MD, PhD Julien Kirchgesner
email: jkirchgesner@bwh.harvard.edu

PhD Rishi J Desai

MD Maria C Schneeweiss

MD, PhD Laurent Beaugerie

MD, ScD Seoyoung C Kim

MD, ScD Sebastian Schneeweiss

Emulation of a randomized controlled trial in ulcerative colitis with US and French claims data: Infliximab with thiopurines compared to infliximab monotherapy

Keywords: Real world evidence, RCT emulation, Ulcerative colitis, anti-TNF

come

Introduction

Real world evidence (RWE) has been mainly used to inform decisions about treatment safety, while it has been less used to inform decisions about treatment effectiveness and complement evidence provided by randomized controlled trials (RCTs). [START_REF] Schneeweiss | A review of uses of health care utilization databases for epidemiologic research on therapeutics[END_REF][START_REF] Makady | Policies for Use of Real-World Data in Health Technology Assessment (HTA): A Comparative Study of Six HTA Agencies[END_REF][START_REF] Franklin | Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making[END_REF][START_REF] Schneeweiss | Real-World Evidence of Treatment Effects: The Useful and the Misleading[END_REF] This might be related to the challenge of capturing clinically meaningful measures of effectiveness outcomes in electronic health records or medical claims data. [START_REF]Framework for FDA's realworld evidence program[END_REF] The emulation of RCTs by RWE studies may help to calibrate effectiveness outcomes available in real world data (RWD), which could be applied to other treatment comparisons within the same indication. Additionally, the use of multiple or multinational healthcare databases to calibrate RWE against RCTs may help to interpret differences between treatment effect estimates from the two study types, that can be driven by residual bias in RWE but also emulation differences. [START_REF] Franklin | Emulation Differences vs. Biases When Calibrating Real-World Evidence Findings Against Randomized Controlled Trials[END_REF][START_REF] Franklin | Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative[END_REF] In several immune-mediated inflammatory diseases, notably ulcerative colitis (UC), recent phase III RCTs compared the active treatment with placebo, and the optimal position of each drug remains largely unknown. [START_REF] Rubin | ACG Clinical Guideline: Ulcerative Colitis in Adults[END_REF] Using RWE in comparative effectiveness research could help fill evidence gaps left by RCTs.

Infliximab is a tumor necrosis factor antagonist (anti-TNF) approved for the treatment of UC in 2006 in the US and Europe, [START_REF] Rutgeerts | Infliximab for induction and maintenance therapy for ulcerative colitis[END_REF] while thiopurines have been used for the treatment of UC since the nineteen seventies. [START_REF] Jewell | Azathioprine in Ulcerative Colitis: Final Report on Controlled Therapeutic Trial[END_REF] The SUCCESS trial was an RCT conducted between 2007 and 2010 assessing the effectiveness of infliximab and thiopurines combined compared to either infliximab or thiopurines alone in patients with UC naïve to anti-TNF. [START_REF] Panaccione | Combination Therapy With Infliximab and Azathioprine Is Superior to Monotherapy With Either Agent in Ulcerative Colitis[END_REF] It concluded that the combination therapy of infliximab and thiopurines was superior to treatment with its component alone.

The aim of this study was to calibrate RWE against RCTs in UC by emulating the SUCCESS trial, studying the effectiveness of a biologic agent in patients with UC using claims data from the U.S. and France.

Methods

Data source

We conducted a cohort study using two U.S. health care claims databases, IBM MarketScan French population. Comprehensive data on demographics and procedures performed during outpatient visits or inpatient stays, and outpatient filled prescription records are available in these three databases and can be tracked longitudinally. Additionally, the two U.S databases contain diagnoses during outpatient visits and the SNDS contains the patient's status with respect to full reimbursement of care for long-term diseases (LTDs), which includes UC and allows to assess the date of UC diagnosis. [START_REF] Kirchgesner | Therapeutic management of inflammatory bowel disease in real-life practice in the current era of anti-TNF agents: analysis of the French administrative health databases 2009-2014[END_REF] The study was approved by the institutional review board of the Brigham and Women's Hospital, and the French Data Protection Authority. Patient informed consent was not required because the databases were deidentified.

