The role of biologics in the treatment of ulcerative colitis
A Thesis
Submitted to the Faculty
of
Drexel University
by
Andrew James Klink
in partial fulfillment of the
requirements for the degree
of
Doctor of Philosophy
May 2014
® Copyright 2014
Andrew J. Klink. All Rights Reserved.
ii
Dedication
I dedicate this work to Samson, who sat on my lap for two years while I wrote this, imparting his own form of wisdom and offering much needed distraction and calming energy. iii
Acknowledgments
There are numerous individuals who generously gave their time, effort, and expertise to make this work possible. Without them, I would still be struggling through my 65th draft of manuscript number 1. Somehow I had been lucky enough to be surrounded with the following individuals and countless more to help me along the way.
Brevity is not my strong suit, so bare with me, and forgive the lack of page space necessary to include everyone who make a lasting contribution to my development.
I first want to thank members of my thesis committee. Thanks to Lucy Robinson for always providing a fresh perspective and a new angle on all of my papers. Lindsey
Albenberg was invaluable at combing through my manuscripts to make them clinically meaningful. Thank you, Bob Baldassano, for not only your clinical expertise and professional guidance, but also for generously offering valuable resources to complete this project. I am grateful for the critical appraisal of my work by Alison Evans, who made this project leagues stronger; I still cannot believe you agreed to be my on my doctoral committee even after graciously serving as my master’s thesis advisor; thank you! I am indebted to Judith Kelsen, who provided me with the necessary clinical guidance throughout this project. I especially appreciate that you entrusted me with your own research so that I could lend a hand (I hope) where I could. Of course, I would not have made it without the support, counsel, and guidance offered by my advisor, Brian
Lee. You truly pushed me to think outside the chi-square box and try more challenging and exciting methods.
I would be remised if I did not acknowledge the many friends, colleagues, professors, and classmates who offered support and encouragement, while patiently iv holding my hand at times: Anna Wallace, Zee Mohamad, Alex Kecojevic, Dorothy
Miller, Betsy Brooks, Nicole Cossrow, Pam Weiss, Seth Welles, Loni Philip Tabb,
Marcy Polansky, Amy Auchincloss, Yvonne Michael, and Craig Newschaffer.
Arguably deserving greatest credit to my success in this project is my partner, Frank
Calabrese. Thank you for putting up with me, particularly after the 927th time I used the excuse that I was “busy writing my dissertation.” You can finally cease your refrain,
“hurry up and finish your paper!” Oh, and your weekend care packages you made for me in the fridge certainly made long weekends more enjoyable. Special gratitude is due to my parents, Jim and Cindy Klink, whose love and encouragement were always a needed boost. Lastly, I want to give thanks to my family in Wisconsin, Minnesota,
Connecticut/Florida/North Carolina, and New Jersey. Thank you all! v
Table of Contents
1! LIST OF TABLES ...... IX!
2! LIST OF FIGURES ...... X!
3! ABSTRACT ...... XI!
4! INTRODUCTION ...... 13!
5! BACKGROUND ...... 16!
5.1! INCIDENCE, PREVALENCE, & MORBIDITY ...... 16!
5.1.1! Burden of IBD ...... 16!
5.1.2! Burden of disease secondary to IBD ...... 18!
5.1.3! Burden of severe and fulminant UC ...... 18!
5.2! TREATMENT OF SEVERE UC ...... 20!
5.2.1! Lack of established standards of care for inpatient pediatric UC ...... 21!
5.3! COMPARATIVE EFFECTIVENESS RESEARCH ...... 22!
5.3.1! Limitations of current disease severity scores for UC ...... 24!
5.3.2! Data source ...... 25!
5.4! STATISTICAL ANALYSES ...... 26!
5.4.1! Disease risk scores ...... 27!
5.5! SUMMARY ...... 34!
5.6! TABLES ...... 36!
6! TRENDS IN INPATIENT MANAGEMENT OF PEDIATRIC SEVERE
ULCERATIVE COLITIS: A RETROSPECTIVE US COHORT FROM 2003 TO 2012 .... 39!
6.1! ABSTRACT ...... 40!
6.2! INTRODUCTION ...... 41!
6.3! MATERIALS & METHODS ...... 43! vi
6.3.1! Data source ...... 43!
6.3.2! Study cohort ...... 43!
6.3.3! Statistical analysis ...... 44!
6.4! ETHICAL CONSIDERATIONS ...... 45!
6.5! RESULTS ...... 45!
6.5.1! Study cohort ...... 45!
6.5.2! Participating hospitals ...... 45!
6.5.3! Length of stay ...... 46!
6.5.4! Medications ...... 46!
6.5.5! Diagnostic procedures and labs ...... 48!
6.5.6! Surgical procedures ...... 49!
6.6! DISCUSSION ...... 50!
6.7! REFERENCES ...... 56!
6.8! TABLES & FIGURES ...... 59!
7! DEVELOPING AND IMPLEMENTING A DISEASE RISK SCORE TO
CONTROL FOR CONFOUNDING BY INDICATION IN
PHARMACOEPIDEMIOLOGIC STUDIES ...... 68!
7.1! ABSTRACT ...... 69!
7.2! INTRODUCTION ...... 70!
7.3! METHODS ...... 71!
7.3.1! Variable selection ...... 72!
7.3.2! Fitting the model ...... 72!
7.3.3! Model assessment and diagnostics ...... 73!
7.3.4! Implementation in the final model ...... 76!
7.3.5! Case study ...... 77! vii
7.4! RESULTS ...... 80!
7.5! DISCUSSION ...... 83!
7.6! TABLES & FIGURES ...... 87!
7.7! REFERENCES ...... 92!
7.8! APPENDIX. R CODE FOR DRS DEVELOPMENT BY BOOSTED CART AND
IMPLEMENTATION IN FINAL MODELS...... 95!
8! INFLIXIMAB REDUCES RISK OF COLECTOMY AMONG HIGH-RISK
PEDIATRIC PATIENTS WITH ULCERATIVE COLITIS ...... 97!
8.1! ABSTRACT ...... 98!
8.2! INTRODUCTION ...... 99!
8.3! METHODS ...... 100!
8.3.1! Patients ...... 100!
8.3.2! Statistical analyses ...... 101!
8.4! RESULTS ...... 102!
8.5! DISCUSSION ...... 104!
8.6! TABLES & FIGURES ...... 109!
8.7! REFERENCES ...... 114!
9! CONCLUSIONS AND IMPLICATIONS ...... 117!
9.1! MANAGEMENT OF PEDIATRIC UC IN THE INPATIENT SETTING ...... 117!
9.2! DEVELOPMENT AND IMPLEMENTATION OF A DRS ...... 119!
9.3! ASSOCIATION OF INFLIXIMAB AND COLECTOMY ...... 121!
9.4! LIMITATIONS ...... 122!
9.5! RECOMMENDATIONS ...... 123!
10! REFERENCES ...... 125! viii
11! APPENDIX A. SUMMARY OF RESULTS FROM PRELIMINARY
ANALYSES 133!
11.1! ABSTRACT ...... 133!
11.2! RESULTS ...... 134!
11.2.1! Study cohort ...... 134!
11.2.2! Participating hospitals ...... 135!
11.2.3! Length of stay and readmissions ...... 135!
11.2.4! Medication exposures ...... 136!
11.2.5! Procedures performed ...... 138!
11.2.6! Sensitivity analyses ...... 139!
11.3! TABLES & FIGURES ...... 141!
11.4! LIST OF CODE TITLES USED FOR THE PRELIMINARY ANALYSIS ...... 149!
11.4.1! Medication exposures ...... 149!
11.4.2! Imaging procedures ...... 149!
11.4.3! Surgical procedures ...... 150!
12! APPENDIX B. ADDITIONAL TRENDS IN UC MANAGEMENT ...... 152!
13! VITA ...... 166!
ix
1 LIST OF TABLES
Table 1. Modifications to the Montreal Classification for ulcerative colitis.26 .... 36!
Table 2. Classification of patients with ulcerative colitis Truelove & Witt Severity
Index.19 ...... 36!
Table 3. Pediatric ulcerative colitis activity index (PUCAI).40 ...... 37!
Table 4. Summary of studies assessing effect of cyclosporine and infliximab. ... 38!
Table 5. Demographic and clinical characteristics of the study cohort...... 59!
Table 6. Medication exposure characteristics...... 60!
Table 7. Procedures and laboratory studies performed...... 61!
Table 8. Baseline covariates in the DRS model...... 87!
Table 9. Treatment effect estimates...... 89!
Table 10. Characteristics of subjects...... 109!
Table 11. Differences in baseline risk predictors by risk groups...... 110!
Table 12. Stratified treatment effects...... 111!
x
2 LIST OF FIGURES
Figure 1. Length of stay across the study period...... 63!
Figure 2. Infliximab use by year...... 64!
Figure 3. Colectomy rates by year...... 66!
Figure 4. Colectomy rate over annual hospital caseload...... 67!
Figure 5. Observed and predicted colectomy rates across risk deciles...... 90!
Figure 6. Rate of colectomy by exposure status...... 91!
Figure 7. Observed and predicted rates of colectomy across risk octiles...... 112!
Figure 8. Colectomy rates by treatment group...... 113
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3 ABSTRACT The role of biologics in the treatment of ulcerative colitis Andrew J. Klink
Background: Of the many options for the management of pediatric ulcerative colitis
(UC), infliximab is promising in reducing the need for colectomy, but lacks sufficient evidence in the pediatric inpatient setting. We aimed to develop and implement a disease risk score (DRS) to estimate the association between infliximab and colectomy among children hospitalized with UC.
Methods: A DRS, which represents the probability of outcome given baseline covariates, was used to control for confounding by indication. Multivariate logistic regression was used to incorporate the DRS and adjust for additional indicators of worsening clinical course to estimate the odds ratio (OR) of colectomy across treatment groups.
Results: Over the 10-year study period, length of stay decreased by 1.5 days, colectomy rate decreased by 3.9%, while infliximab use increased by 1.5% and was started earlier by 1.8 days. In stratified analyses adjusting for baseline risk of colectomy as well as indicators of worsening clinical course during admission, the OR of colectomy was 0.48
(95% CI: 0.29-0.79) in the highest risk octile comparing those treated with infliximab to those not treated with infliximab.
Conclusion: Variation in the management for severe UC appears to be decreasing, a trend led largely by high caseload hospitals. In a nationally representative cohort from real clinical practice at 42 large pediatric hospitals, high-risk pediatric UC patients had reduced odds of colectomy from the use of infliximab during their admission.
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13
4 INTRODUCTION
Inflammatory bowel disease (IBD) is one of the most common chronic gastrointestinal disorders with an estimated prevalence among children and adolescents between 30-70 cases per 100,0001,2 and an annual incidence of 7 cases per 100,000.3
Several investigations have reported an increase in incidence as much as 3-fold over the past 50 years.4-6 Due to the chronic course of IBD, prevalence in adults is much higher, estimated around 240 cases per 100,000,1 representing 1.4 million people in the United
States7.
IBD is a spectrum of chronic immunologic disorders that includes two main types,
Crohn’s disease (CD) and ulcerative colitis (UC). Recent estimates place the incidence of pediatric CD twice that of pediatric UC, 4.5 and 2.2 per 100,000, respectively.3 Disease activity of UC is categorized as remission, to mild, moderate, severe, and fulminant.
Approximately 15% of UC cases will experience an acute severe attack of UC. The severest disease activity among patients with UC includes severe diarrhea with rectal bleeding, dehydration, tachycardia, anemia, and fever necessitating immediate hospitalization.8 If left untreated, the severest form of UC may lead to toxic dilation, hemorrhage, perforation, and death.9 Children with a severe attack of UC are exposed to a growing armamentarium of treatments in the inpatient setting.10 Although intravenous corticosteroids have greatly improved outcomes among those admitted with UC, approximately 30-40% will be refractory,11 requiring second-line treatment. Conflicting evidence exists as to what the optimal second-line therapy is.12 While up to two-thirds of patients with severe UC may require colectomy,13 the use of newer biologics, namely infliximab, may delay or reduce the need for such surgery.14 However, the risks 14 associated with these immunosuppressants need to be weighed against the risks associated with colectomy. It has been recently reported that the number of hospitalizations for IBD has increased over the past decade, while the proportion of inpatients receiving an IBD-related surgery has decreased. When stratified by patient severity, the proportion of inpatients receiving an IBD-related surgery has not changed among the severest patients.15 Thus, the outcome of colectomy for severe UC has not been curbed, despite the potential of newer therapeutics such as infliximab.16 Sufficient evidence for the effectiveness of infliximab for the treatment of severe UC is lacking.
Substantial variation in diagnostic and therapeutic intervention in the management of pediatric CD was found across 48 outpatient practices.17 Variation in care may suggest a lack of a standard of care, or a lack of adherence to the standard of care, and thus lead to poor outcomes. For example, wide variation in a treatment or procedure may indicate that that treatment or procedure is being underused, overused, or misused.18 It is necessary to first identify variation in treatment and evaluation in order to reduce inappropriate variation and to work toward establishing a standard of care. The extent to which infliximab is used among children with UC in the inpatient setting is widely unknown.
Further, the timing of second-line therapy initiation is not well-established as recommendations range from 3-4 days19 and 5-7 days9 after initiating intravenous corticosteroids. At the same time, infliximab is increasingly being used as corticosteroid- sparing therapy, being initiated even before day 3 of hospitalization.20
The Pediatric Ulcerative Colitis Activity Index (PUCAI) has been developed to describe the phenotypes of UC ranging from mild, to moderate and severe disease activity. The index has been validated against four constructs of disease activity in UC: 1) 15 colonoscopic appearance, 2) physician global assessment, 3) the Mayo score, and 4) the
Truelove and Witts ulcerative colitis disease activity classification.21,22 In observational studies, disease activity, as currently measured by the PUCAI, is vital in order to account for confounding by indication, whereby those with greatest disease severity are most likely to be prescribed second-line drugs and are also most likely to experience poor outcomes. This confounded relationship creates a spurious association between the treatment and poor outcomes. All of these disease activity classification schemes rely on the availability of clinical data. At the present, high-quality administrative data have become ubiquitous, rendering observational studies much less expensive and more feasible than randomized clinical trials. As such, a valid measure of disease activity in
UC using administrative data is urgently needed.
To address these gaps in the medical literature, this thesis comprises of three main studies. The first study describes recent clinical management for severe UC exacerbations from 2003 to 2012 among 42 large US pediatric hospitals and to assess trends across the
10-year period. The second study provides a tutorial for researchers on the development and implementation of a disease risk score to control for confounding by indication in pharmacoepidemiologic studies. Using a disease risk score to estimate the baseline risk of colectomy among pediatric patients with UC, the final study describes the association of infliximab on the receipt of colectomy across a wide range of disease severity. Taken together, the three studies provide clinicians with a better understanding of how infliximab has been used, as well as how it should be used in the inpatient setting for children and adolescents admitted with varying degrees of UC disease severity. 16
5 BACKGROUND
5.1 INCIDENCE, PREVALENCE, & MORBIDITY
Inflammatory bowel disease (IBD) is one of the most common chronic gastrointestinal disorders affecting children. Up to 30% of IBD cases are diagnosed during childhood.23 These patients present features unique to the pediatric population.
The paucity of data regarding the severe subset of IBD patients—those with acute severe and fulminant ulcerative colitis—has resulted in suboptimal therapeutic strategies, none of which have led to a standard of care. This project aims to fill this gap in the medical literature by providing effect estimates of infliximab, a relatively recent medical therapy approved by the Food and Drug Administration (FDA) at the end of 2011 for moderately to severely active UC in children, on the need for colectomy among children hospitalized with UC.
5.1.1 Burden of IBD
IBD is one of the most common chronic gastrointestinal disorders with an estimated prevalence among children and adolescents between 30-70 cases per 100,0001,2 and an annual incidence of 7 cases per 100,0003. Several investigations have reported an increase in incidence as much as 3-fold over the past 50 years.4-6 Due to the chronic course of IBD, prevalence in adults is much higher, estimated around 240 cases per
100,000,1 representing 1.4 million people in the United States7. IBD is a spectrum of immunologic disorders distributed throughout the gastrointestinal tract that include two main types, Crohn’s disease and ulcerative colitis. While Crohn’s disease affects the length of the gastrointestinal tract from mouth to anus, it is typically localized to the 17 ileum (small intestine) and cecum, whereas UC is restricted to the colon (large intestine) and rectum.4 Gastrointestinal features most common to UC include abdominal pain and diarrhea, often associated with blood in the stool.23 A report using 2009 US census data estimated that there are 62,000 children and adolescents with IBD in the US (38,000 with
CD and 23,000 with UC [sic]).24
During the 2005 international conference on IBD, gastroenterologists developed the Montreal Classification of IBD to establish clinic characteristics necessary for describing the disease activity of CD and UC, as well as differentiating between these disease types.25 According to this classification, the disease activity of ulcerative colitis can be defined as remission, mild, moderate, and severe. However, the Montreal
Classification has limited applications to pediatric populations. To address special considerations in the pediatric population, the Paris Classification of IBD has been proposed.26 Although the Paris Classification resulted in significant modifications for the classification of Crohn’s disease in pediatrics (e.g., additional categories for age at diagnosis during childhood, additional disease location categories, consideration of growth delay during childhood and adolescences), little change was seen in the classification of UC (Table 1). Interestingly, the Paris Classification dichotomized UC disease severity as “never severe” or “ever severe” based on the Pediatric Ulcerative
Colitis Activity Index (PUCAI). While the authors cite facilitating pediatric research with the establishment of uniform definitions as motivation for the Paris Classification,26 collapsing the PUCAI score into a dichotomous feature may result in substantial loss of information in the analysis of study results. As such, consideration of the continuous
PUCAI score in some form should be utilized in pediatric IBD research, particularly 18 when attempting to adjust for disease severity. Consideration for disease severity, developing and implementing a disease risk score to depict severity upon admission, was taken in the latter two studies in this thesis.
5.1.2 Burden of disease secondary to IBD
In addition to the clinical features of IBD, extraintestinal manifestations are common in children and adolescents. Up to 35% of pediatric IBD patients have at least one extraintestinal manifestation at presentation.23 These symptoms range from skin, mouth, liver, pancreas, kidney, bone, eye, lung, vasculature, joint, to blood involvement.
While arthritis is the most common extraintestinal manifestation, a similar but less described feature, enthesitis, which is inflammation at the ligament or tendon attachment to bone, may be present just as often (unpublished data). The 30% of IBD cases that have onset before age 20 present challenges unique to children and adolescents.27 These include delayed growth, drug dosing, and changes in social, cognitive, and sexual development, largely due to disease-induced nutrient deficiency.4,23 The Paris
Classification is the only disease activity measure that currently considers growth disturbances due to disease activity.26
5.1.3 Burden of severe and fulminant UC
Approximately 15% of UC cases will experience fulminant UC, characterized by severe diarrhea with rectal bleeding, dehydration, tachycardia, anemia, and fever typically requiring immediate hospitalization.8 If left untreated, fulminant UC may lead to toxic megacolon, massive hemorrhaging, and death.9 In the hospital setting, patients with fulminant UC are exposed to a battery of medications, which commonly include 19 intravenous corticosteroids, typically as first-line treatment, as well as total parenteral nutrition, antibiotics, immunomodulators, and most recently biologics including infliximab. Many of these medications have serious side effects and risk of adverse events, particularly with prolonged exposure. After an unsuccessful course with medical therapy, up to one-third of patients with UC will require surgical intervention in the form of colectomy.28 Long-term estimates of colectomy among adult cohorts are as high as
70% by 5 years after index hospitalization.28 Although colectomy may be curative by removal of the disease-affected area in UC, the procedure carries with it several important short-term and long-term risks. In addition to the risk of surgical complications and postoperative infection, loss of the colon has significant metabolic and functional consequences, including reduced fertility among female patients, and psychological burdens.29-31 Ileal pouch anal anastomosis (IPAA) is commonly done in conjuncture with colectomy. At least one episode of pouchitis may occur among patients with IPAA, and up to 10% may experience pouch failure.32 Many studies have assessed the success of a given treatment for ulcerative colitis by its ability to curb the risk of colectomy.13,33-39 As such, the last study in this thesis provides an estimate of the effect of infliximab on the risk of colectomy among children hospitalized with UC.
The first disease activity index to classify subjects specifically with ulcerative colitis was proposed in 1955 by Truelove and Witts (Table 2).21 Several indices and disease activity scores have been proposed since then.22 Recently, Turner and colleagues developed a disease activity score called the Pediatric Ulcerative Colitis Activity Index
(PUCAI) to account for special considerations in the pediatric ulcerative colitis population (Table 3).40 Today, the PUCAI is widely used in the clinical setting. However, 20 its use in observational pharmacoepidemiologic studies utilizing large administrative databases is limited. The PUCAI score requires data derived from patient medical records and/or from patient reported symptoms and activity level.41 Further, the PUCAI is not explicitly intended to discriminate disease severity beyond severe (i.e., fulminant UC).
As such, a score developed for the use in studies utilizing administrative data is needed that can discriminate between severe and fulminant, as well as between remission, mild, moderate, and severe.
5.2 TREATMENT OF SEVERE UC
Upon admission, patients with severe ulcerative colitis are commonly treated with intravenous corticosteroids.9,19 As early as 1955, treatment with intravenous corticosteroids has been shown to substantially reduce the rate of colectomy and mortality in patients with severe UC.21 During the first 3-7 days of hospitalization for severe UC, corticosteroids are often administered in the form of 1-1.5 mg per kilogram per day of methylprednisolone up to a maximum of 60 mg per day.42 If a response is achieved and maintained during the first week of hospitalization, oral prednisolone may be given in preparation for discharge beginning at a dose 20% higher than methylprednisolone and subsequently tapered.19,42 While 30-40% of patients with severe
UC may be refractory to intravenous corticosteroids,11 up to 60% may exhibit only a partial response.19 In a recent meta-analysis, Turner and colleagues have highlighted a higher rate of intravenous corticosteroid failure in the pediatric population compared to the adult population (37% vs. 33%).43 They posited that this difference in response rates is likely due to the higher proportion of children and adolescents presenting with extensive UC compared to adults (up to 90% vs. 33%).44,45 As a result, the need for 21 subsequent treatment with a second-line therapy is common in the pediatric severe UC population. However, the decision to introduce second-line treatment in this population following non-response at 5-7 days of hospitalization is not always clear.44 The PUCAI score of a patient at this point is weighed heavily in this decision.
Early on in the admission, when it has been determined that a patient is refractory to intravenous corticosteroids – or that intravenous corticosteroids are not appropriate, second-line therapy is initiated and generally includes cyclosporine, infliximab, or colectomy.9,19,46 Cyclosporine is a calcineurin inhibitor, thereby binding to calcineurin resulting in the blockage of T-cell activation and proliferation.19 Cyclosporine is administered intravenously at 2-4 mg per kilogram per day.10 There are considerable side-effects with cyclosporine, including opportunistic infections, nephrotoxicity, hypertension, seizures, peripheral neuropathy, anaphylaxis, and colonic perforation.9,36
Infliximab is a monoclonal antibody (i.e., a biologic) that binds to free and membrane- bound tumor necrosis factor-α (TNF-α).19 It is administered at 5 or 10 mg per kilogram per infusion and has a half-life of 9 days.19 In retrospective cohort studies comparing the rates of colectomy among those who received cyclosporine with those who received infliximab, results have been mixed (Table 4). Further, aside from one study that included at least one adolescent (i.e., 12 years old),47 all have been limited to adults with ulcerative colitis and have had relatively small sample sizes. As a result, one treatment has not emerged as a clear choice over the other for second-line treatment for intravenous corticosteroid-refractory patients.
5.2.1 Lack of established standards of care for inpatient pediatric UC
Substantial variation in diagnostic and therapeutic intervention in the management 22 of pediatric IBD was found across 48 outpatient practices.17 Variation in care may suggest a lack of standard of care, or a lack of adherence to the standard of care, and thus lead to poor outcomes. For example, wide variation in a treatment or procedure may indicate that that treatment or procedure is being underused, overused, or misused.18 It is necessary to first identify variation in treatment in order to reduce inappropriate variation and to work toward establishing a standard of care. The variation in inpatient treatment for severe pediatric UC across institutions is widely unknown among gastroenterologists.
