Dis
2007;44:238-44.
64.
Musa S, Thomson S, Cowan M, et al. Clostridium difficile infection and
inflammatory bowel disease. Scand J Gastroenterol 2010;45:261-72.
65.
Greenfield C, Aguilar-Ramirez JR, Pounder RE, et al. Clostridium difficle and
inflammatory bowel disease. Gut 1983;24:713-7.
180
66.
Rolny P, Jarnerot G, Mollby R. Ocurrence of Clostridium difficile toxin in
inflammatory bowel disease. Scand J Gastroenterol 1983;18:61-4.
67.
Keighley MRB, Young D, Johnson M, et al. Clostridium difficile toxin in acute
diarrhoea complicating inflammatory bowel disease. Gut 1982;23:410-4.
68.
Burke DA, Axon AT. Clostridium difficile, sulphasalazine, and ulcerative colitis.
Postgrad Med J 1987;63:955-7.
69.
Weber P, Koch M, Heizmann WR, et al. Microbic superinfection in relapse of
inflammatory bowel disease. J Clin Gastroenterol 1991;14:301-8.
70.
Ott C, Girlich C, Klebl F, et al. Low risk of Clostridium difficile infections in
hospitalized patients with inflammatory bowel disease in a German tertiary
referral center. Digestion 2011;84:187-92.
71.
Masclee GM, Penders J, Jonkers DM, et al. Is Clostridium difficile associated
with relapse of inflammatory bowel disease? results from a retrospective and
prospective cohort study in the Netherlands. Inflamm Bowel Dis 2013;19:212531.
72.
Bignardi GE. Risk factors for Clostridium difficile infection. J Hosp Infect
1998;40:1-15.
73.
Kelly CP, LaMonte JT. Clostridium difficile infection. Ann Rev Med
1998;49:375-90.
74.
Bien J, Palagani V, Bozko P. The intestinal microbiota dysbiosis and Clostridium
difficile infection: is there a relationship with inflammatory bowel disease? Ther
Adv Gastroenterol 2013;6:53-68.
181
75.
Schneeweiss S, Korzenik J, Solomon DH, et al. Infliximab and other
immunomodulating drugs in patients with inflammatory bowel disease and the
risk of serious bacterial infections. Aliment Pharmacol Ther 2009;30:253-64.
76.
Ananthakrishnan AN, Oxford EC, Nguyen DD, et al. Genetic risk factors for
Clostridium difficile infection in ulcerative colitis. Aliment Pharmacol Ther
2013;38:522-30.
77.
Powell N, Jung SE, Krishnan B. Clostridium difficile infection and inflammatory
bowel disease: a marker for disease extent? Gut 2008;57:1183-4.
78.
Ananthakrishnan AN. Clostridium difficile infection: epidemiology, risk factors
and management. Nat Rev Gastroenterol Hepatol 2011;8:17-26.
79.
Croabach MJT, Dekkersm OM, Wilcox MH, et al. European Society of Clinical
Microbiology
and
Infectious
Diseases
(ESCMID):
data
review
and
recommendations for diagnosing Clostridium difficile-infection (CDI). Clin
Microbiol Infect 2009;15:1053-66.
80.
Guo B, Harstall C, Louie T, et al. Systematic review: faecal transplantation for the
treatment of Clostridium difficile-associated disease. Aliment Pharmacol Ther
2012;35:865-75.
81.
Cammarota G, Ianiro G, Gasvarrini A. Fecal Microbiota transplantation for the
treatment of Clostridium difficile infection: A systematic review. J Clin
Gastroenterol 2014; In Press. doi: 10.1097/MCG.0000000000000046
82.
Surawicz CM, Brandt LJ, Binion DG, et al. Guidelines for diagnosis, treatment,
and prevention of Clostridium difficile infections. Am J Gastroenterol
2013;108:478-98.
182
83.
Ananthakrishnan AN, McGinley EL. Infection-related hospitalizations are
associated with increased mortality in patients with inflammatory bowel diseases.