Study population

Inclusion and exclusion criteria were tailored to replicate SUCCESS whenever possible. [START_REF] Panaccione | Combination Therapy With Infliximab and Azathioprine Is Superior to Monotherapy With Either Agent in Ulcerative Colitis[END_REF] The SUCCESS trial included adult patients with moderate to severe active UC with a Mayo score of 6 to 12 points, despite use of corticosteroids and/or aminosalicylates. Patients were naïve to anti-TNF and should be either naïve to thiopurines or free from thiopurines for at least 3 months prior to the randomization. Finally, 90% of patients included in SUCCESS were naïve to thiopurines. To emulate this design, we identified all patients aged 18 years or older initiating infliximab after at least 180 days of continuous enrollment for patients included in MarketScan and Optum databases, and with at least one visit for UC using the International Classification of Diseases 9 th (ICD-9) or 10 th (ICD-10). In the SNDS database, UC diagnosis was based on previous published algorithms, [START_REF] Kirchgesner | Therapeutic management of inflammatory bowel disease in real-life practice in the current era of anti-TNF agents: analysis of the French administrative health databases 2009-2014[END_REF][START_REF] Lemaitre | Association Between Use of Thiopurines or Tumor Necrosis Factor Antagonists Alone or in Combination and Risk of Lymphoma in Patients With Inflammatory Bowel Disease[END_REF][START_REF] Kirchgesner | Risk of acute arterial events associated with treatment of inflammatory bowel diseases: nationwide French cohort study[END_REF][START_REF] Kirchgesner | Risk of serious infections with vedolizumab versus tumor necrosis factor antagonists in patients with inflammatory bowel disease[END_REF] and the date of UC diagnosis was defined as the earliest diagnosis date either from hospital discharge diagnosis or from LTD diagnosis. Cohort entry date was defined as the date of infliximab initiation and patients were followed from the day after cohort entry date to 112 days after cohort entry date (week 16).

In analogy to the SUCCESS trial, we excluded patients previously exposed to any anti-TNF agent any time prior infliximab initiation. We additionally excluded patients exposed to methotrexate, since infliximab can be combined with methotrexate instead of thiopurines as a combination therapy. [START_REF] Rubin | ACG Clinical Guideline: Ulcerative Colitis in Adults[END_REF] Thiopurines exposure was only allowed in the month prior infliximab initiation, and patients exposed to thiopurines more than one month before infliximab initiation were excluded. Previous treatment exposure was assessed within all data available in the three databases to minimize the risk of treatment misclassification. Patients with tuberculosis, opportunistic infections, hepatitis B and C within the previous 6 months were excluded. We also excluded patients with systemic lupus erythematosus, heart failure, organ transplant, HIV infection, multiple sclerosis, previous colectomy, ostomy, and previous history of cancer (excluding non-melanoma skin cancer). Since patients hospitalized for extensive severe UC were excluded in SUCCESS, patients hospitalized at infliximab initiation or with a diagnosis of stricture or abscess within the previous 6 months were excluded. The only exclusion criteria included in the SUCCESS trial and not considered in our study was colonic dysplasia, since it is not collected in administrative health databases. Lastly, additional exclusion criteria were included compared to the SUCCESS trial. First, in order to only include patients among whom infliximab was prescribed for UC, patients with a diagnosis code related to Crohn's disease, or diagnosed with rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, and psoriasis were excluded. Second, patients previously exposed to biologics and immunosuppressants that were available after the publication of the SUCCESS trial in 2014 (natalizumab, vedolizumab, ustekinumab, and tofacitinib) were excluded.