This is due in large part to the uncertainty of optimal evidence-based treatment for acute severe ulcerative colitis, specifically second-line therapy algorithms. There have been several studies investigating the effect of cyclosporine and infliximab for treatment of severe ulcerative colitis (Table 4). Small sample sizes and conflicting results of the effectiveness of either of these treatments have failed to clarify their potential role in the treatment of UC. Further, nearly all studies to date investigating these second-line therapies have been limited to adults with ulcerative colitis. This represents the ideal opportunity to add to the medical literature to advance the evidence of effective treatment algorithms targeted at the pediatric population with UC.
5.3 COMPARATIVE EFFECTIVENESS RESEARCH
The Institute of Medicine has recently identified 100 national priorities for quality comparative effectiveness research. Comparing “the effectiveness of different strategies of introducing biologics into the treatment algorithm for inflammatory diseases, including
[…] ulcerative colitis” is one of the top 25 priorities.48 Few high quality studies of treatment options for children and adolescents with severe UC exist. Further, studies are needed that investigate the treatment effect in populations of the same sex, race, age, or 23 with the same comorbidities as those seen clinically. Comparative effectiveness research focuses specifically on these comparisons to provide reliable guidance on the most effective treatment for the situation at hand.
Comparative effectiveness research in pediatric gastroenterology has been hindered by the relative infrequency of the disease and the challenge of enrolling children and adolescents in clinical trials. Although randomized controlled trials are generally regarded as providing the strongest evidence for the effect of a drug, they are not always possible due to ethical considerations or necessary enrollment size and criteria.
Determining optimal treatment modalities for pediatric UC will require multi-center randomized controlled trials capable of enrolling and following large cohorts of affected children and adolescents. Observational studies often provide an appropriate and advantageous alternative. Observational studies, such as the one proposed, are far more efficient and feasible to perform. Using appropriate quasi-experimental methods, observational studies are capable of providing valid estimates of treatment effects, specifically controlling for confounding by indication.49 A common threat to the validity of observational pharmacoepidemiologic study results is confounding by indication, whereby the severest group of patients receive more intensive treatment such as infliximab. These patients with high disease severity are more likely to experience poor outcomes, creating a spurious association between the intensive treatment and poor outcome. Once appropriately controlled for, results from such observational studies can identify the most promising treatment strategies deserving further investigation in a multi-center randomized clinical trial among children and adolescents with ulcerative colitis. 24
5.3.1 Limitations of current disease severity scores for UC
Disease severity is an important composite measure to consider in order to control for the severity of a patient’s condition, especially early on in their admission. Without adequate control for severity, especially early on in the admission prior to receipt of the treatment under study, confounding by indication may bias effect estimates of the treatment. This bias typically leads to underestimated effect estimate of the treatment due to the spurious association of treatment being administered to those of greater severity of disease.
Several disease activity, severity, and colectomy risk prediction models have been proposed for UC.21,22,28,40,50-52 All currently available models lack important characteristics that render their wide application to pediatric UC inappropriate. Until recently,40,52 all scores have been limited to adults. While the PUCAI is a validated measure that may be used to identify candidate patients for appropriate second-line therapies, it relies on patients’ clinical characteristics rarely available in large administrative databases. No such score has been developed for pediatric UC for use in the administrative data setting. Certain scores have been developed in adult UC to adequately model the risk of colectomy using administrative data, an important feature given the increasing interest paid to use of these data sources. However, previous reports using administrative data have significant limitations related to linking patients over multiple admissions and establishing temporality of the predictors preceding the outcomes. Ten years of the Pediatric Health Information Systems (PHIS) database is used in the three studies of this thesis in order to allow for a sufficient follow-up period by linking patients across readmissions to a PHIS-participating hospital. 25
Comparative effectiveness research in pediatric UC using large administrative data sets is currently not possible due to the inability to adequately control for disease severity at admission. Once a disease risk score has been developed for hospitalized cases of pediatric UC, quasi-experimental methods can be implemented to compare the effectiveness of various treatment modalities on inpatient outcomes using the PHIS database. The disease risk scores developed and implemented in the latter two studies demonstrate how this confounder summary score can be used to adjust for confounding y indication to estimate reliable treatment effects of medical therapy for children and adolescents with UC.
5.3.2 Data source
The Pediatric Health Information System (PHIS) served as the primary data source for all three studies. The PHIS database is a comprehensive, comparative pediatric database that contains clinical and financial details of more than six million patient cases.
Specifically, it contains the diagnosis and procedure codes and billed transaction and utilization data of inpatient and outpatient hospital encounters among 42 Children’s
Hospital Association (CHA) children’s hospitals nationwide. Member hospitals have access to the PHIS database through a data use agreement; the Children’s Hospital of
Philadelphia is a CHA-member hospital. Hospitals in PHIS represent most of the major metropolitan areas across the United States. There are two types of data contained in the
PHIS database. Level 1 data contain: encrypted patient identifiers, demographic information, dates of admission and discharge, physician profiles, clinical classification groupers, charge summaries, and up to 21 ICD-9-CM diagnosis and 21 procedure codes.
Level 2 data contain detailed information about the patient encounter including specific 26 daily financial and utilization data including: pharmacy, supply, laboratory, imaging, and clinical services.
The PHIS database is subject to numerous data quality and assurance processes before inclusion in the database. If data submitted by a member hospital do not achieve a data quality threshold (i.e., expected rates of various metrics), they are rejected, and the hospital is requested to reconcile the data before resubmission.53 The PHIS database is updated quarterly after all data quality and assurances have been met. With the quarterly data release, a report of known data issues for member hospitals (e.g., unreliable financial or clinical data, day of admission begins on day 1 versus day 0, missing admission diagnoses, etc.) provides a template for handling missing data and to determine if any hospital-years must be excluded from the analysis
5.4 STATISTICAL ANALYSES
The thesis was organized into three complimentary studies, each with its own main statistical analysis. The first study investigating management strategies for severe
UC in the inpatient setting was largely descriptive, reporting annual rates of treatment and inpatient outcomes across pediatric hospitals. The latter two studies involved the development and implementation of a disease risk score using the machine learning algorithm called the boosted classification and regression trees (CART) model. While other confounder summary scores are commonly used in epidemiologic studies (e.g., propensity scores), disease risk scores (DRS) are far less frequently used.
Common statistical computations made throughout the thesis included summary statistics such as means and medians, which were put into context by depicting the associated amount of variability in terms of total and interquartile ranges of values. 27
Linear tests for trends were calculated to identify patterns of management practices over the study period from 2003 to 2012. Two-tailed t-tests were performed to identify statistically significant differences in practices across various periods of the study (e.g., first 5 years compared to last 5 years of the study period) and differences in medication use and procedures performed between colectomy risk strata. Logistic regressions were fit to model dichotomous outcomes throughout the study. More advanced statistical approaches were involved in the development and implementation of the disease risk scores, which will be reviewed in detail in this section due to their relatively uncommon use among epidemiologists as a method to control for confounding.
5.4.1 Disease risk scores
The DRS represents the probability of disease occurrence in the absence of the main exposure of interest. If, given absence of exposure, the probability of disease occurrence is a known function of the covariates, then the probability of disease occurrence can be calculated for each cohort member, given absence of exposure. In an effort to standardize terminology surrounding DRS, the use of “disease occurrence” is understood to mean the occurrence of the primary outcome (e.g., myocardial infarction, diabetes, colectomy, etc.).54
5.4.1.1 DRS variable selection
The DRS is calculated by estimating the probability of disease occurrence in the cohort. The score is typically estimated among subjects unexposed to the main exposure/treatment of interest, although the DRS may be calculated using all members of the full cohort.55 Since the DRS represents the baseline risk among unexposed subjects, 28 time invariant covariates (e.g., sex and race) and covariates that occur during a prespecified baseline period (e.g., age, disease status/stage, insurance status, co- morbidities) should be considered for inclusion in the estimation of the DRS. In addition to including variables with a modest association with the outcome in the DRS model,56 variable selection should be guided by clinical and biological theories reported in the literature. Post-exposure variables (i.e., variables that occur consequent to exposure after the baseline period) should be avoided since inclusion in the DRS model will result in conditioning on intermediate variables.
5.4.1.2 Fitting the DRS model
Once variables have been selected, a DRS is modeled in the unexposed cohort.
Logistic regression modeling is commonly used in this step57 due to its familiarity among researchers. In addition, logistic regressions produce interpretable results that convert to the probability scale, making an intuitive application to the DRS. However, the validity of logistic regression is reliant upon its inherent parametric assumptions. These include the assumption of linearity of each parameterized covariate with the log-odds and the appropriate use of interaction terms. In practice, these assumptions are commonly ignored, rendering the results of the logistic regression (and subsequent application of the resulting DRS) susceptible to residual confounding and a biased estimate of the treatment association.57 Machine learning methods, such as boosted CART, are not constrained to these parametric assumptions and have been shown to outperform traditional logistic regression in applications such as propensity score weighting and G-computation.58-60
Given the inherent similarities between propensity scores and DRS, the use of methods 29 such as boosted CART to estimate DRS is a natural extension. See Lee et al59 and
McCaffrey et al58 for a discussion of boosted CART.
The boosted CART model is a tree-based machine learning algorithm. Tree-based methods use recursive partitioning to repeatedly subdivide a sample based on predictor variables into groups that are homogeneous in terms of the outcome.61 For example, if sex and age were predictors, two groups would be formed from males and females, each of which may subsequently be split into two groups based on an age cut point. This partitioning continues until the minimum group size or maximum number of splits is reached while maximizing homogeneity. The tree grows in complexity with each additional split, where the number of splits in a given branch represents the level of interactions between a given set of predictors (e.g., a branch split on sex and subsequently on age represents an interaction between sex and age). Of all the possible splits, the algorithm selects the split that minimizes the model’s prediction error.58 While a single regression tree may lack smoothness, resulting in poor prediction, the boosted
CART algorithm adds a linear combination of multiple regression trees to create a smooth curve, resulting in a reduction in prediction error.
The boosted CART algorithm can be designed to predict the occurrence of the primary outcome using available predictors present during a baseline period. Many of the considerations that must be taken when fitting logistic regressions are not necessary when using the nonparametric boosted CART approach. Assumptions of linearity between predictor variables with the log odds of the outcome are avoided in boosted CART.
Transformations of variables (e.g., log(age), age2, etc.) are not needed when using boosted CART, as the disease risk score adjustment will be the same regardless of how 30 each variable is transformed.58 CART algorithms can be used with categorical, ordinal, continuous, and missing data and are robust against the influence of outliers.59 While single CART algorithms are susceptible to overfitting due to their ability to model complex interactions and non-linearities, several methods can be used to avoid overfitting. These include pruning (i.e., limiting the complexity of interactions) and boosting (i.e., fitting multiple trees on random samples of the dataset).59
With traditional regression methods, 10 outcomes per covariate (i.e., individual variables and interaction terms) included in the model has become the rule.62,63 This is not the case when using boosted CART, thus avoiding the risk of excluding important confounders resulting in inadequate confounder control. The boosted CART model handles a large number of covariates, even if many of the covariates are correlated with one another or are unrelated to the outcome, without sacrificing estimate precision.58,61
This attribute allows for erring on inclusiveness in variable selection for the purposes of prediction models, where reducing prediction error is the primary goal. Nevertheless, parameterization of the research question must inform which variables to include in the model to the extent that temporality between exposures and outcome is respected.
5.4.1.3 DRS model assessment and diagnostics
Once a DRS is estimated for each unexposed member of the cohort, the performance of the DRS model must be assessed. If diagnostics reveal inadequate performance, the fitting of the DRS model should be revisited. In this way, these steps should be viewed as iterative until an acceptable model is fit. We discuss two diagnostic approaches to DRS model assessment that, while conceptually different, often perform similarly in practice (as we later show) to optimize DRS model selection. One approach 31 aims to minimize prediction error without overfitting; the other approach maximizes covariate balance across exposure groups.
Cross-validation is a method used in predictive modeling to assess how well the fitted model will perform on a new data set. Cross-validation is one safeguard against interpreting spurious associations identified using machine learning algorithms as truth.64
This process of cross-validation involves assessing the internal and external validity of the algorithm. If the DRS was estimated using data from the full cohort, an independent data set comprised of subjects not in the original cohort would be necessary for validation. Alternatively, the data set at hand can be randomly divided into equal parts.
For example, if the data set were divided into three parts, the DRS would be estimated using all but one part, leaving that part for validation of the predicted score. This would repeat twice more, each time leaving out the third part of the divided data set. Combining results from fitting the model three times on three non-identical data sets, overfitting is reduced.65 Cross-validation can be used to identify models which minimize prediction error, through metrics such as the Bernoulli deviance.
Bootstrapping is another method used to reduce overfitting. Bootstrapping uses multiple samples from the original data set, ultimately using the entire data set to fit the prediction model.65 Repeated samples are drawn from the study data set, with replacement, to estimate a parameter such as the regression coefficient.61,66 Each sample is equal in size to the study data set; thus, in a given sample, some observations appear multiple times, while others are omitted. The parameter estimate is then averaged over each sample to produce the final bootstrapped parameter.60 This bootstrapped statistic 32 represents that which would be obtained from repeated samples of the source population.61
The second approach to DRS model assessment is to maximize covariate balance across exposure/treatment groups. In other words, at similar values of the DRS, treated and untreated subjects will have similar distributions of baseline risk (i.e., determinants of the outcome at baseline), allowing for appropriate comparisons across groups. As with the goal of balancing confounders in the use of propensity scores, so too is the approach in the use of DRS. One method to assess the balance of potential confounders in the DRS is to calculate the average standardized absolute mean (ASAM) distance.59 The ASAM is calculated as the absolute difference of means, standardized by the standard error among the treated, between treatment groups for a given covariate. An ASAM distance is calculated for each covariate and an average taken across all ASAM distances. Lower values of ASAM distances indicate greater balance between treatment groups and has been shown to be correlated with the amount of bias in the final effect estimate.67 This composite measure summarizes how similar comparison groups are across potential confounders. The ASAM can be calculated in matched analyses as well as stratified analyses using the appropriate weights for the untreated group according to the implementation method of the DRS in the final model. Values less than 0.25 indicate adequate covariate balance.67
The c-statistics (or concordance statistic) is a measure of a model’s discriminatory ability. It is equivalent to the area under the receiver operator characteristic (ROC) curve and ranges from 0.5 (indicating that classification is no better than chance) to 1.0
(indicating perfect classification). Many epidemiologists warn against the use of the c- 33 statistic for variable selection in a confounder summary score, such as the propensity score.68,69 In certain settings, the c-statistic could actually be increased after the addition of an inappropriate variable (i.e., a non-confounder that is associated with exposure but not associated with the outcome in the case of a propensity score approach). The results may lead to a higher c-statistic but with less overlap of the propensity scores between exposure groups, an undesired characteristic of any confounder summary score.69 A high c-statistic is not necessary to ensure covariate balance. Instead the model assessment should focus on measures of covariate balance, such as the ASAM distance proposed above.
An additional means of verifying risk score suitability should evaluate the observed and predicted rates of disease occurrence across quantiles of risk. A graphical representation of outcome rates at each risk quantile should reveal increasing rates of disease occurrence at increasing quantiles, as well as any regions of the DRS where discrepancies may occur between observed and predicted outcome rates.70 If substantial differences between observed and predicted outcome rates are observed, the construction of the DRS model should be revisited.
5.4.1.4 Implementation of the DRS in the final model
Once a DRS is assigned to each member in the cohort, the DRS can then be implemented in a final model including the outcome and main exposure, much the same approach in propensity score methods. Other implementations of the DRS at this stage include k:1 matching with or without replacement, stratification, and inclusion in a doubly robust final model. The resulting measure of association from this final model represents the association of the main exposure with the outcome, adjusted for the 34 potential confounders included in the risk score. Just as the case is with propensity scores, adjustments made using a DRS do not control for unmeasured confounding.57 In addition to controlling for baseline risk of the outcome (e.g., colectomy), indicators of worsening clinical course during admission (i.e., receipt of intravenous corticosteroids, parenteral nutrient, opioids, blood transfusion, cyclosporine, tacrolimus, endoscopy, and mechanical ventilation) are included in the final regression modeling colectomy given exposure to infliximab to control for further confounding by indication during admission.
Matching exposure groups on DRS compares groups that are similar in terms of the baseline covariates that went into the risk score.67 Stratification by DRS quantiles is implemented by estimating a measure of association at each quantile of the risk score, while adjusting for other potential confounders occurring during the admission.
Obtaining effect estimates in each stratum of DRS quantile is a natural approach to investigate effect modification since it reveals whether the treatment effect is constant across risk strata. Attention should be given to identify any strata that may contain small numbers of outcome events so as to render an estimated measure of association in that stratum unreliable. The DRS can also be included in the final treatment effect model as a continuous covariate. However, the DRS is often not continuous, so incorporating the
DRS as categorical (i.e., quantiles of risk) is recommended.71
5.5 SUMMARY
In the studies that follow, infliximab is shown to have increased in use among pediatric patients with UC across a 10-year study period, while maintaining highly variability in its use across 42 US children’s hospitals. The development and implementation of a disease risk score is described in detail in the second study, allowing 35 wide spread adoption of this tool in future pharmacoepidemiologic studies. Finally, a disease risk score is developed to investigate the association of infliximab and the need for colectomy among children and adolescents with varying degrees of UC disease severity. Implications and recommendations from this body of work are discussed following the three manuscripts that were prepared for peer-reviewed scholarly journals.
Results from these studies provide the basis for further comparative effectiveness research among children with UC, ultimately identifying the most promising management practices in this population. 36
5.6 TABLES
Table 1. Modifications to the Montreal Classification for ulcerative colitis.26 Montreal Paris
Extent E1: ulcerative proctitis E1: ulcerative proctitis E2: left-sided UC (distal to E2: left-sided UC (distal to splenic splenic flexure) flexure) E3: extensive (proximal to splenic E3: extensive (hepatic flexure flexure distally) E4: pancolitis (proximal to hepatic flexure) Severity S0: clinical remission S0: never severe* S1: mild UC S1: ever severe* S2: moderate UC S3: severe UC LEGEND: *severe = PUCAI score ≥6540
Table 2. Classification of patients with ulcerative colitis Truelove & Witt Severity Index.19 Item Mild Moderate Severe Number of bloody stools per day (n) <4 4-6 >6 Temperature (°C) Afebrile Intermediate >37.8 Heart rate (beats/min) Normal Intermediate >90 Hemoglobin (g/dL) >11 10.5-11 <10.5 Erythrocyte sedimentation rate (mm/h) <20 20-30 >30
37
Table 3. Pediatric ulcerative colitis activity index (PUCAI).40 Points Item Abdominal pain No pain 0 Pain can be ignored 5 Pain cannot be ignored 10 Rectal bleeding None 0 Small amount only (< 50% of stools) 10 Small amount with most stools 20 Large amount with most stools 30 Stool consistency of most stools Formed 0 Partially formed 5 Completely unformed 10 Number of stools per 24 hours 0-2 0 3-5 5 6-8 10 >8 15 Nocturnal stools (any episode causing wakening) No 0 Yes 10 Activity level No limitation of activity 0 Occasional limitation of activity 5 Severe restricted activity 10 LEGEND: PUCAI score < 10 = remission; 10-29 = mild; 30-64 = moderate; ≥ 65 = severe.
38
Table 4. Summary of studies assessing effect of cyclosporine and infliximab. Design Inclusion criteria N Colectomy rate Study Lichtiger et al, 199436 RCT Adults admitted with severe UC 20 total: 18% CSP refractory to IV CS 11 CSP 44% placebo# 9 placebo D’Haens et al, 200134 RCT Adults admitted with severe UC 30 total: 12 months: 15 CSP 36% CSP 15 IV CS 40% placebo Sands et al, 200138 RCT Adults with severe UC refractory 11 total: 1 month: to IV CS 8 IFX 13% IFX 3 placebo 100% placebo# Kohn et al, 200247 Case series Adolescent(s) and adults with 13 IFX 3 days: severe UC refractory to IV CS 15% IFX Probert et al, 200337 RCT Adults with moderately severe UC 40 total: 6 weeks: refractory to IV CS 23 IFX 0% IFX 17 placebo 6% placebo Järnerot et al, 200513 RCT Adults with severe and fulminant 45 total: 29% IFX UC 24 IFX 67% placebo 21 placebo Croft et al, 200933 Prospective Adults admitted with acute severe 72 total: At discharge: cohort study UC refractory to IV CS 28 IFX 18% IFX 44 CSP 52% CSP* 12 months: 44% IFX 68% CSP* Dean et al, 201235 Retrospective Adults admitted with severe UC 38 total: 3 months: cohort study refractory to IV CS 19 IFX 21% (IFX) 19 CSP 63% (CSP)* 12 months: 37% (IFX) 68% (CSP) Sjöberg et al, 201239 Retrospective Adults admitted with moderate to 92 total: 3 months: cohort study severe UC refractory to IV CS 49 IFX 33% IFX 43 CSP 7% CSP 12 months: 43% IFX 27% CSP LEGEND: Only studies that measured rate of colectomy are included above. IFX: infliximab; IV CS: intravenous corticosteroids; CSP: cyclosporine; #early termination of trial *p-value < 0.05; ^decrease in the Mayo score of at least 3 points and at least 30%
39
6 TRENDS IN INPATIENT MANAGEMENT OF PEDIATRIC SEVERE
ULCERATIVE COLITIS: A RETROSPECTIVE US COHORT FROM 2003
TO 2012
Andrew J. Klink MPH1,2, Brian K. Lee PhD1, Robert N. Baldassano MD2,3, Lindsey G.
Albenberg DO2,3, Lucy F. Robinson PhD1, Alison A. Evans ScD1, and Judith R. Kelsen
MD2,3
1Department of Epidemiology & Biostatistics, Drexel University School of Public
Health; 2Division of Gastroenterology, Nutrition & Hepatology, The Children’s Hospital of Philadelphia; 3Department of Pediatrics, University of Pennsylvania Perelman School of Medicine
Short title: Management trends in pediatric UC
Address for correspondence: Andrew Klink, Drexel University, 3215 Market Street,
Philadelphia, PA 19104. Phone: 267-359-6104. Email: [email protected].
Funding: None
Word count: 3,893
40
6.1 ABSTRACT
Background: Although recent guidelines for severe UC management have been released, little is known about how US pediatric hospitals measure. This study aims to describe recent clinical management for severe UC exacerbations from 2003 to 2012 among 42
US pediatric hospitals and to assess trends across the 10-year period.
Methods: Annual rates of medications, labs, procedures, and outcomes were calculated for each hospital. Regressions were fit, weighted by the annual caseload of each hospital, to test for a linear trend in exposures and outcomes over the study period.
Results: There were 4,063 admissions among 2,921 patients with severe UC exposed to intravenous corticosteroids. Over the 10-year study period, length of stay decreased by
1.5 days (95% CI: 1.1-1.8 days, p-value<0.001), colectomy rate decreased by 3.9% (95%
CI: 2.7%-5.1%), p-value<0.001), while infliximab use increased by 1.5% (95% CI:
0.02%-3.1%, p-value=0.047) and was started earlier by 1.8 days (95% CI: 1.3-2.2 days, p-value<0.001). Testing for Clostridium difficile was gradually replaced by polymerase chain reaction detection in the final three years of the study period. Rates of C-reactive protein monitoring eventually matched rates of sedimentation rate monitoring in the second half of the study period.
Conclusions: This study represents a 10-year baseline period prior to rollout of UC management guidelines and can be used to compare management strategies in the future.
In final years of the study period, variation in the management for severe UC appears to be decreasing, a trend led largely by high caseload hospitals.
41
6.2 INTRODUCTION
Inflammatory bowel disease, composed of Crohn disease and ulcerative colitis
(UC), is one of the most common chronic gastrointestinal disorders affecting children and is rapidly increasing in incidence and prevalence.1,2,4-6 While approximately 15% of UC cases will experience the severest form of UC, characterized by severe diarrhea with rectal bleeding, dehydration, tachycardia, anemia, and fever necessitating immediate hospitalization, up to 28% of children and adolescents with UC may require hospitalization during a 3-year period.8,45
Children hospitalized with UC are exposed to a growing armamentarium of treatments in the inpatient setting.10 Although short-term use of intravenous corticosteroids has been used for improved outcomes among those admitted with severe
UC, approximately 30-40% will be refractory,11 requiring second-line treatment.
Historically, since their introduction in the 1950s for medical management of UC, corticosteroids have been widely used in pediatric UC.20,72 While the use of intravenous corticosteroids may be declining in recent years, our study covering a 10-year period of
2003 to 2012 captures a period during which corticosteroids was considered first-line treatment for acute UC exacerbations in the inpatient setting.73
A lack of consistent evidence exists as to what the optimal second-line therapy is.