J Crohns Colitis 2013;7:107-12.
84.
Ullman T, Loftus EV, Kakar S, et al. The fate of low grade dysplasia in ulcerative
colitis. Am J Gastroenterol 2002;97:922-7.
85.
Itzkowitz SH, Yio X. Inflammation and cancer: IV. Colorectal cancer in
inflammatory bowel disease: the role of inflammation. Am J Physiol Gastrointest
Liver Physiol 2004;281:7-17.
86.
Eaden JA, Abrams KR, Mayberry JF. The risk of colorectal cancer in ulcerative
colitis: a meta-analysis. Gut 2001;48:526-35.
87.
Thomas T, Abrams KA, Robinson RJ, et al. Meta-analysis: cancer risk of lowgrade dysplasia in chronic ulcerative colitis. Aliment Pharmacol Ther
2007;25:657-68.
88.
Lutgens MW, van Oijen MG, van der Heijden GJ, et al. Declining risk of
colorectal cancer in inflammatory bowel disease: an updated meta-analysis of
population-based cohort studies. Inflamm Bowel Dis 2013;19:789-99.
89.
Castano-Milla C, Chaparro M, Gisbert JP. Systematic review with meta-analysis:
the declining risk of colorectal cancer in ulcerative colitis. Aliment Pharmacol
Ther 2014;39:645-59.
90.
Triantafillidis JK, Nasioulas G, Kosmidis PA. Colorectal cancer and
inflammatory bowel disease: Epidemiology, risk factors, mechanisms of
carcinogenesis and prevention strategies. Anticancer Res 2009;29:2727-38.
183
91.
Velayos FS, Loftus EV, Jr., Jess T, et al. Predictive and protective factors
associated with colorectal cancer in ulcerative colitis: A case-control study.
Gastroenterology 2006;130:1941-9.
92.
Grivennikov SI. Inflammation and colorectal cancer: colitis-associated neoplasia.
Semin Immunopathol 2013;35:229-44.
93.
Jess T, Loftus EV, Velayos FS, et al. Incidence and prognosis of colorectal
dysplasia in inflammatory bowel disease: A population-based study from
Olmstead county, Minnesota. Inflamm Bowel Dis 2006;12:669-76.
94.
Soetikno RM, Lin OS, Heidenreich PA, et al. Increased risk of colorectal
neoplasia in patients with primary sclerosing cholangitis and ulcerative colitis: A
meta-analysis. Gastrointest Endosc 2002;56:48-54.
95.
Claessen MM, Lutgens MW, van Buuren HR, et al. More right-sided IBDassociated colorectal cancer in patients with primary sclerosing cholangitis.
Inflamm Bowel Dis 2009;15:1331-6.
96.
Singh S, Varayil JE, Loftus EV, Jr., et al. Incidence of colorectal cancer after liver
transplantation for primary sclerosing cholangitis: a systematic review and metaanalysis. Liver Transpl 2013;19:1361-9.
97.
Riddell RH, Goldman H, Ransohoff DF, et al. Dysplasia in inflammatory bowel
disease: Standardized classification with provisional clinical applications. Hum
Pathol 1983;14:931-68.
98.
Collins PD, Mpofu C, Watson AJ, et al. Strategies for detecting colon cancer
and/or dysplasia in patiens with inflammatory bowel disease. Cochrane Database
Syst Rev 2006; CD000279.
184
99.
Wu L, Li P, Wu J, et al. The diagnostic accuracy of chromoendoscopy for
dysplasia in ulcerative colitis: meta-analysis of six randomized controlled trials.
Colorectal Dis 2012;14:416-20.
100.
Dong YY, Li YQ, Yu YB, et al. Meta-analysis of confocal laser endomicroscopy
for the detection of colorectal neoplasia. Colorectal Dis 2013;15:488-95.
101.
Ullman T, Croog V, Harpaz N, et al. Progression of flat low-grade dysplasia to
Advanced neoplasia in patients with ulcerative colitis. Gastroenterology
2003;125:1311-29.