Treatment groups

Combination therapy was defined as starting thiopurines (either azathioprine or 6mercaptopurine) within 30 days before infliximab initiation, as thiopurines may be started before the first infliximab infusion and take up to 3 months to be effective as monotherapy. [START_REF] Rubin | ACG Clinical Guideline: Ulcerative Colitis in Adults[END_REF] Infliximab monotherapy was defined as infliximab initiation without thiopurines exposure any time prior. We did not consider patients only exposed to thiopurines monotherapy, since recent guidelines recommend against its use as monotherapy for induction of remission in patients with UC. 8

Effectiveness measure

In the SUCCESS trial, the primary end point was corticosteroid-free clinical remission at week 16, defined by a total Mayo score of 2 points or less, with no individual subscore exceeding 1 point and without any systemic corticosteroid use. Since the Mayo score is not available in any of the study databases, we developed an effectiveness outcome measure based on three surrogate endpoints: (1) hospitalization related to UC or colectomy; (2) treatment switch to another biologic (i.e., adalimumab, certolizumab pegol, golimumab, natalizumab, vedolizumab, ustekinumab) or immunosuppressant (tofacitinib); (3) exposure to systemic corticosteroids at week 16. The primary outcome, treatment failure, was defined as the occurrence of one of these three endpoints. Related codes are summarized in Supplementary Table 1.

Follow-up started the day after cohort entry and ended on the earliest occurrence of 112 days of follow-up, outcome occurrence, or death, for all subjects started on either one of the treatments.

Patient characteristics

Pre-treatment patient characteristics and markers of UC severity were assessed, including demographics and comorbidities. Comorbidities were assessed in the 180 days before cohort entry in MarketScan and Optum and in all data available in the SNDS, including previous serious infections, Clostridioides difficile infection, cardiovascular disease, chronic kidney failure, chronic liver disease, chronic pulmonary disease, venous thromboembolism, and diabetes. UC severity and healthcare use were assessed in the 180 days before cohort entry in the three databases. UC severity was assessed by corticosteroids and aminosalicylates exposure, occurrence of UC-related hospitalization, abdominal imaging, gastrointestinal endoscopy, fecal pathogen, and the number of C-reactive protein tests ordered. Healthcare use was assessed by hospitalizations not related to UC and the number of gastroenterologist visits.

Statistical analysis

Multivariable logistic regression models estimated patients' propensity score (PS) values, that is, the predicted probability of combination therapy with infliximab and thiopurines versus infliximab monotherapy conditioning on all the confounders listed above and the year of cohort entry. [START_REF] Glynn | Indications for propensity scores review of their use in pharmacoepidemiology[END_REF] We used PS-matching with a fixed ratio of 1:1 comparing combination therapy and infliximab monotherapy with a matching 'caliper' of 0.02 on the PS scale. [START_REF] Rassen | One-to-many propensity score matching in cohort studies[END_REF] After matching, standardized differences were calculated to assess balance between patients treated with combination therapy and their matched controls treated with infliximab monotherapy. [START_REF] Franklin | Metrics for covariate balance in cohort studies of causal effects[END_REF] Logbinomial regression models were used to estimate adjusted risk ratios (RR) with their 95% confidence intervals (CIs) comparing the risk of treatment failure associated with combination therapy versus infliximab monotherapy. [START_REF] Robbins | What's the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes[END_REF] Cohort-specific RRs were combined by an inverse variance-weighted, fixed-effects model.

Additionally, the risk of each individual component of the composite endpoint was assessed in secondary analyses, and subgroup analysis was stratified by exposure to corticosteroids at infliximab initiation. Finally, we performed sensitivity analyses to test the robustness of our results: (1) variables with an absolute standardized difference greater than 0.1 after PSmatching were included as covariate in log-binomial regression models; (2) patients initiating combination therapy with infliximab and thiopurines were PS matched to patients initiating infliximab monotherapy with a variable ratio up to 1:4; (3) the required enrollment period was extent to 365 days in the US databases; (4) treatment switch was excluded from the outcome definition; (5) since some patients may start thiopurines after infliximab initiation, patients starting thiopurines in the first month after infliximab initiation were excluded.