While up to two-thirds of patients with severe UC may require colectomy,13 the use of infliximab or calcineurin inhibitors may delay or reduce the need for such surgery.14
However, the risks associated with these immunosuppressants need to be weighed against the risks associated with colectomy. It has been recently reported that the number of hospitalizations for IBD has increased over the past decade, while the proportion of 42 inpatients receiving an IBD-related surgery has decreased. When stratified by patient severity, the proportion of inpatients receiving an IBD-related surgery has not changed among the severest patients.15 Thus, we have yet to observe a decline in the rate of colectomy for severe UC, despite the availability of potential second line therapies.
Nevertheless, colectomy remains an option for the management of severe UC, particularly among patients necessitating second-line therapy.74
Until recently, consensus among pediatric gastroenterologists to treat children and adolescents with severe ulcerative colitis (UC) was lacking. Prior to the consensus guidelines presented by Turner and colleagues in 2011,42 the management of severe UC was largely extrapolated from the adult literature. The treatment of pediatric UC to date is thought to be highly variable, due in part to several treatment options available, the conflicting literature, and the limited clinical guidance to these approaches to patient care.75-77 One recent study of outpatient management of pediatric inflammatory bowel disease across 48 practices found substantial variation in diagnostic and therapeutic intervention.17 However, whether there is similar treatment variation in the US pediatric inpatient setting remains undocumented.78
This study aims to describe inpatient treatment for severe pediatric UC in US children’s hospitals over a 10-year period (2003 to 2012) with a focus on laboratory evaluation, medications, and procedures outlined in the recent guidelines for acute severe
UC requiring hospitalization.42 Although it is too early to fully evaluate the impact of the recent guidelines for the management of severe ulcerative colitis by Turner and colleagues,42 the present study highlights some important trends in inpatient treatment, evaluation, and outcomes since their dissemination in 2011. 43
6.3 MATERIALS & METHODS
6.3.1 Data source
The Pediatric Health Information System (PHIS) database is a comprehensive, comparative pediatric database that contains clinical and financial details of more than six million patient cases. Specifically, it contains the diagnosis and procedure codes and billed transaction and utilization data of inpatient and outpatient hospital encounters among 42 Children’s Hospital Association hospitals nationwide. Member hospitals, which include The Children’s Hospital of Philadelphia, have access to the PHIS database through a data use agreement. Hospitals in PHIS represent most of the major metropolitan areas across the United States.
There are two types of data contained in the PHIS database. Level 1 data contain: encrypted patient identifiers, demographic information, dates of admission and discharge, physician profiles, clinical classification groupers, charge summaries, and up to 21 ICD-
9-CM diagnosis and 21 procedure codes. Level 2 data contain detailed information about the patient encounter including specific daily financial and utilization data including: pharmacy, supply, laboratory, imaging, and clinical services. Data are de-identified and subjected to rigorous reliability and validity checks before inclusion in the database. Data that do not meet an established error threshold are rejected and must be corrected before resubmission.53
6.3.2 Study cohort
Patients 1-18 years old were included if they were admitted to a PHIS- participating hospital between January 1, 2003 and September 30, 2012 and had a 44 discharge diagnosis of ulcerative colitis (ICD-9-CM codes 556.x). To reduce misclassification of cases, patients were excluded if they had a dual discharge diagnosis of infectious colitis or Crohn’s disease (ICD-9-CM codes 009.x and 555.x; n=485). In addition to having a discharge diagnosis of ulcerative colitis, all patients in the cohort had a principal diagnosis of ulcerative colitis, which is assigned by the attending physician to designate the condition occasioning the admission. We defined acute severe UC as having 1) an admission priority of “emergency” or “urgent,” 2) exposure to intravenous corticosteroids during admission, and 3) a principal diagnosis of UC. Nine hospital-years of data (from 8 hospitals) were excluded due to missing clinical or financial data.
Medication exposures and laboratory studies ordered were identified by Clinical
Transaction Classification (CTC) codes; CTC codes are recorded for each day of hospitalization. Procedures were identified by ICD-9-CM procedure codes and matched to their day of service during the admission.
6.3.3 Statistical analysis
Categorical variables were summarized by frequency and percent, while continuous variables were summarized by median, interquartile range (IQR), and range.
Rates of medications, labs, and outcomes were calculated for each year of the study and a regression was fit, weighted by the annual caseload of each hospital, to test for a linear trend over the study period. In the event of nonlinear relationships of a variable across the study period, splines (i.e., piecewise regressions fit over a subset of year) where considered, and a comparison of means across the two periods (i.e., the two sets of years, e.g., 2003 to 2006 and 2007 to 2012) was performed using an unpaired t-test. A regression was fit to visually assess the impact of annual hospital caseload on colectomy 45 rates. A two-tailed p-value <0.05 was considered statistically significant. All analyses were conducted using Stata 12.1 (StataCorp, College Station, TX).
6.4 ETHICAL CONSIDERATIONS
This study was reviewed and approved by the internal review boards of The
Children’s Hospital of Philadelphia and Drexel University.
6.5 RESULTS
6.5.1 Study cohort
There were 4,063 admissions among 2,921 patients with UC to a PHIS- participating hospital between January 1, 2003 and September 30, 2013 (Table 1). The majority (62.2%) of the 4,063 admissions were index admissions for UC to a PHIS- participating hospital. Across the study period, the mean annual rate of severe UC admissions was 10.4 per 100,000 total admissions (range: 9.6-11.7). The annual rate of severe UC admissions remained stable across the study period (p-value=0.658). Patients were admitted a median of 1 time (IQR: 1-2), while less than 10% of patients were admitted 3 or more times during the study period for acute severe UC. Nearly half
(48.6%) were male and the vast majority (72.0%) was white, similar to other pediatric
UC cohorts.79,80 The median age at first admission during the study period was 14 years
(IQR: 11-16).
6.5.2 Participating hospitals
There were 348 hospital-years of data among 42 admitting hospitals representing all nine US census divisions during the 10-year study period. The mean 46 number of patients with severe UC admitted to each hospital was 96 (range: 27-234), while annually each hospital admitted an average of 10 (range: 1-55) patients with severe
UC. Hospitals included in the analysis had a mean of 70,209 (range: 5,997-233,263) annual admissions across all diagnoses.
6.5.3 Length of stay
The median length of stay was 6 days (IQR: 4-11) among admissions for severe
UC (Table 1). Weighted by annual caseload of each hospital, a linear regression estimated a reduction of 1.5 days (95% CI: 1.1-1.8 days, p-value<0.001) in mean length of stay across the entire 10-year study period (Fig. 1). The length of stay IQR across hospitals was shortest (i.e., difference between the 75th percentile and the 25th percentile for length of stay) in the final two years of the study, indicating years of least variability in length of stay. Those who received a colectomy during admission had a longer length of stay (mean: 21.5 days; 95% CI: 19.8-23.3 days) compared to those who did not receive a colectomy (mean: 7.8 days; 95% CI: 7.6-8.0 days; p-value<0.001).
6.5.4 Medications
Intravenous corticosteroids were consistently initiated by day 2 of admission in this cohort across the study period (median: day 0, IQR: 0-2; Table 2). Less than 10% of patients received intravenous corticosteroids beyond day 2. The duration of exposure to intravenous corticosteroids decreased by a mean of 2 days over the length of the study period (p-value<0.001).
While antibiotic use was common, ciprofloxacin was less common (used in 8.9% of admissions) than metronidazole (used in 42.7% of admissions; Table 2). The rate of 47 metronidazole decreased by a mean of 10.5% (95% CI: 8.1%-12.9%, p-value<0.001) across the study period (Table 2). During the same period, the rate of ciprofloxacin increased by 3.2% (95% CI: 1.7%-4.7%, p-value<0.001; Table 2). There was an increase in the use of ciprofloxacin in the period from the period 2003 to 2006 (mean: 5.6%, 95%
CI: 4.2%-6.9%) to the period 2007 to 2012 (mean: 10.0%, 95% CI: 8.9%-11.1%, p- value<0.001).
Infliximab was more widely used as a second line treatment (mean: 22.7%) than calcineurin inhibitors (cyclosporine mean use: 1.3%, tacrolimus mean use: 3.1%; Table
2). Although there was an increase of 1.54% (95% CI: 0.02%-3.05%, p-value=0.047) in the rate of infliximab use across the 10-year period, substantial variation among hospitals remained (Fig. 2A). By the end of the study period, infliximab was initiated earlier in the admission (mean reduction of 1.8 days over the 10-year period, 95% CI: 1.3-2.2 days, p- value<0.001; Fig. 2B). Over the study period, half of infliximab users started infliximab on or before day 5 (IQR: 2-9; Table 2). Cyclosporine use decreased over the study period from 2.4% in 2003 to less than 1% in 2011 (Table 2). Similar to ciprofloxacin use, tacrolimus use had two distinct periods within the 10-year study: 2003 to 2006 (mean:
0.8%, 95% CI: 0.2%-1.3%) and 2007 to 2012 (mean: 3.8%, 95% CI: 3.2%-4.5%, p- value<0.001; Table 2). The mean day of starting tacrolimus was later between 2007 and
2012 (mean: day 4.7, 95% CI: 3.8-5.6) compared to earlier years (i.e., 2003 to 2006, mean: 0.5, 95% CI: 0-1.1). The duration of exposure to tacrolimus was longer in the period between 2007 and 2012 (mean: 9.0 days, 95% CI: 7.6-10.5 days) compared to the period between 2003 and 2006 (mean: 3.6 days, 95% CI: 2.3-5.0 days, p-value=0.029). 48
Nearly 14% of patients with severe UC received non-steroidal anti-inflammatory drugs during admission, while the rate decreased 5.4% (95% CI: 4.1%-6.6%, p- value<0.001) across the study period (Table 2). Half of those receiving non-steroidal anti- inflammatory drugs received them by day 2 (IQR: 1-7). Beyond 2005, non-steroidal anti- inflammatory drugs were administered for 3.7 days on average, which was shorter compared to the previous 3-year period from 2003 to 2005 (mean: 7.0 days, 95% CI: 5.5-
8.4 days), p-value<0.001). Over half of the patients in this cohort received opioids (mean:
52.7%), and half of those who received opioids received opioids by the second day of admission (Table 2); less than 10% started opioids beyond day 6.
6.5.5 Diagnostic procedures and labs
The rate of upper endoscopy increased from 27.4% to 43.5% over the 10-year period (p-value<0.001; Table 3). Ninety percent of upper endoscopies were performed within the first 7 days of admission, and half were performed by day 2 (Table 3). Nearly all (98.2%) of the 1,480 inpatient upper endoscopies performed during the study period included biopsy (Table 3). Colonoscopy was more common, performed among 46.8% of the patients with severe UC (Table 3). Similarly, 98.3% of all inpatient colonoscopies performed on this cohort included biopsy.
Across the entire study period, stool specimens were tested for Clostridium difficile (i.e., culture and/or test for toxin by enzyme immunoassay or polymerase chain reaction [PCR]) in 81.1% of admissions for severe UC (Table 3). Testing for Clostridium difficile was gradually replace by PCR detection in the final three years of the study period. By 2012, PCR detection accounted for 73% of tests for Clostridium difficile. High rates of C-reactive protein and sedimentation rate testing were observed (70.3% and 49
82.7% respectively; Table 3). While rates of C-reactive protein testing were below 50% of admissions in the first three years of the study, rates of C-reactive protein and sedimentation rate monitoring were similar at 71%-82% of UC admissions in the remaining years. Over 75% of those receiving testing for Clostridium difficile, C-reactive protein, and sedimentation rate, had these labs drawn or prepared within the first 2 days of admission (Table 3). Nearly all (95.6%) patients in the cohort had a complete blood count performed; over 75% of them were completed on the first day of admission (Table
3). Less than half the cohort had labs drawn for an electrolyte panel (13.2%), albumin
(48.2%), or a liver function panel (17.6%) at any time during their admission (Table 3).
6.5.6 Surgical procedures
Since 2006, the rate of colectomy appears to have peaked in 2010 at 7.9 colectomies per 100 patients (Table 3). The vast majority of the 272 colectomies performed in this cohort during the study period were total colectomies (256 of 272,
94.1%; Table 3). Each year of the 10-year study period, a mean of 19 hospitals (58%; range 13-25 [35%-71%]) performed no colectomies among UC patients in this cohort.
The proportion of hospitals not performing colectomies in this cohort remained stable across the study (test for linear trend p-value=0.211). In a restricted analysis among hospitals that performed colectomies and that had more than 5 admissions for severe UC in a given year, a fitted linear regression, weighted by number of annual caseload at each hospital, estimated a decrease in the rate of colectomy among hospitals performing colectomies across the entire 10-year study period of 3.9% (95% CI: 2.7%-5.1%), p- value<0.001; Fig. 3). Further, a fitted regression shows an inverse association between the colectomy rate at a given hospital and the annual hospital caseload of severe UC 50 patients, suggesting that larger caseload hospitals on average have lower colectomy rates
(Fig. 4).
6.6 DISCUSSION
This large multicenter observational study of real life inpatient management of children and adolescents with severe UC revealed important trends in the use of medications, procedures, labs, and outcomes in the past decade. With few reports on US clinical inpatient practice among severe pediatric UC, we have little to use as a benchmark when comparing to disease management guidelines. This being one of the largest cohorts of severe UC inpatients provides a clear look into how clinicians have been treating this condition over the last 10 years. Moving forward, we can address management practices that are trending in the wrong direction and support those practices that are trending in the right direction.
Recent guidance on the management of acute severe UC among children and adolescents was presented by Turner and colleagues.42 While the recommendations achieved at least 95% consensus among IBD specialists, many remain “practice points,” representing common practice where evidence is lacking. Further, the study presented herein largely reports data during the decade preceding these proposed guidelines.
Prospective data will be needed to determine to what degree clinical management will be affected in light of these guidelines.
Nevertheless, this is an opportunity to put past clinical practice into context by comparing them to the current guidelines for the management of severe UC. In particular, the high rate of antibiotics use may not be warranted as their use is now recommended only among children with suspected infection or toxic megacolon, despite the increase in 51 severity and incidence of Clostridium difficile infections among IBD patients.81-83 One recent study placed the rate of Clostridium difficile infection at 25% among children admitted with IBD.84 Our study revealed that PCR detection gradually replaced previous testing methods for Clostridium difficile from 2010 to 2012, which coincided with a contemporary modest decline in the use of antibiotics. While limitations of the current study did not allow us to investigate results of Clostridium difficile testing, further work should investigate how antibiotics are currently being used in relation to the timing with
Clostridium difficile testing among children admitted with severe UC.
The use of non-steroidal anti-inflammatory drugs and opioids are not advocated in the guidelines, yet important segments of our inpatient UC cohort were exposed to non- steroidal anti-inflammatory drugs (13.9%) or opioids (52.7%) during their admission.
Exposures to both of these medications have remained relatively stable across the 10-year study period, with no appreciable decline in their use. While exposure to non-steroidal anti-inflammatory drugs is contraindicated in UC due to its association with disease activity exacerbation,85 the high rate of opioid use reported in this study may be an indication of the high level of disease severity in this cohort as their use at low doses may be appropriate in certain settings.42
During the 10-year study period, the rate of C-reactive protein monitoring started out at 31% but has matched the rate of sedimentation rate testing at around 80% of hospitalized cases of pediatric UC in recent years. Unlike the reported superior performance of C-reactive protein over sedimentation rate monitoring in Crohn disease, the role of C-reactive protein in UC has been described as additive to that of sedimentation rate monitoring.86,87 52
We report that 30.8% of patients received a second line treatment (i.e, infliximab or a calcineurin inhibitor), colectomy, or both during their admission. This coincides with other reports that corticosteroids may be initially effective in 70-90% of children with
UC,88 thus eliminating – at least temporarily – the need for second line treatment or colectomy. Of these second line treatments, calcineurin inhibitors were far less commonly used in this pediatric population compared to infliximab. In fact, 73.8% of those receiving a second line treatment received infliximab.
While the rate of colectomy by discharge among pediatric patients with severe
UC reported in our study was similar to others reported in similar settings,79,89 the rate of colectomy would likely be higher if we were to include patients being admitted for planned colectomies (e.g., admission priority of “elective”) or to include a follow-up period after discharge. Nevertheless, this study aims to depict inpatient management and outcomes and not that occurring in the outpatient setting. McAteer and colleagues recently reported factors associated with the receipt of colectomy among a similar cohort of pediatric UC patients.73 While our retrospective cohorts were both drawn from the
PHIS database, ours includes an additional three years of admissions and explicitly excludes elective admissions and idiopathic colitis. Their study reports a much lower rate of colectomy of 2.7%, whereas the rate observed in the current study is more than twice that rate (6.3% across entire study period). Due to our restriction to pediatric patients with
UC exposed to intravenous corticosteroids during admission, we are likely capturing a subset of their cohort that represents greater disease severity. Further, as we have illustrated, the rate of colectomy significantly decreased across the 10-year period, while maintaining substantial variation across hospitals. 53
This study begins to elucidate factors associated with exposure and outcome rates.
For example, a higher caseload of UC hospitalizations was associated with lower rates of colectomy at that hospital. Thus, it appears that children’s hospitals in the US that see a greater number of severe UC admission in a given year are leading the trend in reducing colectomy rates. Each year of the study period, between 67% and 100% of high caseload hospitals (i.e., those with > 20 severe UC admissions in a year) were at or below the median colectomy rate for that year. Future studies should investigate what it is about these hospitals (e.g., patient- and hospital-level characteristics) that are associated with receipt of colectomy as well as other exposures and outcomes described in this study.
Pediatric patients admitted for UC who are less than 5 years old represent a subgroup of patients requiring additional treatment and management considerations. In the cohort described herein, the 161 admissions among patients < 5 years old differed from those among patients 5-18 years old on several important inpatient outcome and exposure measures. Compared to study subjects 5-18 years old, those < 5 on average had a longer mean length of stay (10.3 days versus 8.6 days, p-value=0.014), later exposure to intravenous corticosteroids (day 2 versus day 1, p-value<0.001), later exposure to infliximab (day 7.7 versus day 5.9, p-value=0.046), earlier exposure to tacrolimus (day 0 versus day 5, p-value=0.020), and had a higher rate of flexible sigmoidoscopy (3.1% versus 1.2%, p-value=0.040). These differences in treatment practice across age groups may suggest that clinicians are more hesitant to initiate infliximab and to perform full colonoscopies (e.g., opting for flexible sigmoidoscopy instead) among very young patients. Whether these differences in treatment among very young patients are 54 associated with clinical outcomes should be investigated in future studies. Additionally, the safety and effectiveness of infliximab should be studied in this young population.
Our study results must be interpreted with several limitations in mind. As with any retrospective study utilizing administrative data, there is the potential for miscoding and misclassification of diagnoses, procedures, and medications. To limit this bias, we have utilized commonly described exposure and outcome definitions for use in administrative databases. Further, by applying the inclusion and exclusion criteria, we attempted to create a cohort of pediatric UC patients with similar disease severity to render comparisons across hospitals more appropriate. Nevertheless, all associations reported in this study are merely that – associations – and not to be interpreted as causal.
The current study is limited to inpatient data, so future studies are needed to assess both outcomes occurring after discharge and medication exposures occurring prior to and after admission. Although this study utilized a large multidimensional administrative database containing millions of patient records, we did not assess clinical measures including physician global assessments, pediatric ulcerative colitis activity index (PUCAI), and patient-reported outcomes (e.g., quality of life questionnaires, fatigue, anxiety, and depression). Since exposure to intravenous corticosteroids was used to define inclusion into this cohort, the rate of its use cannot be assessed in the current study. However, characteristics of intravenous corticosteroids administration (e.g., day of initiation and duration of exposure) were assessed across the study period. Lastly, it is important to consider how infliximab is increasingly being used as a corticosteroid-sparing therapy.20
As such, this study does not include patients with exacerbations of severe UC who were 55 not treated with intravenous corticosteroids (and perhaps treated with infliximab in their place).
This is one of the largest cohort studies investigating treatment practice in the inpatient setting among children and adolescents with UC, particularly among those with severe UC. Unlike results of controlled trials that have narrow applications to a group of patients with highly defined clinical characteristics not always seen in the real world, our results represent observed clinical practice among US healthcare workers to treat children with severe UC. As sufficient time has passed to allow for the implementation of the recent severe UC guidelines, additional follow-up studies are warranted to benchmark progress in severe UC management. This work may lead to a better understanding of optimal management of UC as well as to identify areas of further research to elucidate treatment effectiveness of current and emerging therapies.
56
6.7 REFERENCES
1. Kappelman MD, Rifas-Shiman SL, Kleinman K, et al. The prevalence and geographic distribution of Crohn's disease and ulcerative colitis in the United States. Clin Gastroenterol Hepatol. Dec 2007;5(12):1424-1429. 2. Kugathasan S, Hoffmann RG. The incidence and prevalence of pediatric inflammatory bowel disease (IBD) in the USA. J Pediatr Gastroenterol Nutr. 2004;39(Suppl):S48-S49. 3. Rufo PA, Bousvaros A. Current therapy of inflammatory bowel disease in children. Paediatr Drugs. 2006;8(5):279-302. 4. Henderson P, Hansen R, Cameron FL, et al. Rising incidence of pediatric inflammatory bowel disease in Scotland. Inflammatory bowel diseases. Jun 17 2011. 5. Lehtinen P, Ashorn M, Iltanen S, et al. Incidence trends of pediatric inflammatory bowel disease in Finland, 1987-2003, a nationwide study. Inflammatory bowel diseases. Aug 2011;17(8):1778-1783. 6. Rowe FA, Walker JH, Karp LC, Vasiliauskas EA, Plevy SE, Targan SR. Factors predictive of response to cyclosporin treatment for severe, steroid-resistant ulcerative colitis. Am J Gastroenterol. Aug 2000;95(8):2000-2008. 7. Turner D, Walsh CM, Benchimol EI, et al. Severe paediatric ulcerative colitis: incidence, outcomes and optimal timing for second-line therapy. Gut. Mar 2008;57(3):331-338. 8. Wong CK, Yacyshyn BR. Fulminant Ulcerative Colitis. Curr Treat Options Gastroenterol. Jun 2000;3(3):217-226. 9. Chaparro M, Burgueno P, Iglesias E, et al. Infliximab salvage therapy after failure of ciclosporin in corticosteroid-refractory ulcerative colitis: a multicentre study. Aliment Pharmacol Ther. Jan 2012;35(2):275-283. 10. Bradley GM, Oliva-Hemker M. Pediatric ulcerative colitis: current treatment approaches including role of infliximab. Biologics. 2012;6:125-134. 11. Hyams J, Markowitz J, Lerer T, et al. The natural history of corticosteroid therapy for ulcerative colitis in children. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. Sep 2006;4(9):1118-1123. 12. McAteer JP, Larison C, Wahbeh GT, Kronman MP, Goldin AB. Total colectomy for ulcerative colitis in children: when are we operating? Pediatr Surg Int. Jul 2013;29(7):689-696. 13. Jarnerot G, Hertervig E, Friis-Liby I, et al. Infliximab as rescue therapy in severe to moderately severe ulcerative colitis: a randomized, placebo-controlled study. Gastroenterology. Jun 2005;128(7):1805-1811. 14. Wilhelm SM, McKenney KA, Rivait KN, Kale-Pradhan PB. A review of infliximab use in ulcerative colitis. Clin Ther. Feb 2008;30(2):223-230. 15. Ananthakrishnan AN, McGinley EL, Binion DG, Saeian K. Physician density and hospitalization for inflammatory bowel disease. Inflamm Bowel Dis. Feb 2011;17(2):633-638. 57
16. Dayan B, Turner D. Role of surgery in severe ulcerative colitis in the era of medical rescue therapy. World J Gastroenterol. Aug 7 2012;18(29):3833-3838. 17. Turner D, Travis SP, Griffiths AM, et al. Consensus for managing acute severe ulcerative colitis in children: a systematic review and joint statement from ECCO, ESPGHAN, and the Porto IBD Working Group of ESPGHAN. Am J Gastroenterol. Apr 2011;106(4):574-588. 18. Kappelman MD, Bousvaros A, Hyams J, et al. Intercenter variation in initial management of children with Crohn's disease. Inflammatory bowel diseases. Jul 2007;13(7):890-895. 19. Rosen D, Kathy Hoffstadter T, Bao R, et al. Analysis of current treatments used in clinical practice in a pediatric summer camp population for children with inflammatory bowel disease. Inflammatory bowel diseases. Oct 2012;18(10):1818-1824. 20. Adler J, Sandberg KC, Shpeen BH, et al. Variation in infliximab administration practices in the treatment of pediatric inflammatory bowel disease. Journal of pediatric gastroenterology and nutrition. Jul 2013;57(1):35-38. 21. Colletti RB, Baldassano RN, Milov DE, et al. Variation in care in pediatric Crohn disease. J Pediatr Gastroenterol Nutr. Sep 2009;49(3):297-303. 22. Russell RK, Protheroe A, Roughton M, et al. Contemporary outcomes for ulcerative colitis inpatients admitted to pediatric hospitals in the United Kingdom. Inflammatory bowel diseases. Jun 2013;19(7):1434-1440. 23. Fletcher DM. Achieving data quality. How data from a pediatric health information system earns the trust of its users. J Ahima. Nov-Dec 2004;75(10):22- 26. 24. Turner D, Mack D, Leleiko N, et al. Severe pediatric ulcerative colitis: a prospective multicenter study of outcomes and predictors of response. Gastroenterology. Jun 2010;138(7):2282-2291. 25. Malaty HM, Abraham BP, Mehta S, Garnett EA, Ferry GD. The natural history of ulcerative colitis in a pediatric population: a follow-up population-based cohort study. Clin Exp Gastroenterol. 2013;6:77-83. 26. Rodemann JF, Dubberke ER, Reske KA, Seo da H, Stone CD. Incidence of Clostridium difficile infection in inflammatory bowel disease. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. Mar 2007;5(3):339-344. 27. Issa M, Vijayapal A, Graham MB, et al. Impact of Clostridium difficile on inflammatory bowel disease. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. Mar 2007;5(3):345-351. 28. Kim J, Smathers SA, Prasad P, Leckerman KH, Coffin S, Zaoutis T. Epidemiological features of Clostridium difficile-associated disease among inpatients at children's hospitals in the United States, 2001-2006. Pediatrics. Dec 2008;122(6):1266-1270. 29. Pascarella F, Martinelli M, Miele E, Del Pezzo M, Roscetto E, Staiano A. Impact of Clostridium difficile infection on pediatric inflammatory bowel disease. The Journal of pediatrics. Jun 2009;154(6):854-858. 58
30. Felder JB, Korelitz BI, Rajapakse R, Schwarz S, Horatagis AP, Gleim G. Effects of nonsteroidal antiinflammatory drugs on inflammatory bowel disease: a case- control study. Am J Gastroenterol. Aug 2000;95(8):1949-1954. 31. Vermeire S, Van Assche G, Rutgeerts P. C-reactive protein as a marker for inflammatory bowel disease. Inflammatory bowel diseases. Sep 2004;10(5):661- 665. 32. Turner D, Mack DR, Hyams J, et al. C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) or both? A systematic evaluation in pediatric ulcerative colitis. J Crohns Colitis. Oct 2011;5(5):423-429. 33. Ruemmele FM, Turner D. Differences in the management of pediatric and adult onset ulcerative colitis--lessons from the joint ECCO and ESPGHAN consensus guidelines for the management of pediatric ulcerative colitis. J Crohns Colitis. Jan 2014;8(1):1-4. 34. Gower-Rousseau C, Dauchet L, Vernier-Massouille G, et al. The natural history of pediatric ulcerative colitis: a population-based cohort study. Am J Gastroenterol. Aug 2009;104(8):2080-2088.