102.
Bernstein CN, Shanahan F, Weinstein WM. Are we telling patients the truth about
surveillance colonosocpy in ulcerative colitis? Lancet 1994;343:71-4.
103.
Siegel CA, Schwartz LM, Woloshin S, et al. When should ulcerative colitis
patients undergo colectomy for dysplasia? Mismatch between patient preferences
and physician recommendations. Inflamm Bowel Dis 2010;16:1658-62.
104.
Nguyen GC, Gulamhusein A, Bernstein CN. 5-aminosalicylic acid is not
protective against colorectal cancer in inflammatory bowel disease: a metaanalysis of non-referral populations. Am J Gastroenterol 2012;107:1298-304.
105.
Baars JE, Looman CW, Steyerberg EW, et al. The risk of inflammatory bowel
disease-related colorectal carcinoma is limited: results from a nationwide nested
case-control study. Am J Gastroenterol 2011;106:319-28.
106.
Onizawa M, Nagaishi T, Kanai T, et al. Signaling pathway via TNF-alpha/NFkappaB in intestinal epithelial cells may be directly involved in colitis-associated
carcinogenesis. Am J Physiol Gastrointest Liver Physiol 2009;296:G850-9.
185
107.
Hansen JD, Kumar S, Lo WK, et al. Ursodiol and colorectal cancer or dysplasia
risk in primary sclerosing cholangitis and inflammatory bowel disease: a metaanalysis. Dig Dis Sci 2013;58:3079-87.
108.
Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Philadelphia, PA,
USA: Lippincott Williams &Wilkins, 2008.
109.
Molodecky NA, Kaplan G. Environmental risk factors for inflammatory bowel
disease. Gastroenterol Hepatol 2010;6:339-46.
110.
Rezaie A, Quan H, Fedorak R, et al. Development and validation of an
administrative case definition for inflammatory bowel diseases. Can J
Gastroenterol 2012;26:711-8.
111.
Wang T, Matukas L, Streutker CJ. Histologic findings and clinical characteristics
in acutely symptomatic ulcerative colitis patients with superimposed Clostridium
difficile infection. Am J Clin Pathol 2013;140:831-7.
112.
Clayton EM, Rea MC, Shanahan F, et al. The vexed relationship between
Clostridium difficile and inflammatory bowel disease: an assessment of carriage
in an outpatient setting among patients in remission. Am J Gastroenterol
2009;104:1162-9.
113.
Burnham CA, Carroll KC. Diagnosis of Clostridium difficile infection: an
ongoing conundrum for clinicians and for clinical laboratories. Clin Microbiol
Rev 2013;26:604-30.
114.
Yanai H, Nguyen GC, Yun L, et al. Practice of gastroenterologists in treating
flaring inflammatory bowel disease patients with Clostridium difficile: Antibiotics
186
alone
or
combined
antibiotics/immunomodulators?
Inflamm
Bowel
Dis
2011;17:1540-6.
115.
Ben-Horin S, Margalit M, Bossuyt P, et al. Combination immunomodulator and
antibiotic treatment in patients with inflammatory bowel disease and Clostridium
difficile infection. Clin Gastroenterol Hepatol 2009;7:981-7.
116.
Briggs A, Claxton K, Sculpher M. Decision modelling for health economic
evaluation: Oxford University Press, 2008.
117.
Oleckno WA. Epidmeiology: Concepts and methods. Long Grove, IL, USA:
Waveland Press, Inc., 2008.
118.
Kaplan GG, Heitman SJ, Hilsden RJ, et al. Population-based analysis of practices
and costs of surveillance for colonic dysplasia in patients with primary sclerosing
cholangitis and colitis. Inflamm Bowel Dis 2007;13:1401-7.
119.
El Feghaly RE, Stauber JL, Tarr PI, et al. Intestinal inflammatory biomarkers and
outcome in pediatric Clostridium difficile infections. J Pediatr 2013;163:1697704.
187