Analyses were performed using the validated Aetion Evidence Platform (V 4.10) using R [START_REF]Software for real-world data analysis[END_REF][START_REF] Wang | Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases[END_REF][START_REF] Patorno | Using Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride[END_REF] and SAS (version 9.4) statistical software (SAS Institute).

Results

Patient characteristics

A total of 22 051 patients with UC who initiated infliximab were identified across the three databases. After applying exclusion criteria (Figure 1), 7850 immunosuppressants and anti-TNF naive patients with UC were included, initiating either combination therapy with infliximab and thiopurines (n=644) or infliximab monotherapy (n=7206). Overall, 96.4% of patients treated with combination therapy were matched to patients with infliximab monotherapy and 93.0% of patients treated with infliximab monotherapy were able to match with at least one patient treated with combination therapy.

In the matched population of 620 pairs, mean age at cohort entry was between 39 and 40 years, which was similar to SUCCESS (Table 1). [START_REF] Panaccione | Combination Therapy With Infliximab and Azathioprine Is Superior to Monotherapy With Either Agent in Ulcerative Colitis[END_REF] Exposure to corticosteroids at infliximab initiation was higher compared to SUCCESS (69.7% compared to 40.5% in SUCCESS). Four covariates had an absolute standardized difference greater than 0.1 (Supplementary Figure 1). Baseline characteristics before PS-matching are provided in Supplementary Table 2, and year of cohort entry before and after PS-matching are provided in Supplementary Table 3.

Combination therapy versus infliximab monotherapy

In the PS matched cohort, 124 (20.0%; 95% CI 17.0-23.3) and 170 (27.4%; 24.1-31.1) patients were in treatment failure at week 16 after initiating combination therapy and infliximab monotherapy, respectively. Cohorts-specific rates of treatment failure ranged from 17.1% to 21.2% in patients initiating combination therapy and from 25.8% to 28.2% in patients initiating infliximab monotherapy. (Table 2) Exposure to corticosteroids at week 16 was the most frequent outcome, accounting for 54% and 50% of outcomes in patients initiating combination therapy and infliximab monotherapy. Treatment switch only accounted for 15% and 12% of outcomes in patients initiating combination therapy and infliximab monotherapy. The proportion of patients with treatment failure before PS-matching is provided in Supplementary Table 4.

Patients initiating combination therapy had a 27% decreased risk of treatment failure compared to patients initiating infliximab monotherapy (RR, 0.73, 95% CI 0.60-0.90). A similar trend was observed across the three databases: RR, 0.76 (95% CI 0.57-1.02) in MarketScan, RR, 0.82 (95% CI 0.54-1.24) in Optum and RR, 0.61 (95% CI 0.41-0.90) in SNDS. RRs of treatment failure in the emulated cohort and in the SUCCESS trial are provided in Figure 2. The reduced risk of treatment failure with combination therapy was observed for two secondary endpoints, with a 40% (12-58%) reduced risk of hospitalization related to UC or colectomy and a 26% (4-43%) reduced risk of persistent corticosteroids exposure at week 16 (Figure 3). A similar trend was observed for treatment switch, although not statistically significant (RR, 0.85, 95% CI, 0.49-1.47).

Subgroup and sensitivity analyses

Results were similar in subgroups of patients according to corticosteroids exposure at infliximab initiation (RR, 0.74 [95%CI, 0.59-0.93] in patients treated with corticosteroids and 0.75 |95%CI, 0.46-1.22] in patients not treated with corticosteroids) (Supplementary Table 5).

Sensitivity analyses revealed similar results (Supplementary Table6). After PS matching with a 1:4 variable ratio, the risk of treatment failure was decreased by 21% (3-36%) with combination therapy compared to infliximab monotherapy.