59
6.8 TABLES & FIGURES
Table 5. Demographic and clinical characteristics of the study cohort. N (%) or median (IQR) Patient characteristics N = 2,921 Male sex 1,419 (48.6) Number of admissions during study period 2 (1, 3) White race 2,102 (72.0) Age at first admission during study (years) 14.3 (11.3, 16.3)
Admission characteristics N = 4,063 Principal diagnosis of ulcerative colitis 4,063 (100.0) Length of stay (days) 6 (4, 11) Disposition Home/home health 4,006 (98.6) Transfer 36 (0.9) Left against medical advice 3 (<0.1) Expired 1 (<0.1) Missing 17 (0.4) Index admissions 2,528 (62.2) Readmissions 1,535 (37.8) Readmission within 30 days 405 (10.0) Government insurance 1,247 (30.7) ED admission 2,379 (58.6)
60
Table 6. Medication exposure characteristics. First day of Duration of Trend^ from exposure, exposure, days, 2003 to 2012 Medication n (%) median (IQR) median (IQR) Corticosteroids Intravenous* 4,063 (100.0) 0 (0, 2) 5 (3, 8) N/A Oral* 2,046 (50.4) 6 (3, 10) 2 (1, 3)
Antibiotics Metronidazole* 1,733 (42.7) 1 (0, 2) 6 (3, 10)
Ciprofloxacin* 360 (8.9) 1 (0, 4) 5 (3, 8)
Infliximab 923 (22.7) 5 (2, 9) 1 (1, 1)#
Calcineurin inhibitors Cyclosporine* 52 (1.3) 5 (0, 10) 10 (7, 16)
Tacrolimus* 124 (3.1) 3 (0, 8) 7 (4, 11)
Thiopurines AZA* 529 (13.0) 1 (0, 2) 4 (2, 7)
6-MP* 804 (19.8) 1 (0, 3) 4 (3, 7)
Mesalamine 1,774 (43.7) 1 (0, 3) 4 (2, 7)
Non-steroidal anti- 563 (13.9) 2 (1, 7) 3 (1, 5) inflammatory drugs
Opioids 2,141 (52.7) 1 (0, 3) 3 (1, 8)
LEGEND: *Not mutually exclusive; #duration of infliximab represents the days between infusions; ^Trend depicted using spark lines (y-axis fitted to minimum and maximum values) with the rates of the first and last years labeled. 61
Table 7. Procedures and laboratory studies performed. First day of Trend^ from exposure, 2003 to 2012 median n (%) (IQR) Procedure Endoscopy Upper endoscopy 1,507 (37.1) 2 (1, 4) + with biopsy 1,480 (98.2) 2 (1, 4)
Colonoscopy 1,902 (46.8) 2 (1, 4) + with biopsy 1,870 (98.3) 2 (1, 4)
Flexible sigmoidoscopy 53 (1.3) 4 (2, 9)
Colectomy Subtotal colectomy 19 (0.5) 13 (9, 21)
Total colectomy 256 (6.3) 9 (3, 15)
Laboratory evaluation C. difficile culture/toxin/PCR 2,980 (81.1) 0 (0, 1)
Electrolytes 486 (13.2) 0 (0, 1)
CRP 2,583 (70.3) 0 (0, 0)
ESR 3,040 (82.7) 0 (0, 0)
Albumin 1,771 (48.2) 0 (0, 2)
Liver function panel 645 (17.6) 0 (0, 2)
62
CBC 3,525 (95.9) 0 (0, 0)
LEGEND: PCR = polymerase chain reaction. ^Trend depicted using spark lines (y-axis fitted to minimum and maximum values for each procedure or lab) with the rates of the first and last years labeled.
63
Figure 1. Length of stay across the study period.
28 26 24 22 20 18 16 14 12 by hospital 10
Length of stay (days), 8 6 4 2 0 p-value<0.001 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
1-9 admissions Median across hospitals 10-20 admissions Interquartile range 21-55 admissions
LEGEND: Each circle represents the mean length of stay at a given hospital for the corresponding year. The fitted linear regression, weighted by number of annual caseload at each hospital, suggests a significant decrease in the mean length of stay across the entire 10-year period of 1.5 days (95% CI: 1.1-1.8 days, p-value<0.001). Five percent jitter was added to avoid overlapping hospitals with the same mean length of stay in a given year. 64
Figure 2. Infliximab use by year. A 60 55 50 45 40 35 30 25 by hospital 20 15 10 Infliximab (per 100 patients), 5 0 p-value=0.047
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
6-9 admissions Median across hospitals 10-20 admissions Interquartile range 21-55 admissions
B 28 p-value<0.001 26 24 22 20 18 16 14 12 by hospital 10 8
Infliximab initiation (day), 6 4 2 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
6-9 admissions Median across hospitals 10-20 admissions Interquartile range 21-55 admissions
LEGEND: Only hospitals that administered infliximab in a given year and had > 5 admissions for acute severe UC for that year were included in these figures. Each circle represents A) the mean rate of infliximab use, and B) the mean day infliximab was started at a given hospital for the corresponding year. Linear regressions, weighted by number of annual caseload at each hospital, suggest a significant increase in the use of infliximab by 1.54% (95% CI: 0.02%-3.05%, p-value=0.047) and earlier initiation by 1.8 days (95% 65
CI: 1.3-2.2 days, p-value<0.001) across the 10-year period. Five percent jitter was added to avoid overlapping data points.
66
Figure 3. Colectomy rates by year.
60 p-value<0.001 55 50 45 40 35 30 25
by hospital 20 15 10
Colectomy (per 100 patients), 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
6-9 admissions Median across hospitals 10-20 admissions Interquartile range 21-55 admissions
LEGEND: Subtotal and total colectomies are represented. Hospitals that did not perform any colectomies were excluded for that year, as well as hospitals that had fewer than 6 admissions for acute severe UC for that year (mean of 20 hospitals (58%) were dropped each year). The fitted linear regression, weighted by annual caseload at each hospital, suggests a significant decrease in the rate of colectomy among hospitals performing colectomies across the entire 10-year study period of 3.9% (95% CI: 2.7%-5.1%), p- value<0.001). Five percent jitter was added to avoid overlapping hospitals with the same colectomy rate.
67
Figure 4. Colectomy rate over annual hospital caseload.
60 2003-2007 2008-2012 50 Fitted regression with 95% CI
40
30
by hospital 20
Colectomy (per 100 patients), 10
0
0 5 10 15 20 25 30 35 40 45 50 55 60 Annual hospital caseload (number of UC patients)
LEGEND: Hospitals that did not perform any colectomies were excluded for that year, as well as hospitals that had fewer than 6 admissions for acute severe UC for that year. The fitted regression using a fractional polynomial model shows an inverse association between the hospital colectomy rate and caseload.
68
7 DEVELOPING AND IMPLEMENTING A DISEASE RISK SCORE TO
CONTROL FOR CONFOUNDING BY INDICATION IN
PHARMACOEPIDEMIOLOGIC STUDIES
Andrew J. Klink MPH1,2, Judith R. Kelsen MD2,3, Robert N. Baldassano MD2,3, Lindsey
G. Albenberg DO3, Lucy F. Robinson PhD1, Alison A. Evans ScD1, and Brian K. Lee
PhD1
1Department of Epidemiology & Biostatistics, Drexel University School of Public
Health; 2Division of Gastroenterology, Nutrition & Hepatology, The Children’s Hospital of Philadelphia; 3Department of Pediatrics, University of Pennsylvania Perelman School of Medicine
Short title: Disease risk score development & implementation
Address for correspondence: Andrew Klink, Drexel University, 3215 Market Street,
Philadelphia, PA 19104. Phone: (267) 359-6104; Email: [email protected]
Key words: confounding, pharmacoepidemiology, disease risk score, boosted, classification and regression trees (CART), logistic regression
Conflicts of interest: None
Word count: 4,636
69
7.1 ABSTRACT
Background: Controlling for confounding by indication is a substantial challenge in estimating pharmacological treatment effects from observational studies, and statistical techniques to address this confounding, such as propensity scores, have gained widespread adoption among epidemiologists. An analogous approach to the propensity score is the disease risk score (DRS), or prognostic score, which can be advantageous as compared to propensity scores in certain situations. We present a tutorial on estimating and implementing DRS.
Methods: We investigate whether early exposure to corticosteroids reduces the risk of colectomy in a national sample of children and adolescents with ulcerative colitis.
Disease risk scores were applied to account for confounding by indication. We guide the reader through all the steps of DRS adjustment, including model estimation using either logistic regression or boosted classification and regression trees (CART), validation, and implementation in the final model.
Results: After DRS adjustment, treatment with intravenous corticosteroids was associated with reduced risk of colectomy (OR=0.43, 95% CI=0.35-0.52). The DRS developed by boosted CART demonstrated similar performance compared to the DRS developed by logistic regression as measured by covariate balance in matched and stratified analyses.
Conclusion: The DRS may represent a superior alternative to other methods to control for confounding by indication in pharmacoepidemiologic studies, particularly when the exposure is rare or an emerging therapy, or when investigating effect modification of the exposure. DRS methods can be easily implemented in R. 70
7.2 INTRODUCTION
A common threat to the validity of observational pharmacoepidemiologic study results is confounding by indication, or confounding by severity,90 whereby the subgroup of patients with greatest disease severity receive more intensive treatment compared to those with lesser disease severity.91,92 These patients with high disease severity are more likely to experience poor outcomes, creating a spurious association between the intensive treatment and poor outcome. Thus, the severity of a patient’s condition is important to control for, especially early on in their admission.
Several tools have been used in prior pharmacoepidemiology studies to address confounding by indication: propensity scores,93,94 disease risk scores (DRS),56,95,96 restriction,97 comorbidity adjustment (e.g., Elixhauser and Charlson-Dayo comorbidity indices),98 and instrumental variables.99-101 Because disease risk scores are most similar to propensity scores and comorbidity adjustment approaches, we briefly compare these methods. Propensity score methods adjust for the probability of treatment assignment.102
In contrast, DRS (also known as prognostic score) methods adjust for the probability of outcome in the absence of exposure.71 In both DRS and propensity score techniques the summary scores are conditioned upon, via methods such as stratification or matching, to estimate the treatment effect. Comorbidity adjustment methods are similar in that they reduce a predefined list of common comorbidities into a single variable.98
Each of these methods to address confounding by indication has various advantages and disadvantages discussed in detail elsewhere.54,56,71,93,94,97-103 While all of the above methods have been used to control for confounding by indication, the use of
DRS has been relatively less common, particularly in comparison to propensity scores. 71
Moreover, there is less guidance in the literature concerning the optimal development and application of risk scores. Tadrous et al recently reviewed 86 articles that used DRS and concluded that there was a general lack in transparency in how the DRS were developed and implemented.54
In this manuscript, we provide a tutorial of developing and implementing a DRS, in a retrospective cohort study of the association between exposure to corticosteroids and risk of colectomy in a nationwide pediatric cohort of patients with ulcerative colitis.
Intravenous corticosteroids are typically given to those with greater disease severity, and disease severity is associated with greater risk of colectomy and other adverse outcomes.
We demonstrate the following steps in applying DRS methods: 1) variable selection for the DRS model; 2) fitting the DRS model; 3) diagnostics of the DRS model (e.g., assessing the relationship of the DRS with the outcome and cross-validation); and 4) adjustment for the DRS (e.g., quantiles of the risk score as a covariate in the regression model, matching on the risk score, and stratification on the risk score) to estimate the treatment effect. Logistic regression is compared to boosted classification and regression trees (CART) to describe the development and implementation of a DRS. Finally, we provide an overview of situations where it may be optimal to consider DRS approaches over other approaches such as propensity scores.
7.3 METHODS
The DRS represents the probability of disease occurrence in the absence of the main exposure of interest. If, given absence of exposure, the probability of disease occurrence is a known function of the covariates, then the probability of disease occurrence can be calculated for each cohort member, given absence of exposure. In an 72 effort to standardize terminology surrounding DRS, the use of “disease occurrence” is understood to mean the occurrence of the primary outcome (e.g., myocardial infarction, diabetes, colectomy, etc.).54
7.3.1 Variable selection
The DRS is calculated by estimating the probability of disease occurrence in the cohort. The score is typically estimated among subjects unexposed to the main exposure/treatment of interest, although the DRS may be calculated using all members of the full cohort.55 Since the DRS represents the baseline risk among unexposed subjects, time invariant covariates (e.g., sex and race) and covariates that occur during a prespecified baseline period (e.g., age, disease status/stage, insurance status, co- morbidities) should be considered for inclusion in the estimation of the DRS. In addition to including variables with a modest association with the outcome in the DRS model,56 variable selection should be guided by clinical and biological theories reported in the literature. Post-exposure variables (i.e., variables that occur consequent to exposure after the baseline period) should be avoided since inclusion in the DRS model will result in conditioning on intermediate variables.
7.3.2 Fitting the model
Once variables have been selected, a DRS is modeled in the unexposed cohort.
Logistic regression modeling is commonly used in this step57 due to its familiarity among researchers. In addition, logistic regressions produce interpretable results that convert to the probability scale, making an intuitive application to the DRS. However, the validity of logistic regression is reliant upon its inherent parametric assumptions. These include 73 the assumption of linearity of each parameterized covariate with the log-odds and the appropriate use of interaction terms. In practice, these assumptions are commonly ignored, rendering the results of the logistic regression (and subsequent application of the resulting DRS) susceptible to residual confounding and a biased estimate of the treatment association.57 Machine learning methods, such as boosted CART, are not constrained to these parametric assumptions and have been shown to outperform traditional logistic regression in applications such as propensity score weighting and G-computation.58-60
Given the inherent similarities between propensity scores and DRS, the use of methods such as boosted CART to estimate DRS is a natural extension. See Lee et al59 and
McCaffrey et al58 for a discussion of boosted CART.
With traditional regression methods, 10 outcomes per covariate (i.e., individual variables and interaction terms) included in the model has become the rule.62,63 This is not the case when using boosted CART, thus avoiding the risk of excluding important confounders resulting in inadequate confounder control. The boosted CART model handles a large number of covariates, even if many of the covariates are correlated with one another or are unrelated to the outcome, without sacrificing estimate precision.58,61
This attribute allows for erring on inclusiveness in variable selection for the purposes of prediction models, where reducing prediction error is the primary goal. Nevertheless, parameterization of the research question must inform which variables to include in the model to the extent that temporality between exposures and outcome is respected.
7.3.3 Model assessment and diagnostics
Once a DRS is estimated for each unexposed member of the cohort, the performance of the DRS model must be assessed. If diagnostics reveal inadequate 74 performance, the fitting of the DRS model should be revisited. In this way, these steps should be viewed as iterative until an acceptable model is fit. We discuss two diagnostic approaches to DRS model assessment that, while conceptually different, often perform similarly in practice (as we later show) to optimize DRS model selection. One approach aims to minimize prediction error without overfitting; the other approach maximizes covariate balance across exposure groups.
Cross-validation is a method used in predictive modeling to assess how well the fitted model will perform on a new data set. Cross-validation is one safeguard against interpreting spurious associations identified using machine learning algorithms as truth.64
This process of cross-validation involves assessing the internal and external validity of the algorithm. If the DRS were estimated using data from the full cohort, an independent data set comprised of subjects not in the original cohort would be necessary for validation. Alternatively, the data set at hand can be randomly divided into equal parts.
For example, if the data set were divided into three parts, the DRS would be estimated using all but one part, leaving that part for validation of the predicted score. This would repeat twice more, each time leaving out the third part of the divided data set. Combining results from fitting the model three times on three non-identical data sets, overfitting is reduced.65 Cross-validation can be used to identify models which minimize prediction error, through metrics such as the Bernoulli deviance.
The second approach to DRS model assessment is to maximize covariate balance across exposure/treatment groups. In other words, at similar values of the DRS, treated and untreated subjects will have similar distributions of baseline risk (i.e., determinants of the outcome at baseline), allowing for appropriate comparisons across groups. As with 75 propensity scores, the goal of the DRS approach is to balance potential confounders. One method to assess the balance of potential confounders in the DRS is to calculate the average standardized absolute mean (ASAM) distance.59 The ASAM is calculated as the absolute difference of means, standardized by the standard error among the treated, between treatment groups for a given covariate. An ASAM distance is calculated for each covariate and an average taken across all ASAM distances. Lower values of ASAM distances indicate greater balance between treatment groups and has been shown to be correlated with the amount of bias in the final effect estimate.67 This composite measure summarizes how similar comparison groups are across potential confounders. The
ASAM can be calculated in matched analyses as well as stratified analyses using the appropriate weights for the untreated group according to the implementation method of the DRS in the final model. Values less than 0.2 indicate adequate covariate balance.58
The c-statistic (or concordance statistic) is a measure of a model’s discriminatory ability. It is equivalent to the area under the receiver operator characteristic (ROC) curve and ranges from 0.5 (indicating that classification is no better than chance) to 1.0
(indicating perfect classification). Many epidemiologists warn against the use of the c- statistic for variable selection in a confounder summary score, such as the propensity score.68,69 In certain settings, the c-statistic could actually be increased after the addition of an inappropriate variable (i.e., a non-confounder that is associated with exposure but not associated with the outcome in the case of a propensity score approach). The results may lead to a higher c-statistic but with less overlap of the propensity scores between exposure groups, an undesired characteristic of any confounder summary score.69 A high c-statistic is not necessary to ensure covariate balance. Instead the model assessment 76 should focus on measures of covariate balance, such as the ASAM distance proposed above.
An additional means of verifying risk score suitability should evaluate the observed and predicted rates of disease occurrence across quantiles of risk. A graphical representation of outcome rates at each risk quantile should reveal increasing rates of disease occurrence at increasing quantiles, as well as any regions of the DRS where discrepancies may occur between observed and predicted outcome rates.70 If substantial differences between observed and predicted outcome rates are observed, the construction of the DRS model should be revisited.
7.3.4 Implementation in the final model
Once a DRS is assigned to each member in the cohort, the DRS can then be implemented in a simple model including the outcome and main exposure, much the same approach in propensity score methods. Other implementations of the DRS at this stage include k:1 matching, stratification, and inclusion in a doubly robust final model.
The resulting measure of association from this final model represents the association of the main exposure with the outcome, adjusted for the potential confounders included in the risk score. Just as the case is with propensity scores, adjustments made using a DRS do not control for unmeasured confounding.57
Matching exposure groups on DRS compares groups that are similar in terms of the baseline covariates that went into the risk score.67 Stratification by DRS quantiles is implemented by estimating a measure of association at each quantile of the risk score.
Obtaining effect estimates in each stratum of DRS quantile is a natural approach to investigate effect modification since it reveals whether the treatment effect is constant 77 across risk strata. Attention should be given to identify any strata that may contain small numbers of outcome events so as to render an estimated measure of association in that stratum unreliable. The DRS can also be included in the final treatment effect model as a continuous covariate. However, the DRS is often not continuous, so incorporating the
DRS as categorical (e.g., deciles of risk) is recommended.71
7.3.5 Case study
To illustrate the development and application of a DRS, we examine whether the use of intravenous corticosteroids is associated with risk of colectomy in a sample of children and adolescents hospitalized with ulcerative colitis. While up to two-thirds of patients with severe ulcerative colitis may require colectomy (thereby increasing the potential for adverse sequelae including surgical complications, reduced fertility among female patients, and psychological burdens),13 medical therapy may delay or reduce the need for such surgery.14 Children hospitalized with ulcerative colitis are exposed to a growing armamentarium of treatments in the inpatient setting, including intravenous corticosteroids.10 Although short-term use of intravenous corticosteroids has been used for improved outcomes among those admitted with severe ulcerative colitis, approximately 30-40% of patients will be refractory,11 requiring second-line treatment.
The Pediatric Health Information System (PHIS) served as the primary data source to develop the disease severity score. The PHIS database contains clinical and financial details of more than six million patient cases. Specifically, it contains the diagnosis and procedure codes and billed transaction and utilization data of inpatient and outpatient hospital encounters among 42 children’s hospitals nationwide. Hospitals in 78
PHIS represent 70% of all freestanding pediatric hospitals in the US and most of the major metropolitan areas across the United States.104
There are two levels of data contained in the PHIS database. Level 1 data contain encrypted patient identifiers, demographic information, dates of admission and discharge, physician profiles, clinical classification groupers, charge summaries, and up to 21 ICD-
9-CM diagnosis and 21 procedure codes. Level 2 data contain detailed information about the patient encounter including specific daily financial and utilization data from pharmacy, supply, laboratory, imaging, and clinical services.
Variables available in the PHIS data set were included in the DRS model if they were time-invariant or present on the first 2 days of hospitalization. Predictors were limited to days 1-2 of hospitalization to avoid further confounding by severity since exposure to certain medications or procedures after day 2 are likely due to a worsening clinical course. The resulting model represents a baseline risk of colectomy within 48 hours of admission among children and adolescents with ulcerative colitis. Both patient- and hospital-level variables were included in the DRS. Variables were excluded if they represented a feature that may have occurred after day 2 of hospitalization or whose temporality could not be established given the available data (e.g., comorbid discharge diagnosis of volume depletion is not assigned a date of diagnosis and could likely occur during admission as a result of worsening clinical course).