Discussion

This multi-database RWE cohort study emulating, the SUCCESS trial resulted in consistent findings by assessing effectiveness of combination therapy with infliximab and thiopurines compared to infliximab monotherapy in patients with UC. The real-world effectiveness outcome measure was clinically relevant and based on a composite of treatment failure including hospitalization related to UC or colectomy, treatment switch, or corticosteroid use by week 16.

Findings were robust across all three claims databases and multiple sensitivity analyses.

Calibrating clinically meaningful real-world effectiveness outcome measures is of major importance for clinical and regulatory decision making. In UC, all recent phase III RCTs compared the active treatment with placebo in immunosuppressant naïve patients, whereas standard first line treatment is available and the European Medicines Agency recommended a direct comparison with current generally accepted standard first line treatment in this setting. [START_REF] Ema | Guideline on the development of new medicinal products for the treatment of Ulcerative Colitis[END_REF] Once a valid real-world effectiveness outcome is agreed upon, real-world evidence may complement existing placebo-controlled trials by providing effectiveness estimates in head-tohead comparisons in addition to the usual safety studies. [START_REF] Kirchgesner | Risk of serious infections with vedolizumab versus tumor necrosis factor antagonists in patients with inflammatory bowel disease[END_REF] This would also expand the evidence base to understanding treatment effectiveness in clinical practice and include patients who were excluded from RCTs. [START_REF] Ha | Patients Enrolled in Randomized Controlled Trials Do Not Represent the Inflammatory Bowel Disease Patient Population[END_REF] Starting from a large source population of patients initiating infliximab, a total of only 644 patients naïve to anti-TNFs and immunosuppressants and initiating infliximab and thiopurines were included. This small study size is a consequence of the restrictive exclusion criteria imposed by emulating SUCCESS, since infliximab is mainly used in acute severe UC during hospitalization and other immunosuppressants and biologics such as adalimumab may be initiated before infliximab in patients with moderate to severe UC. [START_REF] Rubin | ACG Clinical Guideline: Ulcerative Colitis in Adults[END_REF] While our study design was tailored to emulate the design of the SUCCESS trial as much as possible, this highlights the opportunities of RWE to complement and expand beyond the existing randomized trial evidence base. [START_REF] Franklin | Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative[END_REF] Clearly, more RCT evidence will help everybody but in its absence RWE will be able to expand our understanding of treatments in UC.

To our knowledge, this is the first study replicating findings of RCTs in UC based on both U.S and French administrative health databases. Performing the same analysis in RWE databases from different countries allowed us to assess whether different healthcare schemes and potential prescribing patterns may impact the treatment effect observed. While healthcare is guaranteed for all French residents and patients are followed from birth to emigration or death in the French database, the U.S databases only included commercially insured patients and health insurance enrollment changes may reduce the enrollment period to assess covariates.

We observed similar results in the stratified analysis according to each database, which suggest that these differences had a minimal impact on the treatment estimate observed.

Although our design was as close as possible to that of the SUCCESS trial, we observed differences in terms of co-treatment exposure and disease severity. Higher rates of corticosteroids exposure reported in our study population may suggest that patients initiating infliximab in real world settings have a more severe disease compared to patients included in the RCTs. In the SUCCESS trial, the exclusion of patients hospitalized for severe extensive UC was based on the investigator judgment that the patient was likely to require colectomy within 12 weeks. Conversely, in real world settings infliximab is more frequently used compared to other biologics in patients experiencing acute severe UC. [START_REF] Rubin | ACG Clinical Guideline: Ulcerative Colitis in Adults[END_REF] However, our subgroup analysis stratified according to baseline exposure to corticosteroid provided consistent results.