DRS models estimated using the boosted CART algorithm were fit with the
“gbm” (generalized boosted regression) package105 in R version 2.15.13 (R Foundation for Statistical Computing, Vienna, Austria). Parameters for the boosted CART are described below and were guided by work of McCaffrey and colleagues.58 McCaffrey et 79 al have found that 20,000 iterations are usually sufficient. Here, we found that a higher number of iterations was ideal, although relative gains were minimal beyond 20,000. The number of trees (i.e., iterations) to fit was set at 40,000 (n.trees = 40,000), allowing for 4- way interactions (interaction.depth = 4), with a minimum node size of 10
(n.minobsinnode = 10), at a learning rate of 0.0005 (shrinkage = 0.0005). Friedman’s random sub-sampling was set at 0.5 (bag.fraction = 0.5). A 3-fold cross-validation was performed to obtain the final boosted CART model (cv.folds = 3). A logistic regression was fit among subjects unexposed to intravenous corticosteroids using the “glm” function
(family = binomial) to model the probability of colectomy using the same covariates used in the boosted CART model. See the Appendix for example R code used in this analysis.
A DRS was assigned to each subject in the cohort using results from boosted
CART and the logistic regression. The ASAM distance was calculated for the DRS model derived by boosted CART and logistic regression according to the DRS implementation method in the final model (e.g., matching or stratification). The observed and predicted rate of colectomy in each decile was calculated for the DRS derived by boosted CART and by logistic regression to assess model fit across all regions of the
DRS.
To estimate the association between intravenous corticosteroids and colectomy, the DRS by boosted CART and by logistic regression were 1) included as a continuous and categorical covariate in a final model, 2) included as a categorical covariate in a doubly robust model (i.e., logistic regression modeling colectomy given all 39 covariates and the DRS), 3) used to match across exposure groups, and 4) classified as deciles and used in a stratified analysis. The resulting point estimates and their 95% confidence 80 intervals from each of the final models (i.e., boosted CART and logistic regression) were compared.
Data analyses were performed using R 2.15.13, while data management was previously performed using Stata 12.1 (StataCorp, College Station, TX). This study was reviewed and approved by the Children’s Hospital of Philadelphia institutional review board committee for the protection of human subjects.
7.4 RESULTS
Of the 8,371 subjects, 3,068 (36.7%) were unexposed to intravenous corticosteroids in the cohort and were included in the DRS models (i.e., boosted CART and logistic regression). In the unexposed cohort, 317 (12.1%) subjects had a colectomy during the study period. Of the 107 variables in the data set, 39 variables were time- invariant (e.g., sex, admitting hospital, insurance type, etc.) or indicated an exposure within the first 2 days of hospitalization (e.g., exposure to opioids on day 1). The absolute values of all exposure-confounder correlations were < 0.26 (all but one – attending physician subspecialty (e.g., gastroenterology) – had correlation coefficients < 0.17).
The boosted CART algorithm completed 40,000 iterations in 13 minutes using a
MacBook Pro OS X (version 10.5.7) with a dual Intel core i7 processor running at 2.7
GHz with 8 GB of memory. The optimal number of trees as determined by 3-fold cross- validation was 21,465 (Fig. 1), which is the iteration at which the loss function (i.e.,
Bernoulli deviance) was minimized to 0.3907.
The 10 most influential predictors of colectomy identified by the boosted CART were the admitting hospital, an admission diagnosis of ulcerative colitis, the attending physician’s subspecialty (e.g., gastroenterology), subject’s admission priority (e.g., 81 urgent, emergency, or elective), month and year of admission, age at admission, and exposure to propofol, midazolam, and/or ondansetron during the baseline period (Table
1). Fourteen of the 39 variables were statistically significant predictors of colectomy in multiple logistic regression (Table 1).
Using the boosted CART results from the optimal number of trees by 3-fold cross-validation, predicted log-odds were assigned to all subjects in the cohort. These log-odds ranged from -4.58 to 4.00, corresponding to probabilities of colectomy from
0.01 to 0.98. Fitting the logistic regression among the unexposed cohort, the outcome
(i.e., colectomy) did not occur at 4 hospitals, during 1 year of the study period, and among those exposed to cyclosporine on days 0-1, involving a total of 289 observations.
As such, regression coefficients for these 6 variables were not estimated by the logistic model. The log-odds of colectomy estimated by the multivariate logistic regression ranged from -40.10 to 4.47, corresponding to probabilities from < 0.001 to 0.99.
Comparing observed and predicted rates of colectomy in each disease risk decile using boosted CART revealed similar rates across deciles 1-9 and overestimation of the rates in decile 10 (Fig. 1A). Assessing the DRS derived by logistic regression, predicted colectomy rates were underestimated in deciles 1-7 and overestimated in deciles 8-10
(Fig. 1B). In both the boosted CART and logistic regression approaches, the rate of colectomy increased across increasing deciles of the DRS.
The unadjusted odds ratio of intravenous corticosteroid exposure on the receipt of colectomy was 0.34 (95% CI: 0.29-0.41). Implementations of the DRS in the final model to estimate the treatment effect of intravenous corticosteroids on the receipt of colectomy are summarized in Table 2. In unstratified analyses, treatment effect estimates adjusted 82 for the DRS derived from boosted CART were attenuated toward the null as compared to estimates adjusted for the DRS derived from logistic regression (except for the doubly robust model). Odds ratios adjusted for the DRS by boosted CART and logistic regression were attenuated away from the unadjusted odds ratio and toward the null in all unstratified analyses. The strongest treatment effect in unstratified analyses was when the
DRS was implemented as a categorical variable (i.e., deciles of risk) in the logistic regression approach and when the DRS was included in a doubly robust model in the boosted CART approach. Odds ratios and their corresponding confidence intervals were similar across unstratified analyses implementing the DRS by boosted CART and logistic regression (Table 2).
Stratified analyses revealed equal or increased odds of colectomy among those exposed to intravenous corticosteroids compared to those unexposed in risk strata ≤ 7
(individual strata not shown). Reduced odds of colectomy among those exposed to intravenous corticosteroids were observed in the top 2 risk strata (p-value < 0.001 for both boosted CART and logistic regression approaches). Fig. 2 provides a visual depiction of the rates of colectomy among those exposed and unexposed to intravenous corticosteroids across risk strata. Individual DRS deciles < 8 contained very low counts of colectomy, particularly among the untreated. To summarize the treatment effect across disease risk deciles containing at least 10 events in either treatment group (i.e., deciles 8-
10), the odds ratio of colectomy among those treated with intravenous corticosteroids versus those untreated was 0.21 (95% CI: 0.17-0.26) and 0.19 (95% CI: 0.15-0.24) using boosted CART and logistic regression, respectively. 83
7.5 DISCUSSION
In this paper, we have demonstrated how to develop and implement a DRS to estimate the treatment effect to adjust for confounding by indication in a pharmacoepidemiologic study. Specifically, guidance on variable selection for the DRS, fitting of the risk score model, assessment of the risk score model, and adjustment in the final model using the risk score have been provided. Throughout this tutorial, approaches using boosted CART and traditional logistic regression were compared, suggesting marginally superior performance using the boosted CART model to construct the DRS.
There were differences in fitting the DRS model by boosted CART compared to logistic regression that ultimately had important impacts on the final treatment effect estimates. The first is model parameterization. While boosted CART models do not require assumptions about the distribution of the covariates, logistic regression models assume a linear relationship between each covariate and the log-odds of the outcome.
This assumption warrants further investigation into each covariate to determine whether exponentiation, log transformations, interaction terms, or splines most appropriately model otherwise nonlinear relationships. In practice, this assumption is often overlooked or downplayed, potentially resulting in notable residual confounding of the effect estimates (e.g., point estimates and standard errors of the estimates).102 Missingness among covariates –even when missing completely at random— can also be problematic in logistic regression, effectively reducing the sample size when log odds are estimated for the entire cohort. Boosted CART on the other hand, does not require these model assumptions and successfully models nonlinear relationships, missingness, and complex interaction terms without overfitting the data. Thus, the boosted CART approach 84 preserves the maximum sample size, which avoids added bias of the effect estimates due to using complete cases only, as well as impacting the estimated standard errors around the estimates.
Model fit was assessed both quantitatively using the ASAM distance and cross- validated prediction error and qualitatively by visual inspection of the observed versus predicted outcome rates across the range of risk strata. The comparison of ASAM distances between logistic regression and boosted CART approaches revealed similarly small values (all < 0.20 in matched and stratified analyses), suggesting adequate covariate balance was achieved across treatment groups. The logistic regression and boosted CART risk scores both depicted increasing rates of colectomy across increasing risk scores.
However, more discrepancies were noted between observed and predicted colectomy rates in the logistic regression DRS in terms of magnitude and frequency (i.e., how often the discrepant rates occurred across risk strata). This underestimation of outcome in lower risk strata and overestimation in higher risk strata likely contributed to biased effect estimates and the corresponding standard errors, rendering the estimates from logistic regression less precise and accurate than those from the boosted CART model.
In unstratified analyses, adjustment for the DRS resulted in statistically significant odds ratios < 1.0. Conversely, stratified analyses both graphically and computationally revealed inconsistent treatment effects across risk strata. At lower risk strata, exposure to intravenous corticosteroids was associated with increased odds of colectomy, whereas at higher risk strata, exposure was associated with reduced odds of colectomy. This scenario represents an example of how DRS implementation may be used to identify effect modification. Nevertheless, caution must be made to further investigate the potential for 85 effect modification. In our case study, the effect estimates in the lower strata appear to be a result of small numbers of the outcome across treatment groups and not due to effect modification. These small cell counts resulted in the inability to estimate an effect estimate for that stratum (e.g., deciles 1-7) or with imprecise estimates depicted by wide confidence intervals. There are several approaches to consider in this situation.
Reevaluate the inclusion and exclusion criteria for the cohort under study. In the case study presented above, the cohort consisted of a wide range of disease severity, and as such, the risk of colectomy was only appreciable in approximately 20% of the cohort
(i.e., only the top 2 deciles of DRS had probabilities of colectomy > 0.10). Second, consider combining strata or using fewer risk strata (e.g., quartiles) to accurately depict the treatment effect, remaining mindful of any strata that may render an estimated measure of association questionable. Finally, as is in the case study at hand, reporting the treatment effect by stratum may not be warranted and/or appropriate. Rather, results from a matched analysis may best depict the true effect of the treatment.
The use of DRS in the epidemiologic literature has been less common in comparison to propensity scores. Nevertheless, there are several settings in which DRS may be more advantageous than the others. For example, when the exposure is polytomous (e.g., non-use, low-dose, high-dose), propensity score methods become more difficult to implement whereas DRS simply use a polytomous exposure in the final model. DRS are a natural tool with which to investigate effect modification,106 a phenomena difficult to address using propensity scores. The differences in the rates of outcome across quantiles of the risk score can reveal important nonlinear trends attributable to the exposure. Also, when the exposure is an emerging therapy and thus 86 prescriber preferences have not stabilized (thus making it difficult to accurately model the probability of treatment), the choice of using DRS over propensity scores has clear advantages.56 In other settings, e.g., when there is a high exposure-confounder correlation present, DRS may not be appropriate. Propensity scores are also considered superior to
DRS when the outcome is rare, due to the relative inability to fit a model of the outcome.71
In settings where DRS are advantageous, boosted CART modeling should be considered. One barrier for the adoption of this method may be due in part from its lack of clinical interpretability (e.g., relative influence measure and not the commonly recognized odds ratios). Nevertheless, considering the goal at hand of modeling a DRS function to induce covariate balance, the lack of direct clinical interpretability should not be a concern.107 In the case of the DRS, a measure of association, e.g., odds ratio, relative risk, etc. is not warranted. Rather, it is to serve as an adjustment tool through its implementation in the final model, or upon which stratification can be performed.
We have presented a guide for the development and implementation of DRS for pharmacoepidemiologic studies. DRS can be estimated in a wide range of statistical software, such as R, as we have shown. Along with a suite of other confounding adjustment methods, DRS methods should be in the toolbox of epidemiologists who are often faced with the problem of confounding by indication.
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7.6 TABLES & FIGURES
Table 8. Baseline covariates in the DRS model. Boosted N (%) or CART Logistic regression median Relative Predictor (IQR) influence* OR (95% CI) Demographics Age, years 14 (11-16) 0.76 1.02 (0.97-1.06) White 5,923 (70.8) 0.04 0.80 (0.52-1.25) Male 4,333 (51.8) 0.04 0.99 (0.70-1.39)
Admission characteristics Gastroenterology attending 5,112 (61.1) 20.59 0.06 (0.03-0.11) Admission diagnosis of UC 3,058 (36.5) 22.41 12.27 (8.28-18.20) Admission priority - 15.66 - - Emergency 3,924 (46.9) - Ref. - Urgent 2,849 (34.0) - 2.41 (1.37-4.25) Elective 1,306 (15.6) - 5.39 (3.14-9.23) Not recorded 292 (3.5) - 0.64 (0.14-2.95) Admitting hospital 42 24.97 - - Admission year 10 2.19 - - Admission month 12 4.32 - - Government insurance 2,619 (31.3) 0.43 0.50 (0.33-0.77) Readmission 3,633 (43.4) 0.08 0.97 (0.67-1.39)
Procedures on days 0-1 Transfusion 189 (2.3) 0.26 5.28 (1.63-17.08) Mechanical ventilation 74 (0.9) 0.11 2.03 (0.63-6.54) Endoscopy 490 (5.9) <0.01 1.15 (0.44-2.97)
Medications on days 0-1 Propofol 1,185 (14.2) 2.01 1.54 (0.91-2.59) Midazolam 1,107 (13.2) 1.54 1.53 (0.97-2.42) Opioids 3,330 (39.8) 0.66 1.32 (0.84-2.09) Ondansetron 3,037 (36.3) 1.17 1.86 (1.24-2.79) Parenteral nutrition 474 (5.7) 0.69 3.37 (1.62-7.02) Metronidazole 2,185 (26.1) 0.74 1.83 (1.21-2.78) Lidocaine 1,731 (20.7) 0.57 1.71 (1.11-2.64) Oral prednisone 928 (11.1) 0.04 1.43 (0.83-2.46) Ranitidine 1,249 (14.9) 0.27 1.39 (0.83-2.32) NSAIDs 1,047 (12.5) 0.04 0.61 (0.39-0.96) Oral mesalamine 2,016 (24.1) 0.06 0.37 (0.19-0.74) Acetaminophen non-narcotic combo 2,966 (35.4) 0.03 0.86 (0.58-1.27) Diphenhydramine 1,218 (14.6) 0.02 0.75 (0.47-1.22) Budesonide 153 (1.8) <0.01 0.23 (0.05-1.09) 88
Lansoprazole 1,471 (17.6) 0.02 0.62 (0.30-1.25) Ferrous 1,110 (13.3) 0.23 2.93 (1.55-5.53) Ciprofloxacin 473 (5.7) 0.01 0.53 (0.20-1.44) Sulfasalazine 430 (5.1) 0.00 0.35 (0.12-1.01) Tacrolimus 216 (2.6) 0.00 0.48 (0.14-1.72) 6-MP 845 (10.1) 0.00 0.74 (0.25-2.16) AZA 671 (8.0) 0.00 1.05 (0.41-2.67) Ciclosporin 48 (0.6) 0.00 1.00 N/A Infliximab 276 (3.3) 0.00 0.45 (0.09-2.33) Rectal mesalamine 338 (4.0) 0.00 0.48 (0.06-4.09) Methotrexate 45 (0.5) 0.00 4.87 (0.38-62.89) LEGEND: “-” Individual odds ratios were calculated for each level of the variable in logistic regression and one relative influence was calculated considering all levels of the variable in boosted CART. *Relative influence represents the proportion of the total reduction in prediction error of the model attributable to each variable!and sums to 100 for all predictors.
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Table 9. Treatment effect estimates. Boosted CART# Logistic regression Implementation method OR (95% CI) ASAM OR (95% CI) ASAM Unadjusted model (no DRS) - - 0.34 (0.29-0.41) - DRS as continuous variable in logistic 0.50 (0.41-0.62) - 0.41 (0.34-0.49) - regression DRS deciles as categorical variable in 0.43 (0.35-0.52) - 0.40 (0.33-0.48) - logistic regression Doubly robust model+ 0.41 (0.33-0.51) - 0.46 (0.36-0.57) - Matching on the DRS 1:1 nearest neighbor with 0.50 (0.41-0.60) 0.06 0.48 (0.40-0.59) 0.06 replacement Stratification on DRS decile DRS decile 1-7* 6.20 (2.86-13.45) 0.13 2.90 (1.77-4.76) 0.12 DRS decile 8 2.11 (0.96-4.67) 0.17 1.33 (0.61-2.92) 0.14 DRS decile 9 0.53 (0.33-0.83) 0.15 0.40 (0.26-0.63) 0.17 DRS decile 10 0.12 (0.09-0.16) 0.13 0.11 (0.08-0.15) 0.13 LEGEND: ASAM = average standardized absolute mean distance; DRS = disease risk score; OR = odds ratio; CI = confidence interval; Odds ratio are derived from the β coefficient from fitting a logistic regression modeling the log odds of colectomy given IV CS exposure and adjusting for the DRS. #The boosted CART model with the minimum Bernoulli deviance was used to calculate the DRS. +Doubly robust model estimated the odds of colectomy given all 39 baseline covariates used in the DRS as well as the DRS as a categorical variable. *Deciles 1-7 were collapsed, because they contained <5 outcomes among those unexposed to IV CS, resulting in unstable OR estimates. The DRS was included as a categorical variable to adjust for differences in baseline risk in deciles 1-7.
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Figure 5. Observed and predicted colectomy rates across risk deciles. A 70 Observed Predicted 60
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 9 10 Risk decile
B 70 Observed Predicted 60
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 9 10 Risk decile
LEGEND: The observed and estimated rates of colectomy are shown for each decile of the DRS fit by booted CART (A) and logistic regression (B).
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Figure 6. Rate of colectomy by exposure status. A 70 No IV CS IV CS 60
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 9 10 Risk decile
B 70 No IV CS IV CS 60
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 9 10 Risk decile
LEGEND: The rate of colectomy across deciles of risk among those exposed and unexposed to intravenous corticosteroids (IV CS) using boosted CART (A) and logistic regression (B) to model the DRS. Among those exposed to IV CS, the rate of colectomy is higher in deciles ≤ 7 and lower in the top 3 deciles compared to those unexposed to IV CS. 92
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16. Arbogast PG, Ray WA. Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders. Am J Epidemiol. Sep 1 2011;174(5):613-620. 17. Tadrous M, Gagne JJ, Sturmer T, Cadarette SM. Disease risk score as a confounder summary method: systematic review and recommendations. Pharmacoepidemiol Drug Saf. Feb 2013;22(2):122-129. 18. Miettinen OS. Stratification by a multivariate confounder score. Am J Epidemiol. Dec 1976;104(6):609-620. 19. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. Aug 2010;63(8):826-833. 20. McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods. Dec 2004;9(4):403-425. 21. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. Feb 10 2010;29(3):337-346. 22. Austin PC. Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation. Multivariate Behav Res. Jan 2012;47(1):115-135. 23. Harrell FE, Jr., Lee KL, Califf RM, Pryor DB, Rosati RA. Regression modelling strategies for improved prognostic prediction. Stat Med. Apr-Jun 1984;3(2):143- 152. 24. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. Dec 1996;49(12):1373-1379. 25. Vittinghoff E. Regression methods in biostatistics : linear, logistic, survival, and repeated measures models. New York: Springer; 2005. 26. Flouris AD, Duffy J. Applications of artificial intelligence systems in the analysis of epidemiological data. Eur J Epidemiol. 2006;21(3):167-170. 27. Harrell FE, Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. Feb 28 1996;15(4):361-387. 28. Stuart EA, Lee BK, Leacy FP. Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. J Clin Epidemiol. Aug 2013;66(8 Suppl):S84-S90 e81. 29. Ali MS, Groenwold RH, Pestman WR, et al. Propensity score balance measures in pharmacoepidemiology: a simulation study. Pharmacoepidemiol Drug Saf. Jan 29 2014. 30. Westreich D, Cole SR, Funk MJ, Brookhart MA, Sturmer T. The role of the c- statistic in variable selection for propensity score models. Pharmacoepidemiol Drug Saf. Mar 2011;20(3):317-320. 31. Selker HP, Griffith JL, Patil S, Long WJ, D'Agostino RB. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J Investig Med. Oct 1995;43(5):468-476. 94
32. Jarnerot G, Hertervig E, Friis-Liby I, et al. Infliximab as rescue therapy in severe to moderately severe ulcerative colitis: a randomized, placebo-controlled study. Gastroenterology. Jun 2005;128(7):1805-1811. 33. Wilhelm SM, McKenney KA, Rivait KN, Kale-Pradhan PB. A review of infliximab use in ulcerative colitis. Clin Ther. Feb 2008;30(2):223-230. 34. Wong CK, Yacyshyn BR. Fulminant Ulcerative Colitis. Curr Treat Options Gastroenterol. Jun 2000;3(3):217-226. 35. Chaparro M, Burgueno P, Iglesias E, et al. Infliximab salvage therapy after failure of ciclosporin in corticosteroid-refractory ulcerative colitis: a multicentre study. Aliment Pharmacol Ther. Jan 2012;35(2):275-283. 36. Ambroggio L, Lorch SA, Mohamad Z, Mossey J, Shah SS. Congenital anomalies and resource utilization in neonates infected with herpes simplex virus. Sex Transm Dis. Nov 2009;36(11):680-685. 37. Ridgeway G. gbm: Generalized boosted regression models. R package version 2.0-8. 2013; http://CRAN.R-project.org/package=gbm. 38. Strauss D. On Miettinen's multivariate confounder score. J Clin Epidemiol. Mar 1998;51(3):233-236. 39. Rose S. Mortality risk score prediction in an elderly population using machine learning. Am J Epidemiol. Mar 1 2013;177(5):443-452.
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7.8 APPENDIX. R CODE FOR DRS DEVELOPMENT BY BOOSTED CART
AND IMPLEMENTATION IN FINAL MODELS.
# Load packages for generalized boosted models library(gbm) library(twang)
# Fit the GBM gbm1 <- gbm(y ~ ., # formula where y is colectomy, "." are all predictors data=mydata, # dataset containing baseline predictors distribution="bernoulli", # indicates logistic regression n.trees=40000, # runs for 40,000 iterations, start with a few hundred just to make sure code works shrinkage=0.0005, # shrinkage or learning rate interaction.depth=4, # allows up to 4-way interactions bag.fraction=0.5, # sets fraction used for Friedman's random sub- sampling of the data train.fraction=1.0, # train.fraction<1.0 allows for out-of-sample prediction for stopping the algorithm n.minobsinnode=10, # minimum node size for tree cv.folds=3 # do 3-fold cross-validation )
# Relative influence summary(gbm1)
# Check performance using 3-fold cross-validation best.iter.cv <- gbm.perf(gbm1, method="cv") print(best.iter.cv)
# Assign disease risk score to all members of the cohort best.iter.cv <- gbm.perf(gbm1, method="cv") ivcs.cv.drs.logodds <- predict(gbm1, mydata, best.iter.cv)
# DRS as continuous glm(colectomy ~ ivcs_exp + ivcs.cv.drs.logodds, data=mydata, family=binomial)
# Create DRS deciles drs.decile <- cut(ivcs.cv.drs.logodds, quantile(ivcs.cv.drs.logodds, seq(0, 1, 0.1)), include=TRUE, labels=1:10) glm(colectomy ~ ivcs_exp + drs.decile, data=mydata, family=binomial)
# Doubly robust model (all DRS covariates + DRS decile) 96 glm(colectomy ~ . + drs.decile, data=mydata, family=binomial)
# Matching on the DRS (1:1 nearest neighbor) # Load MatchIt package library(MatchIt) nearest.replace <- matchit(formula=ivcs.exp ~ GIatt + nsaids01 + opioids01 + ifx01 + oralpred01 + budesonide01 + oralmesa01 + rectalmesa01 + mtx01 + csa01 + tacro01 + AZA01 + mp601 + sulfa01 + pn01 + flagyl01 + cipro01 + acetanonnar01 + ondansetron01 + midazolam01 + lidocaine01 + lansoprazole01 + diphen01 + propofol01 + ferrous01 + ranitidine01 + mechvent01 + transfusion01 + giendoscopy01 + ayr + amo + sex + white + adxUC + admpriority + govpay + age + readmission + hosp, distance=drs.dataset$ivcs.cv.drs.prob, data=drs.dataset, method="nearest", replace=TRUE) # summary function to calculate ASAM summary(nearest.replace, standardize=TRUE) match.replace <- match.data(nearest.replace) # Final logistic regression model matched on disease risk scores glm.final.replace <- glm(colectomy ~ ivcs.exp, data=match.replace, family=binomial)
# Stratified analyses by DRS decile glm(colectomy ~ ivcs_exp, data=mydata[mydata$DRS.decile==1, ], family=binomial) glm(colectomy ~ ivcs_exp, data=mydata[mydata$DRS.decile==2, ], family=binomial) ... glm(colectomy ~ ivcs_exp, data=mydata[mydata$DRS.decile==10, ], family=binomial)
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8 INFLIXIMAB REDUCES RISK OF COLECTOMY AMONG HIGH-RISK
PEDIATRIC PATIENTS WITH ULCERATIVE COLITIS
Andrew J. Klink MPH1,2, Brian K. Lee PhD1, Robert N. Baldassano MD2,3, Lindsey G.
Albenberg DO3, Lucy F. Robinson PhD1, Alison A. Evans ScD1, and Judith R. Kelsen
MD2,3
1Department of Epidemiology & Biostatistics, Drexel University School of Public
Health; 2Division of Gastroenterology, Nutrition & Hepatology, The Children’s Hospital of Philadelphia; 3Department of Pediatrics, University of Pennsylvania Perelman School of Medicine
Short title: Infliximab reduces risk of colectomy
Address for correspondence: Andrew Klink, Drexel University, 3215 Market Street,
Philadelphia, PA 19104. Phone: (267) 359-6104; Email: [email protected]
Key words: infliximab, ulcerative colitis, pediatric, colectomy
Conflicts of interest: None
Word count: 2,633
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8.1 ABSTRACT
Background: Important limitations to previous reports of the effectiveness of infliximab have resulted in inconsistent evidence of the effect of infliximab on the need for colectomy among children and adolescents hospitalized with ulcerative colitis (UC). We aimed to investigate the effect of infliximab on the risk of colectomy using the largest cohort to date of pediatric patients hospitalized with UC, allowing for adequate control for confounding by indication.
Methods: A disease risk score, which represents the baseline risk of colectomy, was used to control for confounding by indication. Multivariate logistic regression was used to incorporate the disease risk score and adjust for additional indicators of worsening clinical course (i.e., receipt of procedures and medications) to estimate the odds ratio
(OR) of colectomy across treatment groups.
Results: The unadjusted odds ratio of colectomy across treatment groups was 0.78 (95%
CI: 0.64, 0.95). In stratified analyses adjusting for baseline risk of colectomy as well as indicators of worsening clinical course during admission, the OR of colectomy was 0.48
(95% CI: 0.29-0.79) in the highest risk octile comparing those treated with infliximab to those not treated with infliximab.
Conclusion: We present nationally representative data from real clinical practice at 42 large pediatric hospitals that describe high-risk pediatric UC patients who had reduced odds of colectomy from the use of infliximab during their admission.
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8.2 INTRODUCTION
Infliximab is a monoclonal antibody that binds to free and membrane-bound tumor necrosis factor-α.19 It is administered at 5 or 10 mg per kilogram per infusion and has a half-life of 9 days.19 In September 2011, the Food and Drug Administration (FDA) approved infliximab for moderately to severely active UC in children who have failed conventional therapy. Off label use of infliximab in the pediatric UC population had been common practice as rescue therapy for the past decade, prior to FDA approval.108 As such, several recent studies have attempted to study the safety, efficacy, and effectiveness of infliximab among pediatric UC patients.
While the Active Ulcerative Colitis Trials 1 and 2 (ACT 1 and ACT 2) reported promising results among patients with UC treated with UC, 109 other reports of infliximab users with UC contributed varying results, particularly when investigating the risk of colectomy. Despite the use of infliximab among UC patients, the short-term rate of colectomy reported for treated patients remains around 30%,13,31,47,78,110-118 and up to
50%119 in longer follow-up (e.g., 3 years).
There are important limitations to these reports that have not led to consistent evidence of the effect of infliximab on the need for colectomy in this population. Many of these studies are reports of small case series, including at most a few dozen patients with
UC, many do not include a control group, and most are restricted to adults.111,115-118
Clinical trials of infliximab have enrolled very few patients hospitalized with UC.111 The small sizes of the cohorts in these reports have rendered controlling for confounders (e.g., concomitant medication and patient characteristics) not possible. Therefore, we aim to investigate the effect of infliximab on the risk of colectomy using the largest cohort to 100 date of pediatric patients hospitalized with UC, allowing for adequate control for confounding by indication.
8.3 METHODS
8.3.1 Patients
Patients 1-18 years old were included in the retrospective cohort study if they were admitted to one of 42 participating US children’s hospitals between January 1, 2003 and September 30, 2012 and had a discharge diagnosis of ulcerative colitis (ICD-9-CM codes 556.x). To reduce misclassification of cases, patients were excluded if they had a dual discharge diagnosis of infectious colitis or Crohn’s disease (ICD-9-CM codes 009.x and 555.x; n=485). Admissions occurring after a colectomy was performed were excluded as the patient was no longer at risk for colectomy. In addition to having a discharge diagnosis of ulcerative colitis, all patients in the cohort had a principal diagnosis of ulcerative colitis, which is assigned by the attending physician to designate the condition occasioning the admission. Medication exposures and laboratory studies ordered were identified by Clinical Transaction Classification (CTC) codes; CTC codes are recorded for each day of hospitalization. Procedures were identified by ICD-9-CM procedure codes and matched to their day of service during the admission.
Forty-two participating hospitals that are members of the Children’s Hospital
Association (CHA) contributed inpatient data to the Pediatric Health Information System
(PHIS) database during the study period. The PHIS database contains diagnosis and procedure codes and billed transaction and utilization data of hospital encounters.
Hospitals in PHIS represent 70% of all freestanding pediatric hospitals in the US and 101 most of the major metropolitan areas across the United States.104 Nine hospital-years of data (from 8 hospitals) were excluded due to missing clinical or financial data.
8.3.2 Statistical analyses
In an attempt to control for baseline risk of colectomy, a disease risk score was modeled in the unexposed cohort. Variables available in the PHIS data set were included in the disease risk score model if they were time-invariant or present on the first 2 days of hospitalization. Predictors were limited to days 1-2 of hospitalization to avoid further confounding by severity since exposure to certain medications or procedures after day 2 are likely due to a worsening clinical course. The resulting model represents a baseline risk of colectomy within 48 hours of admission among children and adolescents with ulcerative colitis. Both patient- and hospital-level variables were included in the disease risk score. Variables were excluded from the disease risk score if they represented a feature that may have occurred after day 2 of hospitalization or whose temporality could not be established given the available data (e.g., comorbid discharge diagnosis of volume depletion is not assigned a date of diagnosis).
The disease risk score was fit using a boosted classification and regression tree
(CART) model among patients unexposed to infliximab to estimate the disease risk score.
Boosted CART has several advantages over traditional logistic regression modeling in the development of a disease risk score (Klink et al, submitted). The CART model was fit using the generalized boosted model package105 in R version 2.15.13 (R Foundation for
Statistical Computing, Vienna, Austria). To limit overfitting of the data, a 3-fold cross- validation was performed to obtain the final boosted CART model. 102
A disease risk score was assigned to each subject in the cohort regardless of exposure status using results from the boosted CART model. To assess baseline covariate balance, the average standardized absolute mean (ASAM) distance was calculated for the disease risk score model.59 Values less than 0.25 indicate adequate covariate balance.67
The observed and predicted rate of colectomy in each decile was calculated for the disease risk score to assess model fit across all regions of the disease risk score.
To estimate the association between infliximab and colectomy, a stratified analysis using octiles of the disease risk score was performed to fit a logistic regression modeling colectomy given exposure to infliximab and adjusting for additional indicators of worsening clinical course during admission (i.e., receipt of intravenous corticosteroids, parenteral nutrient, opioids, blood transfusion, cyclosporine, tacrolimus, endoscopy, and mechanical ventilation). The resulting point estimates and their 95% confidence intervals from the final model represents the treatment effect of infliximab on the risk of colectomy, adjusted for the baseline covariates included in the disease risk score as well as confounding by indication during admission. Chi-square tests and unpaired t-tests were performed to determine a significant difference between the highest risk group (i.e., top quartile) compared to the lower risk groups (i.e., quartiles 1-3). Data management was performed using Stata 12.1 (StataCorp, College Station, TX). This study was reviewed and approved by the institutional review boards of the Children’s Hospital of
Philadelphia (CHOP) and Drexel University.
8.4 RESULTS
There were 10,003 subjects with an admission for UC during the 10-year study period across 42 US pediatric hospitals. There were 984 admissions in which a colectomy 103 was performed; 118 (8.1%) in the group treated with infliximab and 866 (10.1%) in the untreated group. Among those receiving colectomy, the median day of surgery was day
18 (IQR: 11-26) in the infliximab treated group and day 2 (IQR: 1-6) in the untreated group. Intravenous corticosteroids were administered prior to the surgery in 36.3%
(n=357) of admissions in which a colectomy was performed (94.1% [n=111] in the treated group and 28.4% [n=246] in the untreated group). The median day of infliximab initiation was 4 (IQR: 2-8) among the 1,458 (14.6%) patients who received infliximab.
The disease risk score modeling the probability of colectomy was fit among the
8,545 (85.4%) subjects who did not receive infliximab during their admission using 38 baseline variables (Table 1). After estimating a disease risk score for all members of the cohort, the mean scores among the untreated and treated groups were 0.02 (range: 0.003 to 0.99) and 0.02 (range: 0.003 to 0.89), respectively. The observed and predicted rates of colectomy were similar across all values of the disease risk score with slight underestimation in the top risk octile, suggesting adequate DRS model fit (Fig. 1).
Stratified on octiles of disease risk scores, the mean ASAM across disease risk octiles was 0.1555 and ranged from 0.1152 (octile 2) to 0.2293 (octile 8) indicating adequate covariate balance across treatment groups.
The 10 most influential baseline predictors of colectomy were the admitting hospital, attending physician’s subspecialty (e.g., gastroenterology), admission diagnosis of ulcerative colitis, year and month of admission, subject’s admission priority (e.g., urgent, emergency, or elective), normalized charges on days 0-1, exposure to intravenous corticosteroids, and parenteral nutrition, and age at admission (Table 1). Patients falling in the top quartile for colectomy risk (n=2,456) differed from patients in the bottom 3 risk 104 quartiles in terms of admission characteristics and procedures and medications received in the first 48 hours of admission (Table 2).
The unadjusted odds ratio of colectomy across treatment groups was 0.78 (95%
CI: 0.64, 0.95). In stratified analyses adjusting for baseline risk of colectomy as well as indicators of worsening clinical course during admission (i.e., receipt of intravenous corticosteroids, parenteral nutrient, opioids, blood transfusion, cyclosporine, tacrolimus, endoscopy, and mechanical ventilation), the OR of colectomy was 0.48 (95% CI: 0.29-
0.79) in the highest risk octile comparing those treated with infliximab to those not treated with infliximab (Table 3). The ORs of individual risk octiles 1 to 7 were all above
1.0, albeit not all were statistically significant or reliable estimates due to small numbers of colectomy within strata across treatment groups. To summarize the treatment effect of infliximab on the receipt of colectomy across risk octiles 1 to 7, the ORs was 3.15 (95%
CI: 2.28-4.36) and was adjusted for the subject’s disease risk octile as well as the indicators of worsening clinical course described above. Fig. 2 confirms that the treatment effect of infliximab is consistent across colectomy risk octiles 1 to 7 and changes (i.e., protective against colectomy) in octile 8.
8.5 DISCUSSION
In this study, treatment with infliximab resulted in reduced odds of colectomy among high-risk pediatric UC patients. The association between infliximab and colectomy in lower-risk patients is tenuous. Our results represent treatment estimates from the largest inpatient cohort of pediatric patients with ulcerative colitis to investigate the association between infliximab and colectomy. 105
Previous studies have reported conflicting results describing the risk of colectomy attributable to infliximab exposure among patients with UC.120 Even fewer reports have involved pediatric subjects and/or subjects hospitalized with UC.111 Many clinical trials report results among patients with less severe UC. The present study, while not a clinical trial, involved a large longitudinal cohort of pediatric patients hospitalized with UC, allowing for appropriate control for confounding by indication using clinical indicators of worsening clinical course during admission.
Results of our analyses have important implications in the treatment of children and adolescents admitted with UC. We have identified a subgroup of pediatric patients with UC in which treatment with infliximab reduces the risk of colectomy; these were admitted children with high baseline risk for colectomy. In general, patients in this subgroup were more likely to have had a previous admission for UC (i.e., readmission carries higher risk of colectomy) and require more intensive treatment in the first 48 hours of admission (e.g., in terms of number and frequency of medications and procedures). In other words, these patients would be identified clinically as presenting with an acute severe attack of UC. Recent guidelines by Turner and colleagues recommend initiation of infliximab as rescue therapy, which is often determined by days
3-5.42,79 As infliximab is increasingly being used as a corticosteroid-sparing therapy, the treatment with infliximab prior to day 3 of admission should be considered.20
Several disease activity, severity, and colectomy risk prediction models have been proposed for UC.21,22,28,40,50-52 All currently available models lack important characteristics that render their wide application to pediatric UC inappropriate. Until recently,40,52 all scores have been limited to adults. While the Pediatric Ulcerative Colitis 106
Activity Index is a validated measure that may be used to identify candidate patients for appropriate second-line therapies, it relies on patients’ clinical characteristics rarely available in large administrative databases. No such score has been developed for pediatric UC for use in the administrative data setting. Certain scores have been developed in adult UC to adequately model the risk of colectomy using administrative data, an important feature given the increasing interest paid to use of these data sources.
However, previous reports using administrative data have significant limitations related to linking patients over multiple admissions and establishing temporality of the predictors preceding the outcomes. Using a large nationally representative administrative database, we developed and implemented a disease risk score in a pediatric UC population to estimate the association between infliximab and the receipt of colectomy among children hospitalized with UC.
Despite the advantage of amassing the largest inpatient pediatric UC cohort, administrative data are not without important limitations. With any recorded data, there is the chance for miscoding or misclassification, by the clinician or data entry analyst. The
PHIS database is subjected to rigorous reliability and validity checks prior to including patient records in the database. If records from a given hospital do not meet predetermined error thresholds, they are sent back for reconciliation.53 We have attempted to reduce misclassification of subjects in the cohort by using established diagnosis codes to identify eligible subjects and to define procedures and drugs.
Another important limitation to consider when interpreting the results of this study is the inclusion of pediatric patients with UC across a wide range of disease severity. We have addressed potential confounding by severity through the use of a 107 disease risk score, which adjusts for baseline risk for colectomy, essentially a surrogate or covariate of their disease severity upon admission. To further address confounding by indication, we have included indicators of worsening clinical course throughout an admission in multivariate regression in addition to controlling for a subject’s baseline risk. The implications of including a wide range of disease severity in this study can be seen in the rates of colectomy in disease risk octiles 1 to 7. Patients in these risk strata represent nearly 88% of the cohort and have a probability of colectomy < 0.10.
Therefore, the results of our study apply largely to the 1,250 children and adolescents in the highest risk group, who have a baseline probability for colectomy of nearly 60%. We report a rate of colectomy (24.5%) among infliximab users in this top risk group, which is similar to others.112,115 Further, inclusion of a wide range of disease severity better depicts real clinical practice as infliximab is being used in UC patients with a wider range of disease severity.31 Without proper stratification on a severity indicator, the risk of colectomy could be misleading by under- or over-reporting the rate in a group with too much disease severity heterogeneity.
Lastly, our study is limited to inpatient data. Although we were able to link multiple admissions for each subject, thereby ensuring correct identification of those who have had a colectomy during a prior admission and are no longer at risk for the surgery, we were unable to identify use of infliximab outside the inpatient setting. This would have implications of whether someone who recently received infliximab (2-7 weeks based on common posology) prior to the admission included in our analyses may have lasting effects of the drug during the admission. 108
The underestimated rate of colectomy in the top risk octile (Fig. 2) may indicate that patients in the highest risk stratum for colectomy were channeled away from infliximab, perhaps due to an immediate need for colectomy. To further support this, the majority of those admissions involving colectomy in the highest risk octile (n=534, 68%) occurred within the first 2 days of admission. Although practice among pediatric gastroenterologist may be using infliximab sooner, or even without a first course of corticosteroids in the inpatient setting, further investigation in the use of infliximab among patients who require colectomy by day 2 is warranted. Additionally, future studies of this size among children and adolescents hospitalized with UC should be performed to investigate long term outcomes, including colectomy, and adverse events, including infections and lymphoma, associated with treatment with infliximab.
In conclusion, treatment with infliximab was associated with reduced odds of colectomy among high-risk pediatric patients in the inpatient setting.
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8.6 TABLES & FIGURES
Table 10. Characteristics of subjects. Relative Predictor n (%) influence^ Demographics Age, years, median (IQR) 14 (11, 16) 1.17 White 5,091 (70.6) 0.03 Male 3,758 (52.1) 0.08 Admission characteristics Gastroenterology attending 4,377 (60.7) 12.72 Admission diagnosis of UC 2,492 (34.6) 15.33 Admission priority 4.46 Emergency 4,306 (59.7) - Urgent 2,902 (40.3) - Admitting hospital, n 42 30.97 Admission year, n 10 3.47 Admission month, n 12 4.51 Government insurance 2,273 (31.5) 0.04 Readmission 3,009 (41.8) 1.09 Normalized charges on days 0-1, median (IQR) -0.28 (-0.56, 0.05) 19.63 Procedures on days 0-1 Transfusion 253 (3.5) 0.01 Mechanical ventilation 182 (2.5) 0.08 Endoscopy 1,272 (17.7) 0.02 Medications on days 0-1 Intravenous corticosteroids 3,409 (47.3) 2.16 Oral prednisone 2,906 (40.3) 0.11 Cyclosporine 81 (1.1) 0.04 Tacrolimus 298 (4.1) 0.01 6-mercaptopurine 1,132 (15.7) 0.01 Azathioprine 823 (11.4) <0.01 Sulfasalazine 582 (8.1) 0.01 Methotrexate 141 (2.0) 0.20 Oral mesalamine 2,555 (35.5) 0.16 Rectal mesalamine 590 (35.5) <0.01 Propofol 2,242 (31.1) 0.22 Opioids 3,886 (53.9) 0.34 Ondansetron 3,623 (50.3) 0.53 Parenteral nutrition 1,120 (15.5) 1.28 Metronidazole 2,683 (37.2) 0.57 Ciprofloxacin 632 (8.8) 0.09 Budesonide 189 (2.6) <0.01 Lidocaine 2,511 (34.8) 0.21 Ranitidine 1,510 (21.0) 0.03 Lansoprazole 2,374 (32.9) 0.05 Ferrous 1,664 (23.1) 0.11 Non-steroidal anti-inflammatory drug 1,315 (18.2) 0.07 Acetaminophen non-narcotic combo 3,853 (53.5) 0.13 LEGEND: Prevalence of each predictor is reported as percent (%) unless otherwise noted. ^Relative influence is the proportion attributable to a given variable in reducing the prediction error of the model. Relative influences are calculated in the untreated group only and sum to 100. 110
Table 11. Differences in baseline risk predictors by risk groups.
N (%) or median (IQR) Very low risk Low risk Moderate risk High risk p- (n=2,456) (n=2,456) (n=2,456) (n=2,456) value^ Demographics Age, years, median (IQR) 14 (11, 16) 14 (11, 16) 14 (10, 16) 14 (11, 16) 0.904 White 1,741 (70.9) 1,751 (71.3) 1,734 (70.6) 1,788 (72.8) 0.075 Male 1,290 (52.5) 1,285 (52.3) 1,278 (52.0) 1,268 (51.6) 0.568 Admission characteristics Gastroenterology attending 2,056 (83.7) 1,936 (78.8) 1,302 (53.0) 404 (16.5) <0.001 Admission diagnosis of UC 610 (24.8) 692 (28.2) 940 (38.3) 1,323 (53.9) <0.001 Admission priority <0.001 Emergency 1,125 (45.8) 1,242 (50.6) 1,195 (48.7) 737 (30.0) Urgent 998 (40.6) 683 (27.8) 609 (24.8) 592 (24.1) Government insurance 854 (34.8) 753 (30.7) 717 (29.2) 645 (26.3) <0.001 Readmission 709 (28.9) 891 (36.3) 1,222 (49.8) 1,263 (51.4) <0.001 Normalized charges on days -0.32 (-0.57, 0.00) -0.33 (-0.61, -0.04) -0.32 (-0.63, 0.05) -0.11 (-0.51, 0.82) <0.001 0-1, median (IQR) Procedures on days 0-1 Transfusion 78 (3.2) 46 (1.9) 39 (1.6) 51 (2.1) 0.690 Mechanical ventilation 5 (0.2) 11 (0.5) 16 (0.7) 56 (2.3) <0.001 Endoscopy 122 (5.0) 211 (8.6) 196 (8.0) 142 (5.8) 0.017 Medications on days 0-1 Intravenous corticosteroids 1,022 (41.6) 1,236 (50.3) 1,167 (47.5) 1,361 (55.4) <0.001 Oral prednisone 221 (9.0) 286 (11.6) 301 (12.3) 281 (11.4) 0.516 Cyclosporine 8 (0.3) 10 (0.4) 16 (0.6) 25 (1.0) 0.002 Tacrolimus 56 (2.3) 58 (2.4) 85 (3.5) 87 (3.5) 0.032 6-mercaptopurine 276 (11.2) 310 (12.6) 269 (11.0) 201 (8.2) <0.001 Azathioprine 196 (8.0) 229 (9.3) 238 (9.7) 180 (7.3) 0.011 Sulfasalazine 130 (5.3) 142 (5.8) 132 (5.4) 108 (4.4) 0.036 Methotrexate 3 (0.1) 5 (0.2) 17 (0.7) 38 (1.6) <0.001 Oral mesalamine 817 (33.3) 659 (26.8) 545 (22.2) 364 (14.8) <0.001 Rectal mesalamine 165 (6.7) 114 (4.6) 93 (3.8) 48 (2.0) <0.001 Propofol 181 (7.4) 274 (11.2) 270 (11.0) 639 (26.0) Opioids 595 (24.2) 809 (32.9) 886 (36.1) 1,459 (59.4) <0.001 Ondansetron 587 (23.9) 808 (32.9) 858 (34.9) 1,190 (48.5) <0.001 Parenteral nutrition 40 (1.6) 54 (2.2) 117 (4.8) 333 (13.6) <0.001 Metronidazole 378 (15.4) 553 (22.5) 754 (30.7) 790 (32.2) <0.001 Ciprofloxacin 77 (3.1) 140 (5.7) 162 (6.6) 133 (5.4) 0.600 Budesonide 33 (1.3) 41 (1.7) 53 (2.2) 49 (2.0) 0.380 Lidocaine 322 (13.1) 440 (17.9) 480 (19.5) 730 (29.7) <0.001 Ranitidine 305 (12.4) 321 (13.1) 384 (15.6) 433 (17.6) <0.001 Lansoprazole 441 (18.0) 466 (19.0) 461 (18.8) 375 (15.3) <0.001 Ferrous 319 (13.0) 346 (14.1) 388 (15.8) 273 (11.1) <0.001 Non-steroidal anti- <0.001 211 (8.6) 202 (8.2) 282 (11.5) 425 (17.3) inflammatory drug Acetaminophen non-narcotic 740 (30.1) 878 (35.8) 884 (36.0) 872 (35.5) 0.162 combo LEGEND: ^Chi-square tests and unpaired t-tests were performed to determine a significant difference between the highest risk group (i.e., top quartile) compared to the lower risk groups (i.e., quartiles 1-3).