Additionally, as administrative health databases do not provide data on clinical or endoscopic disease activity, we were unable to use the same outcome definition as used in the SUCCESS trial. We used a composite endpoint of treatment failure based on hospitalization related to UC or colectomy, treatment switch to another biologic or immunosuppressants, or corticosteroids continuation at week 16. Our definition may select higher degree of disease activity, as we reported a lower rate of treatment failure compared to SUCCESS. It highlights the fact that the design of an emulated trial based on administrative health databases cannot perfectly emulate the design of a highly-controlled RCT that includes effectiveness outcomes not collected in administrative health databases. Thus, differences in design between RCTs and their emulations in RWD should be comprehensively reported and considered in the interpretation.

Treatment switch to another biologic or immunosuppressant was not statistically significantly reduced in patients treated with combination therapy compared to infliximab monotherapy.

Treatment switch may also include switch related to intolerance or safety. However, point estimates were in the same range of those of the two other secondary outcomes, and treatment switch only accounted for 15% of the treatment failure reported. Lastly, since endoscopic and histological data were not collected in the three databases, residual confounding by these parameters cannot be entirely ruled out. We adjusted for the number of C-reactive protein tests ordered, while we did not adjust for the number of fecal calprotectin test ordered. Indeed, fecal calprotectin was not used in the early part of our inclusion period and fecal calprotectin is not reimbursed in France.

In conclusion, this study based on three large population-based claims databases of patients with UC in both the US and France provides strong support that clinically-relevant real-world effectiveness outcome measures can be calibrated by comparing RCTs and their emulations in RWD. These measures can be further used to assess effectiveness of other treatments in patients with similar diseases.

Author Contributions:

A list of each author's contributions. The standard contributions include: J.K. and S.S. wrote manuscript, J.K., R.J.D., M.S., L.B., S.C.K., and S.S. designed research, J.K. and S.S. performed research, JK. analyzed data.

(

  MarketScan) 2004-2018 and Optum's Clinformatics® Data Mart Database (Optum) 2005-2019, and the French administrative health database 2009-2018 (Système National des Données de Santé, SNDS). Patients enrolled in the MarketScan and Optum databases are representative of a commercially insured population in the U.S.; the SNDS covers 95% of the

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Study Population Flowchart

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Study Population Flowchart

Table Legends :Table 1 .

 Legends1 Characteristics of study patients, propensity score-matched with a 1:1 fixed ratio

Table 2 .

 2 Outcome risk after 1:1 propensity-score matching

Table 1 . Characteristics of study patients treated with infliximab and thiopurines or infliximab monotherapy, propensity score-matched with a 1:1 fixed ratio

 1

		MarketScan	Optum	SNDS	SUCCESS Trial
		Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab
	Variable	Mono.	Combo.	Mono.	Combo.	Mono.	Combo.	Mono.	Combo.
		(n=288)	(n=288)	(n=151)	(n=151)	(n=181)	(n=181)	(n=78)	(n=80)
	Age, mean (SD), year	39.8 (14.0) 40.4 (14.0) 40.6 (15.4) 40.6 (13.7) 39.0 (14.1) 39.8 (15.4) 38.5 (12.7) 38 (12.2)
	Sex
	Male	140 (48.6) 142 (49.3) 74 (49.0)	70 (46.4)	91 (50.3)	94 (51.9)	42 (53.9) 48 (60.0)
	Female	148 (51.4) 146 (50.7) 77 (51.0)	81 (53.6)	90 (49.7)	87 (48.1)	36 (46.1) 32 (40.0)
	UC disease duration, mean (SD), (years)	-	-	-	-	4.4 (5.7)	3.9 (5.3)	6.3 (6.5)	5.2 (5.1)
	Aminosalicylates (oral) at cohort entry	135 (46.9) 147 (51.0) 71 (47.0)	81 (53.6)	94 (51.9)	94 (51.9)	-	-
	Corticosteroids (oral)
	within 180 days before cohort entry	274 (95.1) 272 (94.4) 134 (88.7) 135 (89.4) 157 (86.7) 153 (84.5)	-	-
	at cohort entry	216 (75.0) 217 (75.3) 101 (66.9) 95 (62.9) 119 (65.7) 116 (64.1) 31 (39.7) 38 (47.5)
	Opioids	144 (50.0) 140 (48.6) 58 (38.4)	58 (38.4)	17 (9.4)	19 (10.5)	-	-
	UC activity assessment a
	Hospitalization related to ulcerative colitis	140 (48.6) 136 (47.2) 42 (27.8)	49 (32.5)	48 (26.5)	58 (32.0)	-	-
	Abdominal imaging	79 (27.4)	67 (23.3)	49 (32.5)	50 (33.1)	52 (28.7)	57 (31.5)	-	-
	Lower GI endoscopy	251 (87.2) 244 (84.7) 132 (87.4) 126 (83.4) 147 (81.2) 147 (81.2)	-	-
	Upper GI endoscopy	43 (