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Table 12. Stratified treatment effects. Mean baseline Colectomy DRS probability of n (%) Treatment effect stratum N colectomy Treated Untreated OR (95% CI) 1 1,251 0.006 9 (6.4) 1 (0.1) N/A^ 2 1,250 0.010 6 (3.8) 2 (0.2) N/A^ 3 1,251 0.013 8 (4.2) 1 (0.1) N/A^ 4 1,250 0.018 7 (3.1) 2 (0.2) N/A^ 5 1,250 0.025 11 (4.7) 11 (1.1) 2.47 (0.97-6.30) 6 1,251 0.040 13 (6.3) 26 (2.5) 2.25 (1.08-4.69) 7 1,250 0.087 24 (12.8) 79 (7.4) 1.73 (1.04-2.89) 8 1,250 0.591 40 (24.5) 744 (65.6) 0.48 (0.29-0.79) LEGEND: DRS = disease risk score for baseline colectomy risk; OR = odds ratio; CI = confidence interval. ^The OR was not calculated due insufficient number of outcomes to model. The OR of colectomy after collapsing DRS strata 1-7 is 3.15 (95% CI: 2.28-4.36).
112
Figure 7. Observed and predicted rates of colectomy across risk octiles.
70 Observed Predicted 60
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 Risk octile
LEGEND: The observed and predicted rates of colectomy are shown for each octile of the disease risk score.
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Figure 8. Colectomy rates by treatment group.
No infliximab 60 Infliximab
50
40
30
20 Colectomy (per 100 patients) 100 (per Colectomy
10
0 1 2 3 4 5 6 7 8 Risk octile
LEGEND: The rate of colectomy among those treated and untreated with infliximab are shown for each quartile of the disease risk score.
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8.7 REFERENCES
1. Jakobovits SL, Travis SP. Management of acute severe colitis. Br Med Bull. 2005;75-76:131-144. 2. Mamula P, Markowitz JE, Brown KA, Hurd LB, Piccoli DA, Baldassano RN. Infliximab as a novel therapy for pediatric ulcerative colitis. Journal of pediatric gastroenterology and nutrition. Mar 2002;34(3):307-311. 3. Rutgeerts P, Sandborn WJ, Feagan BG, et al. Infliximab for induction and maintenance therapy for ulcerative colitis. N Engl J Med. Dec 8 2005;353(23):2462-2476. 4. Daperno M, Sostegni R, Scaglione N, et al. Outcome of a conservative approach in severe ulcerative colitis. Dig Liver Dis. Jan 2004;36(1):21-28. 5. Falaiye TO, Mitchell KR, Lu Z, et al. Outcomes following infliximab therapy for pediatric patients hospitalized with refractory colitis-predominant IBD. Journal of pediatric gastroenterology and nutrition. Feb 2014;58(2):213-219. 6. Halpin SJ, Hamlin PJ, Greer DP, Warren L, Ford AC. Efficacy of infliximab in acute severe ulcerative colitis: a single-centre experience. World J Gastroenterol. Feb 21 2013;19(7):1091-1097. 7. Jarnerot G, Hertervig E, Friis-Liby I, et al. Infliximab as rescue therapy in severe to moderately severe ulcerative colitis: a randomized, placebo-controlled study. Gastroenterology. Jun 2005;128(7):1805-1811. 8. Kohn A, Prantera C, Pera A, Cosintino R, Sostegni R, Daperno M. Anti-tumour necrosis factor alpha (infliximab) in the treatment of severe ulcerative colitis: result of an open study on 13 patients. Dig Liver Dis. Sep 2002;34(9):626-630. 9. Kohn A, Prantera C, Pera A, Cosintino R, Sostegni R, Daperno M. Infliximab in the treatment of severe ulcerative colitis: a follow-up study. Eur Rev Med Pharmacol Sci. Sep-Oct 2004;8(5):235-237. 10. Moore SE, McGrail KM, Peterson S, et al. Infliximab in ulcerative colitis: the impact of preoperative treatment on rates of colectomy and prescribing practices in the province of British Columbia, Canada. Dis Colon Rectum. Jan 2014;57(1):83-90. 11. Nielsen SD, Wewer V, Paerregaard A, et al. Does Infliximab Prevent Colectomy in Acute and Chronic Active Ulcerative Colitis? A Paediatric Study From 2005- 2012. Journal of pediatric gastroenterology and nutrition. Feb 24 2014. 12. Russell RK, Protheroe A, Roughton M, et al. Contemporary outcomes for ulcerative colitis inpatients admitted to pediatric hospitals in the United Kingdom. Inflammatory bowel diseases. Jun 2013;19(7):1434-1440. 13. Teisner AS, Ainsworth MA, Brynskov J. Long-term effects and colectomy rates in ulcerative colitis patients treated with infliximab: a Danish single center experience. Scand J Gastroenterol. Dec 2010;45(12):1457-1463. 14. Tiemi J, Komati S, Sdepanian VL. Effectiveness of infliximab in Brazilian children and adolescents with Crohn disease and ulcerative colitis according to clinical manifestations, activity indices of inflammatory bowel disease, and corticosteroid use. Journal of pediatric gastroenterology and nutrition. Jun 2010;50(6):628-633. 115
15. Vahabnezhad E, Rabizadeh S, Dubinsky MC. A 10-year, single tertiary care center experience on the durability of infliximab in pediatric inflammatory bowel disease. Inflammatory bowel diseases. Apr 2014;20(4):606-613. 16. Yamamoto S, Nakase H, Matsuura M, et al. Efficacy and safety of infliximab as rescue therapy for ulcerative colitis refractory to tacrolimus. Journal of gastroenterology and hepatology. May 2010;25(5):886-891. 17. Sjoberg M, Magnuson A, Bjork J, et al. Infliximab as rescue therapy in hospitalised patients with steroid-refractory acute ulcerative colitis: a long-term follow-up of 211 Swedish patients. Aliment Pharmacol Ther. Aug 2013;38(4):377-387. 18. Ambroggio L, Lorch SA, Mohamad Z, Mossey J, Shah SS. Congenital anomalies and resource utilization in neonates infected with herpes simplex virus. Sex Transm Dis. Nov 2009;36(11):680-685. 19. Ridgeway G. gbm: Generalized boosted regression models. R package version 2.0-8. 2013; http://CRAN.R,project.org/package=gbm. 20. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. Feb 10 2010;29(3):337-346. 21. Stuart EA, Lee BK, Leacy FP. Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. J Clin Epidemiol. Aug 2013;66(8 Suppl):S84-S90 e81. 22. Lv R, Qiao W, Wu Z, et al. Tumor necrosis factor alpha blocking agents as treatment for ulcerative colitis intolerant or refractory to conventional medical therapy: a meta-analysis. PLoS One. 2014;9(1):e86692. 23. Turner D, Mack D, Leleiko N, et al. Severe pediatric ulcerative colitis: a prospective multicenter study of outcomes and predictors of response. Gastroenterology. Jun 2010;138(7):2282-2291. 24. Turner D, Travis SP, Griffiths AM, et al. Consensus for managing acute severe ulcerative colitis in children: a systematic review and joint statement from ECCO, ESPGHAN, and the Porto IBD Working Group of ESPGHAN. Am J Gastroenterol. Apr 2011;106(4):574-588. 25. Bradley GM, Oliva-Hemker M. Pediatric ulcerative colitis: current treatment approaches including role of infliximab. Biologics. 2012;6:125-134. 26. D'Haens G, Sandborn WJ, Feagan BG, et al. A review of activity indices and efficacy end points for clinical trials of medical therapy in adults with ulcerative colitis. Gastroenterology. Feb 2007;132(2):763-786. 27. Ho GT, Mowat C, Goddard CJ, et al. Predicting the outcome of severe ulcerative colitis: development of a novel risk score to aid early selection of patients for second-line medical therapy or surgery. Aliment Pharmacol Ther. May 15 2004;19(10):1079-1087. 28. Truelove SC, Witts LJ. Cortisone in ulcerative colitis; final report on a therapeutic trial. Br Med J. Oct 29 1955;2(4947):1041-1048. 29. Travis SP, Farrant JM, Ricketts C, et al. Predicting outcome in severe ulcerative colitis. Gut. Jun 1996;38(6):905-910. 30. Ananthakrishnan AN, McGinley EL, Binion DG, Saeian K. Simple score to identify colectomy risk in ulcerative colitis hospitalizations. Inflammatory bowel diseases. Sep 2010;16(9):1532-1540. 116
31. Moore JC, Thompson K, Lafleur B, et al. Clinical variables as prognostic tools in pediatric-onset ulcerative colitis: a retrospective cohort study. Inflammatory bowel diseases. Jan 2011;17(1):15-21. 32. Turner D, Otley AR, Mack D, et al. Development, validation, and evaluation of a pediatric ulcerative colitis activity index: a prospective multicenter study. Gastroenterology. Aug 2007;133(2):423-432. 33. Fletcher DM. Achieving data quality. How data from a pediatric health information system earns the trust of its users. J Ahima. Nov-Dec 2004;75(10):22- 26.
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9 CONCLUSIONS AND IMPLICATIONS
9.1 MANAGEMENT OF PEDIATRIC UC IN THE INPATIENT SETTING
Real life inpatient management of children and adolescents with severe UC was the focus of the first study. Important trends in the use of medications, procedures, labs, and outcomes in the past decade were revealed across a 10-year period at 42 US pediatric hospitals. With few reports on US clinical inpatient practice among severe pediatric UC, we have little to use as a benchmark when comparing to disease management guidelines.
This being one of the largest cohorts of severe UC inpatients provides a clear look into how clinicians have been treating this condition over the last 10 years. Moving forward, we can address management practices that are trending in the wrong direction and support those practices that are trending in the right direction.
This study begins to elucidate factors associated with exposure and outcome rates.
For example, a higher caseload of UC hospitalizations was associated with lower rates of colectomy at that hospital. Thus, it appears that children’s hospitals in the US that see a greater number of severe UC admission in a given year are leading the trend in reducing colectomy rates. Each year of the study period, between 67% and 100% of high caseload hospitals (i.e., those with > 20 severe UC admissions in a year) were at or below the median colectomy rate for that year. Future studies should investigate what it is about these hospitals (e.g., patient- and hospital-level characteristics) that are associated with receipt of colectomy as well as other exposures and outcomes described in this study. 118
While the recommendations achieved at least 95% consensus among IBD specialists, many remain “practice points,” representing common practice where evidence is lacking. This study represents a 10-year baseline period prior to rollout of UC management guidelines and can be used to compare management strategies in the future.
Prospective data will be needed to determine to what degree clinical management will be affected in light of the recent guidelines on the management of acute severe UC among children and adolescents was presented by Turner and colleagues.42. Nevertheless, this is an opportunity to put past clinical practice into context by comparing them to the current guidelines for the management of severe UC.
Second-line therapy was common among children admitted with UC with 30.8% of patients receiving a second line treatment (i.e, infliximab or a calcineurin inhibitor), colectomy, or both during admission. Of these second line treatments, calcineurin inhibitors were far less commonly used in this pediatric population compared to infliximab. In fact, 73.8% of those receiving a second line treatment received infliximab.
Although there was a modest increase of 1.5% in the rate of infliximab use across the 10- year period, substantial variation across hospitals remained with rates of infliximab ranging from 6% to 65% in the final year of the study period. It is important to consider how infliximab is increasingly being used as a corticosteroid-sparing therapy.20 As such, this study does not include patients with exacerbations of severe UC who were not treated with intravenous corticosteroids (and perhaps treated with infliximab in their place).
This is one of the largest cohort studies investigating treatment practice in the inpatient setting among children and adolescents with UC, particularly among those with severe UC. Unlike results of controlled trials that have narrow applications to a group of 119 patients with highly defined clinical characteristics not always seen in the real world, our results represent observed clinical practice among US healthcare workers to treat children with severe UC. As sufficient time has passed to allow for the implementation of the recent severe UC guidelines, additional follow-up studies are warranted to benchmark progress in severe UC management. This work may lead to a better understanding of optimal management of UC as well as to identify areas of further research to elucidate treatment effectiveness of current and emerging therapies.
9.2 DEVELOPMENT AND IMPLEMENTATION OF A DRS
The second study described the development and implementation of a DRS as a method to control for confounding by indication. The DRS may represent a superior alternative to other methods to control for confounding by indication in pharmacoepidemiologic studies, particularly when the exposure is rare or an emerging therapy, or when investigating effect modification of the exposure. DRS methods can be easily implemented in R.
Two methods – boosted CART and logistic regression – were illustrated in the development and implementation of a DRS. Boosted CART has been compared to logistic regression in other studies, with varying results suggesting that one is not always superior in terms of performance.58-60 In deciding which method to pursue for a given study, several considerations should be weighed. These considerations relate to the inherent assumptions and limitations of the models. For logistic regression, this includes the assumption of linearity of each parameterized covariate with the log-odds and the appropriate use of interaction terms. In practice, these assumptions are commonly ignored, rendering the results of the logistic regression (and subsequent application of the 120 resulting DRS) susceptible to residual confounding and a biased estimate of the treatment association.57 With traditional regression methods, 10 outcomes per covariate (i.e., individual variables and interaction terms) included in the model has become the rule.62,63
This is not the case when using boosted CART, thus avoiding the risk of excluding important confounders resulting in inadequate confounder control. The boosted CART model handles a large number of covariates, even if many of the covariates are correlated with one another or are unrelated to the outcome, without sacrificing estimate precision.58,61 Missingness among covariates –even when missing completely at random— can also be problematic in logistic regression, effectively reducing the sample size when log odds are estimated for the entire cohort. Boosted CART on the other hand, does not require these model assumptions and successfully models nonlinear relationships, missingness, and complex interaction terms without overfitting the data.
Thus, the boosted CART approach preserves the maximum sample size, which avoids added bias of the effect estimates due to using complete cases only, as well as impacting the estimated standard errors around the estimates. While boosted CART does not share these limitations when fitting the data, this method has limited familiarity and thus reduced ease of results interpretability. In addition, the boosted CART does require more processing time compared to traditional logistic regression modeling. Performing a 3-fold cross validation, boosted CART fits 3 models using subsets of the dataset before fitting a final cross-validated model. In each of these folds, the CART model fits m number of iterations (e.g., 40,000), resulting in consequential processing time. Both boosted CART and logistic regression can be fit using open source software, such as R. 121
Regardless of which method is selected to develop a disease risk score, the use of a DRS to control for confounding by indication in pharmacoepidemiologic studies is underutilized with many researchers opting to use propensity scores instead.
Nevertheless, there are several settings in which DRS may be more advantageous than the others. For example, when the exposure is polytomous (e.g., non-use, low-dose, high- dose), propensity score methods become more difficult to implement whereas DRS simply use a polytomous exposure in the final model. DRS are a natural tool with which to investigate effect modification,106 a phenomena difficult to address using propensity scores. The differences in the rates of outcome across quantiles of the risk score can reveal important nonlinear trends attributable to the exposure. Also, when the exposure is an emerging therapy and thus prescriber preferences have not stabilized (thus making it difficult to accurately model the probability of treatment), the choice of using DRS over propensity scores has clear advantages.56 In other settings, e.g., when there is a high exposure-confounder correlation present, DRS may not be appropriate. Propensity scores are also considered superior to DRS when the outcome is rare, due to the relative inability to fit a model of the outcome.71 Along with a suite of other confounding adjustment methods, DRS methods should be in the toolbox of epidemiologists who are often faced with the problem of confounding by indication.
9.3 ASSOCIATION OF INFLIXIMAB AND COLECTOMY
The thesis culminates with the third study investigating the association between infliximab use and the receipt of colectomy among pediatric patients with UC. The study presents nationally representative data from real clinical practice at 42 large pediatric hospitals that describe high-risk pediatric UC patients who had reduced odds of 122 colectomy from the use of infliximab during their admission. However, treatment effects of infliximab among pediatric patients with UC at low risk of colectomy (i.e., probability
<0.10) were not reliable in this cohort due to small numbers of outcomes in these low risk strata.
The implications of including a wide range of disease severity in this study can be seen in the rates of colectomy in disease risk octiles 1 to 7. Patients in these risk strata represent nearly 88% of the cohort and have a probability of colectomy < 0.10.
Therefore, the results of our study apply largely to the 1,250 children and adolescents in the highest risk group, who have a baseline probability for colectomy of nearly 60%.
Further, inclusion of a wide range of disease severity better depicts real clinical practice as infliximab is being used in UC patients with a wider range of disease severity.31
Without proper stratification on a severity indicator, the risk of colectomy could be misleading by under- or over-reporting the rate in a group with too much disease severity heterogeneity.
9.4 LIMITATIONS
The results obtained from this aim must be understood within its limited generalizability. The ability to generalize outside the population under study is not intended, nor is it warranted. The population under study consists of children and adolescents hospitalized with severe ulcerative colitis to a freestanding children’s hospital. Given known differences between freestanding children’s hospitals and non- children’s hospitals,121 this limitation is one of generalizability and not necessarily of selection bias. On average, children’s hospitals care for children and adolescents of greater severity and with great number of comorbidities.121 Since the intended population 123 for which estimates of treatment effects is most needed is among children and adolescents of greatest severity of ulcerative colitis, the dataset represents an appropriate choice to achieve the aims of this thesis.
9.5 RECOMMENDATIONS
Results of this research as a whole provide evidence to guide the use of infliximab in the hospital setting to treat children with varying degrees of UC disease severity. This is the largest inpatient cohort of children and adolescents with UC used to investigate the treatment effect of infliximab. Many include at most a few dozen patients with UC, and most of those studies are restricted to adults.13,31,110-113,115-119,122 Clinical trials of infliximab have enrolled very few patients hospitalized with UC. The cohort described herein, while not a clinical trial, involved a large number of pediatric patients hospitalized with UC, allowing for appropriate control for confounding by indication using clinical indicators of worsening clinical course during admission. Future collaborations should take advantage of this large existing pediatric cohort to perform additional studies.
This work builds the foundation for and support for continued and future research with the following recommended directions. The effects of infliximab should be investigated in a prospective cohort of children with UC, focusing on quality of life and patient reported outcomes among the other common outcomes of clinical response and colectomy. No studies have investigated treatment and outcomes among children recently discharged from an admission for severe UC. As such, studying the effect and utility of infliximab among children with UC in the outpatient setting is warranted. Additional pharmacoepidemiology and comparative effectiveness studies for the treatment of 124 pediatric UC should be performed. Specifically, in addition to comparing infliximab with other biologics (e.g., adalimumab, certolizumab pegol, golimumab, and natalizumab), comparisons of infliximab doses (i.e., 5 mg per kilogram versus 10 mg per kilogram) and induction schedules (e.g., 0, 2, 6, 14 weeks versus shorter escalated scheduled) need to be the focus of comparative effective research in this pediatric population. An alternative exposure measure in these studies should include serum infliximab trough levels, as they may represent the relevant exposure to infliximab.111
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11 APPENDIX A. SUMMARY OF RESULTS FROM PRELIMINARY
ANALYSES
11.1 ABSTRACT
Objectives: This descriptive study presents inpatient treatment practices for ulcerative colitis (UC) and treatment variation categorized by severity across a national sample of
US children’s hospitals.
Methods: The Pediatric Health Information System (PHIS) database was used to describe treatment practice for patients 1-18 years old admitted to one of 43 US children’s hospitals for UC. Rates of medications and procedures at each hospital were calculated and compared to other PHIS-participating hospitals. Parallel analyses compared admissions among children with mild-to-moderate UC to admissions among children with severe UC. Correlations of hospital case mix and caseload with rates of medications and procedures were measured by Pearson’s r.
Results: There were 12,246 admissions among 6,166 patients with UC between January
1, 2003 and September 30, 2012 at 43 US children’s hospitals. Exposure to corticosteroids, antibiotics, opioids, and infliximab were significantly more common among children with severe UC than children with mild-to-moderate UC (all p- values<0.05). Rates of medication exposures and procedures performed among both categories of admission severity varied widely across hospitals and were not correlated with hospital case mix or caseload. 134
Conclusions: Substantial variation in common treatment practices for children with UC exists across UC children’s hospitals, even within categories of admission severity.
Follow-up comparative effectiveness research is warranted to identify optimal treatment strategies for varying degrees of severity among children with UC.
11.2 RESULTS
11.2.1 Study cohort
There were 12,246 admissions for any reason among 6,166 patients with UC to a
PHIS-participating hospital between January 1, 2003 and September 30, 2013. While all admissions had a discharge diagnosis for UC, 8,114 (66.3%) admissions carried the diagnosis of UC as the principal diagnosis (Table 1). Across all hospitals, the mean
(range) annual rate of UC inpatient admissions was 38.6 per 100,000 total admissions
(32.9, 41.1). The annual rate of UC inpatient admissions remained stable across the study period (p-value=0.97). Patients were admitted a median (interquartile range [IQR]) of 1 time (1, 2) during the 10-year study period for a median length of stay of 5 days (3, 9).
Nearly half (49.1%) were male and the vast majority (73.0%) was white, which is similar to other pediatric UC cohorts (12, 13). The median age at first admission during the study period was 14 years (10, 16). While patients with severe UC were older at first admission and had more admissions than those with mild-moderate UC (p-values<0.01 for both), these differences were minor and not clinically meaningful (Table 1). Stratified by severity, the two subgroups had similar distributions for sex and race. 135
11.2.2 Participating hospitals
There were 390 hospital-years of data among 43 admitting hospitals representing all nine US census divisions during the 10-year study period. The mean (range) total number of patients with UC admitted to each hospital was 143 (35, 389), while annually each hospital admitted a mean of 22 (2, 76) patients with UC. Over the 10-year study period, hospitals admitted a mean of 247 (50, 619) and 38 (3, 123) children with mild-to- moderate UC and severe UC, respectively. Six hospitals did not report a gastroenterologist as an attending physician for at least one year during the study period.
Hospitals included in the analysis had a mean of 85,354 (5,997; 257,215) annual admissions for any reason.
11.2.3 Length of stay and readmissions
The median (IQR) LOS was 5 (3, 9) days among all admissions for UC (Table 1).
While LOS remained stable over the study period at 4 (3, 7) days among children with mild-to-moderate UC, there was a trend toward increasing LOS among children with severe UC suggesting a mean increase in LOS of 1 day every 3 years (p-value<0.01; Fig.
1A). Children with severe UC experienced a mean LOS of 9.7 days longer compared to children with mild-to-moderate UC (p-value<0.01). There were 6,080 readmissions at any time among 2,488 children and adolescents with UC during the study period (Table
1). Over a quarter of readmissions (27.5%; n=1,673) occurred within 30 days of discharge. Readmissions at any time were more common among children with mild-to- moderate UC (51.5%) than among children with severe UC (37.3%), while 30-day readmissions were similar across severity categories (Table 1; Fig. 1B). 136
11.2.4 Medication exposures
The most common drug class of medication exposure was corticosteroids with
8,810 (71.9%) admissions (see Appendix for list of corticosteroids). Among admissions involving corticosteroids exposure, corticosteroids were initiated within the first 2 days of
77.3% of admissions (median: day 0; IQR: 0, 1) for a median (IQR) of 4 (2, 6) days and
10 (6, 17) days among children with mild-to-moderate UC and severe UC, respectively
(Table 2). Across all hospitals, a mean (range) of 58.9% (43.6%, 77.3%) and 86.3%
(50.0%, 100%) of children with mild-to-moderate UC and severe UC, respectively, were exposed to intravenous corticosteroids at a given hospital (Fig. 2A).
Opioids were the second most common exposure, as a drug class, with 7,420
(60.6%) admissions followed by individual medications odansetron and acetaminophen.
The most common opioids administered during UC admissions were morphine sulfate
(number of admissions: n=4,392; 35.9%), fentanyl (base or citrate; n=4,207; 34.4%), narcotic analgesic combinations (see Appendix; n=2,300; 18.8%), followed by hydromorphone HCl (n=1,687; 13.8%) and oxycodone HCl (n=1,062; 8.7%). All other opioids (see Appendix) occurred during less than 7% of admissions. Hospitals administered opioids to a mean (range) of 57.3% (25.8%, 84.0%) and 76.8% (40.0%,
100%) of children with mild-to-moderate UC and severe UC, respectively.
By hospital, antibiotics were administered at a mean (range) rate of 58.1%
(37.1%, 81.9%) and 80.9% (47.9%, 100%) of admissions among children with mild-to- moderate UC and severe UC, respectively (Fig. 2B). Metronidazole was more common than ciprofloxacin among both severity categories, while both antibiotics were 137 significantly more common among severe UC admissions compared to mild-to-moderate
UC admissions (Table 2).