Table 2 . Outcome risk a after 1:1 propensity-score matching

 2 Secondary outcomes are assessed without censoring at the first outcome occurrence in case of multiple outcome occurrences during follow-up. Abbreviation: UC, ulcerative colitis

	-	-

a 16-week risk b

Table 1 .

 1 Effectiveness outcome measure with related codes Page 3. Supplementary Table 2. Characteristics of study patients treated with infliximab and thiopurines or infliximab monotherapy, before propensity score-matching Page 4. Supplementary Table 3. Outcome risk, before propensity-score matching Page 5. Supplementary Table 4. Relative risk of treatment failure in patients treated with combination therapy versus infliximab monotherapy, subgroups analyses Page 6. Supplementary Table 5. Relative risk of treatment failure in patients treated with combination therapy versus infliximab monotherapy, sensitivity analyses Page 7. Supplementary Figure 1. Standardized differences before and after propensity score matching (MarketScan [A], Optum[B], and SNDS [C])

	Supplementary Material
	Page 2. Supplementary

Table 1 . Effectiveness outcome measure with related codes

 1

	Outcomes			ICD-10	ICD-9	Procedures	Anatomical Therapeutic Chemical (ATC) classification system code
	Hospitalization related to ulcerative colitis or colectomy	K51; K92.2; R10 K56; K62.5; K63.0; K63.1; K60; K63.2; K65.0; K65.1;	556; 560; 567.21; 578.9; 789.0 567.22; 567.29; 569.3; 569.5; 569.81; 569.83;	Colectomy	-
								L04AB04	(adalimumab),	L04AB05
	Switch	to	another				(certolizumab	pegol),	L04AB06
	biologic		drug	or	-	-	-	(golimumab), L04AA23 (natalizumab),
	tofacitinib							L04AA33	(vedolizumab),	L04AC05
								(ustekinumab), or L04AA29 (tofacitinib)
								H02AB04	(methylprednisolone);
	Exposure			to				H02AB06 (prednisolone, only IV or oral
	corticosteroids at week	-	-	-	intake); H02AB07 (prednisone, only IV or
	16							oral intake); H02AB10 (cortisone, only IV
								or oral intake)

Table 2 . Characteristics of study patients treated with infliximab and thiopurines or infliximab monotherapy, before propensity score-matching