Infliximab was administered in 12.9% of all UC admission (n=1,583). The mean
(range) rate of infliximab receipt was more common among children with severe UC compared to children with mild-to-moderate UC at 34.4% (9.1%, 53.8%; excluding one outlier hospital with 100% exposure rate [n=7/7]) and 9.7% (3.4%, 17.1%), respectively
(Fig. 2C). There were relatively few admissions involving the receipt of cyclosporine or tacrolimus 136 (1.1%) and 450 (3.7%), respectively. Twenty-eight (65.1%) and 38
(88.4%) hospitals administered cyclosporine and tacrolimus, respectively, at least once during the study period. Among hospitals that administered cyclosporine during the study period, 85.7% (n=24/28) of hospitals administered cyclosporine during 9 or fewer admissions. Excluding tacrolimus for which the rate of exposure was similar across severity categories, all medication exposures reported in Table 2 were significantly more common among children with severe UC compared to children with mild-to-moderate
UC (all p-values<0.05). Across hospitals, the rates of IV CS, antibiotics, and infliximab use among mild-to-moderate UC admissions were moderately positively correlated with the rates of use among severe UC admissions (r = 0.40, 0.44, and 0.32, respectively;
Figure 2). The correlations between hospital caseloads for both severity categories and rates of IV CS, antibiotics, and infliximab among their respective severity category were negligible. Similarly, a larger ratio of severe to mild-to-moderate UC admissions was not correlated with the rates of IV CS, antibiotics, or infliximab. 138
11.2.5 Procedures performed
Endoscopy, including upper endoscopy, colonoscopy, and flexible sigmoidoscopy, was performed at a mean (range) rate of 31.9 (0.0, 54.2) per 100 admissions and 54.2 (0.0, 100) per 100 admissions among children with mild-to- moderate UC and severe UC, respectively (Fig. 3A). Half of all hospitals had an endoscopy rate between 39.8 and 58.3 (i.e., the IQR) per 100 admissions among children with severe UC. The rate of abdominal imaging ranged from 18.2 to 60.0 per 100 admissions and 17.3 to 100 per 100 admissions among children with mild-to-moderate
UC and severe UC, respectively (Fig. 3B). The most common abdominal imaging among admissions included x-ray (n=3,165; 25.8%), computed tomography scan (n=1,466;
12.0%), ultrasound (n=835; 6.8%), and magnetic resonance imaging (n=440; 3.6%). The mean (range) rate of colon resection among children with mild-to-moderate UC and severe UC was 7.4 (0.0, 18.3) and 15.0 (0.0, 35.7) per 100 admissions, respectively (Fig.
3C). Endoscopy, abdominal imaging, and colon resection were all significantly more common among children with severe UC compared to children with mild-to-moderate
UC (Table 1). Across hospitals, the rates of endoscopy, abdominal imaging, and colon resection among mild-to-moderate UC admissions were positively correlated with the rates of use among severe UC admissions (r=0.65, 0.45, and 0.48, respectively; Figure 3).
Hospitals with a greater number and a greater proportion of severe UC admissions tended to perform fewer abdominal imaging procedures among severe UC admissions (r=-0.38, -
0.33, respectively). 139
11.2.6 Sensitivity analyses
Restricted cohort. The cohort of 12,246 admissions among children and adolescents with UC represents admissions for UC-related episodes as well as for unrelated episodes, such as viral infections, physical injuries, etc. In an attempt to delineate inpatient practices to treat UC versus other conditions unrelated to UC, the cohort was restricted in several ways. First, the cohort was restricted to include only those with a principal diagnosis of UC (all UC, n=8,114; mild-to-moderate UC, n=6,499; severe UC, n=1,615), which represents the condition occasioning the admission.
Corticosteroid administration was common, involving 82.4% (n=6,689), 80.3%
(n=5,221), and 90.9% (n=1,468) of admissions with a principal diagnosis of UC among all, mild-to-moderate, and severe admissions, respectively, during the study period
(76.3% [n=6,188] involved IV corticosteroids; 40.9% [n=3,322] involved oral prednisone). Infliximab was recorded in 17.8% (n=1,442), 14.1% (n=915), and 32.6%
(n=527) of admissions with a principal diagnosis of UC among all, mild-to-moderate, and severe admissions, respectively. Although the gaps in IV CS and infliximab use between the restricted mild-to-moderate UC admissions and severe UC admissions were attenuated, significant differences in use remained (p-values both <0.01). The rates of antibiotics use (i.e., metronidazole and ciprofloxacin) were similar (32.4% and 6.4%, respectively) in the restricted mild-to-moderate UC cohort (results of severe UC cohort reported in Table 2).
History of infliximab exposure. History of infliximab exposure is likely underreported from these inpatient PHIS data, since patients in this cohort may have had exposure to infliximab in the outpatient setting prior to an inpatient admission. Infliximab 140 has a half-life of 9 days, resulting in sustained therapeutic levels in the patient several weeks after administration (14). Since not all PHIS-participating hospitals submit outpatient data to the PHIS database, infliximab exposure prior to inclusion in the study could not be determined for the full cohort used in this analysis. Nevertheless, history of infliximab exposure can be determined for a subset of patients who were admitted to a hospital that submitted outpatient data available during the study period (n=4,493 admissions; 36.7%). Based on similar clinical and demographic characteristics, it is reasonable to assume that the rate of prior infliximab exposure among these patients is similar to the rate among patients for whom outpatient data are not available. Therefore, accounting for infliximab exposure prior to an inpatient admission the estimated rate of exposure to infliximab within 8 weeks of inclusion in the study is 13.5% among mild to moderate UC admissions and 41.3% among severe UC admissions. 141
11.3 TABLES & FIGURES
Table 1. Demographic and clinical characteristics of the study cohort. Mild to moderate UC N (%) or median (IQR) All UC admissions admissions Severe UC admissions p-value* Patient characteristics N = 6,166 N = 5,154 N = 1,012 Male sex 3,025 (49.1) 2,535 (49.2) 490 (48.4) 0.664 Number of admissions during study period 1 (1, 2) 1 (1, 2) 1 (1, 3) <0.001 White race 4,500 (73.0) 3,761 (73.0) 739 (73.0) 0.973 Age at first admission during study (years) 14.1 (10.7, 16.2) 14.0 (10.6, 16.2) 14.3 (11.2, 16.2) 0.004
Admission characteristics N = 12,246 N = 10,631 N = 1,615 Discharge diagnosis of ulcerative colitis 12,246 (100.0) 10,631 (100.0) 1,615 (100.0) N/A Principal diagnosis of ulcerative colitis 8,114 (66.3) 6,499 (61.1) 1,615 (100.0) <0.001 Length of stay (days) 5 (3, 9) 4 (3, 7) 13 (8, 20) <0.001 Disposition Home/home health 12,041 (98.3) 10,459 (98.4) 1,582 (98.0) Ref Transfer 109 (0.9) 83 (0.8) 26 (1.6) 0.001 Left against medical advice 8 (0.1) 8 (<0.1) 0 (0.0) N/A Expired 29 (0.2) 28 (0.3) 1 (<0.1) 0.156 Missing 59 (0.5) 53 (0.5) 6 (0.4) 0.502 Readmissions 6,080 (49.6) 5,477 (51.5) 603 (37.3) <0.001 Readmission within 30 days 1,673 (13.7) 1,448 (13.6) 225 (13.9) 0.734 Government insurance 3,701 (30.2) 3,251 (30.6) 450 (27.9) 0.027 Admission to intensive care unit 799 (6.5) 652 (6.1) 147 (9.1) <0.001 Admission to emergency department 5,694 (46.5) 4,904 (46.1) 790 (48.9) 0.036 Use of ventilator 319 (2.6) 255 (2.4) 64 (4.0) <0.001 Use of total parenteral nutrition 2,559 (20.9) 944 (8.9) 1,615 (100.0) <0.001 Endoscopy performed 4,314 (35.2) 3,416 (32.1) 898 (55.6) <0.001 Abdominal imaging performed 4,151 (33.9) 3,450 (32.5) 701 (43.4) <0.001 Colon resection 1,100 (9.0) 871 (8.2) 229 (14.2) <0.001 LEGEND: *p-value calculated by chi-squared test, logistic regression, and t-test for dichotomous, categorical, and continuous variables, respectively.
142 Table 2. Medication exposure characteristics.
All UC admissions Mild to moderate UC admissions Severe UC admissions (N=12,246) (N=10,631) (N=1,615) First day of Duration of First day of Duration of First day of Duration of exposure, exposure, days, exposure, exposure, days, exposure, exposure, days, Medication n (%) median (IQR) median (IQR) n (%) median (IQR) median (IQR) n (%) median (IQR) median (IQR) Corticosteroids 8,810 (71.9) 0 (0, 1) 5 (3, 9) 7,342 (69.1) 0 (0, 1) 4 (3, 7) 1,468 (90.9)# 1 (0, 2) 12 (7, 19)# Intravenous* 7,629 (62.3) 0 (0, 2) 4 (3, 8) 6,217 (58.5) 0 (0, 1) 4 (2, 6) 1,412 (87.4)# 1 (0, 2) 10 (6, 17)# Oral* 4,337 (35.4) 4 (1, 8) 2 (1, 4) 3,450 (32.5) 3 (1, 6) 2 (1, 3) 887 (54.9)# 10 (6, 16)# 2 (2, 5) Infliximab 1,583 (12.9) 4 (2, 8) 1 (1, 1)^ 1,056 (9.9) 3 (1, 6) 1 (1, 1) 527 (32.6)# 7 (4, 10)# 1 (1, 1)# Opioids 7,420 (60.6) 1 (0, 2) 4 (1, 7) 6,171 (58.1) 0 (0, 1) 3 (1, 6) 1,249 (77.3)# 1 (0, 4)# 8 (2, 16)# Cyclosporine 136 (1.1) 1 (0, 7) 8 (5, 16) 97 (0.9) 1 (0, 4) 7 (4, 14) 39 (2.4)# 7 (1, 11) 10 (7, 17) Tacrolimus 450 (3.7) 1 (0, 2) 7 (3, 10) 385 (3.6) 0 (0, 1) 6 (3, 9) 65 (4.0) 4 (1, 10)# 9 (6, 13) Thiopurine 2,777 (22.7) 1 (0, 2) 4 (2, 7) 2,245 (21.1) 1 (0, 1) 4 (2, 6) 532 (32.9)# 1 (0, 6)# 8 (4, 12)# AZA* 1,151 (9.4) 1 (0, 1) 4 (2, 7) 944 (8.9) 1 (0, 1) 4 (2, 6) 207 (12.8)# 1 (0, 6)# 7 (3, 12)# 6-MP* 1,648 (13.5) 1 (0, 2) 4 (2, 7) 1,319 (12.4) 1 (0, 2) 4 (2, 6) 329 (20.4)# 1 (0, 6) 8 (4, 12)# NSAIDs 2,920 (23.8) 1 (0, 3) 3 (1, 5) 2,457 (23.1) 1 (0, 2) 3 (1, 4) 463 (28.7)# 4 (1, 10)# 4 (2, 7)# Antibiotics 7,555 (61.7) 0 (0, 1) 5 (3, 9) 6,240 (58.7) 0 (0, 1) 4 (2, 7) 1,315 (81.4)# 1 (0, 3)# 10 (6, 17)# Metronidazole* 4,293 (35.1) 1 (0, 2) 5 (3, 9) 3,266 (30.7) 1 (0, 2) 4 (3, 7) 1,027 (63.6)# 1 (0, 3)# 9 (6, 15)# Ciprofloxacin* 1,049 (8.6) 1 (0, 4) 4 (2, 7) 804 (7.6) 1 (0, 3) 4 (2, 6) 245 (15.2)# 2 (0, 7)# 7 (4, 11)#
LEGEND: *Not mutually exclusive; ^duration of infliximab exposure was measure by the number of injections administered during admission; +severe UC admissions consisted of admissions with a principal diagnosis of UC, admission priority other than “elective,” and involving TPN; #statistically significant difference (at α=0.05) between mild to moderate UC admissions versus severe UC admissions by t-test or chi-square test. 143
Figure 1. LOS and readmissions over the 10-year study period.
A 25 20 15 10 Length of stay (days) Length 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
B 1 .8 .6 .4 .2 Rate of 30-day readmission of Rate 30-day 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
Mild-to-moderate UC Severe UC Mean Mean IQR IQR
144
LEGEND: LOS = length of stay; IQR = interquartile range. The mean and IQR for LOS (A) and 30-day readmission rate (B) by year are depicted for each category of admission severity. Admissions among children with severe UC are depicted by black solid lines, and admissions among children with mild-to-moderate UC are depicted by gray dashed lines.
145
Figure 2. Mean rate of medication use.
A Pearson's r = 0.40 100 90 80 70 60 50 IV CS use (%) among mild-to-moderate UC mild-to-moderate IV (%) among CS use 40 40 50 60 70 80 90 100 IV CS use (%) among severe UC
B Pearson's r = 0.44 100 80 60 40 Antibiotic use (%) among mild-to-moderate UC mild-to-moderate (%) among use Antibiotic 20 40 50 60 70 80 90 100 Antibiotic use (%) among severe UC
146
C
20 Pearson's r = 0.32 15 100 80 10 60 5 40 Infliximab use (%) among mild-to-moderate UC mild-to-moderate (%) among use Infliximab 0
0 20 40 60 80 100
20 Infliximab use (%) among severe UC Antibiotic use (%) among mild-to-moderate UC mild-to-moderate (%) among use Antibiotic 40 50 60 70 80 90 100 Antibiotic use (%) among severe UC
<10% severe UC cases 10-20% severe UC cases >20% severe UC cases Fitted values
LEGEND: IV CS = intravenous corticosteroids. Receipt of IV CS (A), antibiotics (B), and infliximab (C) are shown among mild-to-moderate UC admissions and among severe UC admissions for each hospital. The size of the circle represents the number of total UC admissions seen at that given hospital, while the shading of the circle indicates the proportion of severe UC at that given hospital with darker shades indicating increasing proportion of severe UC admissions. Fitted values were estimated by linear regression. Strength of correlation was measured by Pearson’s r.
147
Figure 3. Mean rate of procedures performed.
A Pearson's r = 0.65 100 80 60 40 20 0 Receipt of endoscopy (%) among mild-to-moderate UC mild-to-moderate (%) among of endoscopy Receipt 0 20 40 60 80 100 Receipt of endoscopy (%) among severe UC
B Pearson's r = 0.45 100 80 60 40 among mild-to-moderate UC mild-to-moderate among 20 Receipt of abdominal imaging (%) imaging of abdominal Receipt 0
0 20 40 60 80 100 Receipt of abdominal imaging (%) among severe UC
148
C
40 Pearson's r = 0.48 30 100 80 20 60 10 40 Colon resection (%) among mild-to-moderate UC mild-to-moderate (%) among resection Colon 0
0 10 20 30 40
20 Colon resection (%) among severe UC Antibiotic use (%) among mild-to-moderate UC mild-to-moderate (%) among use Antibiotic 40 50 60 70 80 90 100 Antibiotic use (%) among severe UC
<10% severe UC cases 10-20% severe UC cases >20% severe UC cases Fitted values
LEGEND: Receipt of endoscopy (A), abdominal imaging (B), and colon resection procedures (C) are shown among mild-to-moderate UC admissions and among severe UC admissions for each hospital. The size of the circle represents the number of total UC admissions seen at that given hospital, while the shading of the circle indicates the proportion of severe UC at that given hospital with darker shades indicating increasing proportion of severe UC admissions. Fitted values were estimated by linear regression. Strength of correlation was measured by Pearson’s r.
149
11.4 LIST OF CODE TITLES USED FOR THE PRELIMINARY ANALYSIS
The lists of codes were adjusted as necessary to capture the procedures of interest. In particular, codes to identify relevant colectomies and surgeries were shortened in subsequent analyses (i.e., studies 1-3 in the main body of the thesis).
11.4.1 Medication exposures
NSAIDs: aspirin, aspirin and other salicylate combinations, celecoxib, ibuprofen, indomethacin, ketorolac tromethamine, nabumetone, naproxen, rofecoxib.
Corticosteroids: methylprednisolone, prednisolone, prednisone, adrenal combination corticosteroids, dexamethasone, triamcinolone.
Opioids: alfentanil, butorphanol tartrate, codeine, fentanyl, hydromorphone, meperidine, methadone, morphine sulfate, nalbuphine, narcotic analgesic combinations (includes combinations with codeine and acetaminophen; codeine, acetaminophen, butalbital and caffeine; dihydrocodeine, acetaminophen and caffeine; hydrocodone and acetaminophen; hydrocodone and ibuprofen; opium and belladonna; oxycodone and acetaminophen; oxycodone and ibuprofen; meperidine and promethazine; propoxyphene and acetaminophen; and tramadol and acetaminophen), nonnarcotic analgesic and barbiturate combinations (includes combinations with acetaminophen and butalbital; acetaminophen, butalbital and caffeine; and aspirin, butalbital and caffeine), oxycodone, remifentanil, tramadol.
11.4.2 Imaging procedures
Abdominal imaging: percuaneous hepatic cholangiogram, intravenous cholangiogram, intraoperative cholangiogram, other cholangiogram, other biliary tract x-ray, barium
150 swallow, upper GI series, small bowel series, lower GI series, other x-ray of intestines, contrast pancreatogram, other digestive tract x-ray, soft tissue x-ray of abdomen, computerized axial tomography of abdomen, other abdomen tomography, sinogram of abdominal wall, abdominal lymphangiogram, other soft tissue x-ray of abdominal wall, pelvic opaque dye contrast radiography, other x-ray of abdomen, diagnostic ultrasound of digestive system, diagnostic ultrasound of abdomen and retroperitoneum, magnetic resonance imaging of pelvis, prostate, and bladder, magnetic resonance imaging of other site, including abdomen.
Endoscopy: transabdominal endoscopy of small intestines, endoscopy of small intestines through artificial stoma, other endoscopy of small intestines, closed (endoscopic) biopsy of small intestine, EGD with closed (endoscopic) biopsy, transabdominal endoscopy of large intestine, endoscopy of large intestine through artificial stoma, colonoscopy, flexible sigmoidoscopy, closed (endoscopic) biopsy of large intestine, operative esophagoscopy by incision, esophagoscopy through artificial stoma, other esophagoscopy, closed [endoscopic] biopsy of esophagus, transabdominal proctosigmoidoscopy, proctosigmoidoscopy through artificial stoma, rigid proctosigmoidoscopy, closed (endoscopic) biopsy of rectum, transabdominal gastroscopy, gastroscopy through artificial stoma, other gastroscopy, closed (endoscopic) biopsy of stomach
11.4.3 Surgical procedures
Ostomies: exteriorization of small intestine, resection of exteriorized segment of small intestine, exteriorization of large intestine, resection of exteriorized segment of large intestine, colostomy (temporary and permanent), ileostomy (temporary and permanent),
151 continent ileostomy, percutaneous (endoscopic) jejunostomy, duodenostomy or feeding enterostomy, proctostomy, anterior resection of rectum with synchronous colostomy, rectorectostomy
Resections: open and other multiple segmental resection of large intestine, open and other cecectomy, open and other right hemicolectomy, open and other resection of transverse colon, open and other left hemicolectomy, open and other sigmoidectomy, other and unspecified partial excision of large intestine, laparoscopic total intra-abdominal colectomy, open total intra-abdominal colectomy, other and unspecified total intra- abdominal colectomy, pull-through resection of rectum, soave submuscosal resection of rectum, laproscopic pull-through resection of rectum, open pull-through resection of rectum, other pull-through resection of rectum, abdominoperineal resection of rectum, abdominoperineal resection of the rectum, laproscopic abdominoperineal resection of the rectum, open abdominoperineal resection of the rectum, other abdominoperineal resection of the rectum, transsacral rectosigmoidectomy, anterior resection of rectum with synchronous colostomy, other anterior resection of rectum, posterior resection of rectum,
Duhamel resection of rectum, partial proctectomy or rectal resection, excision of anus
152
12 APPENDIX B. ADDITIONAL TRENDS IN UC MANAGEMENT
Table 1. Hospitals excluded by year in restricted analyses.
< 6 admissions No colectomies Either Total no. Year n (%) n (%) n (%) hospitals 2003 12 (43) 15 (54) 17 (61) 28 2004 15 (54) 16 (57) 19 (68) 28 2005 11 (33) 18 (55) 20 (61) 33 2006 13 (37) 25 (71) 27 (77) 35 2007 7 (18) 23 (61) 24 (63) 38 2008 8 (20) 24 (60) 27 (68) 40 2009 6 (15) 22 (56) 22 (56) 39 2010 0 (0) 13 (35) 13 (35) 37 2011 3 (8) 13 (35) 13 (35) 37 2012 8 (24) 19 (58) 20 (61) 33 Total 83 (24) 188 (54) 202 (58) 348
LEGEND: Hospitals that did not perform any colectomies were excluded for that year, as well as hospitals that had fewer than 6 admissions for acute severe UC for that year (mean of 20 hospitals (58%) were dropped each year).
153
Figure 1A. Trends in medication exposure over study period.
A 60 55 50 45 40 35 30 25 20 15 10
Mean rate (per 100 patients) 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
Oral prednisone Opioids Mesalamine Metronidazole 6-MP AZA Infliximab NSAIDs Ciprofloxacin Cyclosporine Tacrolimus
B 60 55 50 45 40 35 30 25 20 15 10
Mean rate (per 100 patients) 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
Oral prednisone Opioids Mesalamine Metronidazole 6-MP AZA Infliximab NSAIDs Ciprofloxacin Cyclosporine Tacrolimus
154
LEGEND: All rates of medication are shown above in color (A) and for those with at least a 10% increase or decrease in use over the 10-year study period (B).
155
Figure 2. Trends in procedures over study period.
60 55 50 45 40 35 30 25 20 15 10 Mean rate (per 100 patients) 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
Upper endoscopy Total colectomy Colonoscopy Subtotal colectomy Flexible sigmoidoscopy
LEGEND: Rates of procedures are shown over the study period.
156
Figure 3. Trends in lab testing over study period.
100 90 80 70 60 50 40 30 20 Mean rate (per 100 patients) 10 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year
CBC ESR C. difficile CRP Albumin Liver function panel Electrolytes
LEGEND: Rates for laboratory studies are shown above. Initially, it appeared that testing for C. difficile declined substantially in the last 3 years of the study period. However, it was identified that the flag for this test did not capture C. difficile detection by polymerase chain reaction (PCR) method. Once PCR was included, the rate of C. difficile testing stabilized in the second half of the study period around 80%.
157
Figure 4. Box plots representing distribution of annual means across hospitals.
A 80 60 40 20 Number of primary UC admissions at each hospital at each admissions UC of primary Number 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
B 40 30 20 Mean LOS hospital at Mean each 10 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
158
C 15 10 5 Median LOS hospital at each Median 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
D 20 15 10 age 5 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
159
E 6 4 2 Mean day IV CS initiated at each hospital at each IV day CS initiated Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
F 20 15 10 5 Mean duration of IV hospital CS at duration each Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
160
G 1 .8 .6 .4 .2 Rate of antibiotic use at each hospital at use each of Rate antibiotic 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
H 40 30 20 10 Mean duration of antibiotic at each hospital at each of antibiotic duration Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
161
I 1 .8 .6 .4 .2 Rate of Flagyl use at each hospital at use each of Rate Flagyl 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
J .8 .6 .4 .2 Annual rate of cipro at each hospital at rate of each cipro Annual 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
162
K 1 .8 .6 .4 .2 Annual infection rate at each hospital rate at each infection Annual 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
L .8 .6 .4 .2 Annual rate of infliximab at each hospital at each rate of infliximab Annual 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
163
M 25 20 15 10 5 Mean day infliximab initiated at each hospital at each initiated infliximab day Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
N 1 .8 .6 .4 .2 Annual rate of endoscopy at each hospital at each rate of endoscopy Annual 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
164
O 25 20 15 10 5 Mean day endoscopy performed at each hospital at each performed endoscopy day Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
P 10 8 6 4 2 Mean day colonoscopy performed at each hospital at each performed colonoscopy day Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
165
Q 50 40 30 20 10 Mean day colectomy performed at each hospital at each performed colectomy day Mean 0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
166
13 VITA
Andrew Klink was born in Wisconsin in 1983. He received his Masters in Public
Health in Epidemiology from Drexel University and his Bachelors in Science from the
University of Wisconsin – Madison. Mr. Klink is a peer-reviewer of the American
Journal of Epidemiology and a Research Associate at The Children’s Hospital of
Philadelphia, specializing in pharmacoepidemiology and outcomes research on pediatric chronic diseases. He has co-authored several articles
167