 2

						MarketScan, n (%)	Optum, n (%)	SNDS, n (%)
						Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab
	Variable					Mono.	Combo.	Mono.	Combo.	Mono.	Combo.
						(n=4614)	(n=288)	(n=1562)	(n=152)	(n=1030)	(n=204)
	Age, mean (SD), years			40.6 (14.5)	40.4 (14.0)	42.0 (15.9)	40.4 (13.8)	41.7 (16.1)	39.1 (15.1)
	Sex
	Male					2174 (47.1)	142 (49.3)	736 (47.1)	70 (46.1)	522 (50.7)	107 (52.5)
	Female					2440 (52.9)	146 (50.7)	826 (52.9)	82 (53.9)	508 (49.3)	97 (47.5)
	IBD disease duration, mean (SD), years	-	-	-	-	4.1 (5.6)	3.8 (5.2)
	Aminosalicylates (oral) at cohort entry	1450 (31.4)	147 (51.0)	658 (42.1)	82 (53.9)	519 (50.4)	106 (52.0)
	Corticosteroids (oral)
	within 180 days before cohort entry	2556 (55.4)	272 (94.4)	1156 (74.0)	136 (89.5)	744 (72.2)	175 (85.8)
	at cohort entry				1770 (38.4)	217 (75.3)	737 (47.2)	96 (63.2)	490 (47.6)	139 (68.1)
	Opioids a					1395 (30.2)	140 (48.6)	470 (30.1)	59 (38.8)	108 (10.5)	24 (11.8)
	Ulcerative	colitis	activity
	assessment a
	Hospitalization ulcerative colitis	related	to	1114 (24.1)	136 (47.2)	377 (24.1)	49 (32.2)	236 (22.9)	68 (33.3)
	Abdominal imaging			833 (18.1)	67 (23.3)	473 (30.3)	51 (33.6)	281 (27.3)	67 (32.8)
	Lower GI endoscopy			2983 (64.7)	244 (84.7)	1071 (68.6)	127 (83.6)	693 (67.3)	169 (82.8)
	Upper GI endoscopy			417 (9.0)	37 (12.8)	189 (12.1)	17 (11.2)	194 (18.8)	51 (25.0)
	CRP tests ordered, mean (SD)	0.65 (1.07)	0.98 (1.13)	1.02 (1.68)	1.09 (1.42)	2.58 (2.41)	3.30 (2.83)
	Fecal pathogen tests ordered		1664 (36.1)	157 (54.5)	669 (42.8)	79 (52.0)	251 (24.4)	78 (38.2)
	Comorbidities
	Clostridioides difficile infection	198 (4.3)	13 (4.5)	70 (4.5)	9 (5.9)	8 (0.8)	5 (2.5)
	Serious infection				96 (2.1)	9 (3.1)	28 (1.8)	1 (0.7)	10 (1.0)	2 (1.0)
	Cardiovascular disease			262 (5.7)	16 (5.6)	99 (6.3)	6 (3.9)	54 (5.2)	10 (4.9)
	Chronic kidney failure			33 (0.7)	0 (0.0)	28 (1.8)	3 (2.0)	7 (0.7)	1 (0.5)
	Chronic liver disease			85 (1.8)	0 (0.0)	56 (3.6)	5 (3.3)	11 (1.1)	2 (1.0)
	Chronic pulmonary disease		383 (8.3)	41 (14.2)	167 (10.7)	21 (13.8)	199 (19.3)	36 (17.6)
	Venous thromboembolism		124 (2.7)	8 (2.8)	38 (2.4)	7 (4.6)	18 (1.7)	3 (1.5)
	Diabetes					307 (6.7)	19 (6.6)	124 (7.9)	12 (7.9)	74 (7.2)	8 (3.9)
	Healthcare use characteristics a
	Hospitalizations not related to ulcerative colitis	436 (9.4)	31 (10.8)	134 (8.6)	11 (7.2)	90 (8.7)	19 (9.3)
	Gastroenterologist visits, mean (SD)	4.78 (5.41)	5.55 (6.44)	7.70 (7.13)	9.40 (8.08)	5.94 (6.36)	7.68 (7.15)
						protein

a Assessed within 180 days before cohort entry; Abbreviations: Mono, monotherapy; Combo, combination therapy; UC, ulcerative colitis; CRP, C-reactive

Table 4 . Outcome risk a , before propensity-score matching

 4

			MarketScan, n (%)	Optum, n (%)	SNDS, n (%)	Overall cohort, n (%)
			Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab	Infliximab
	Variable		monotherapy	and thiopurines	monotherapy	and thiopurines	monotherapy	and thiopurines	monotherapy	and thiopurines
			(n=4614)	(n=288)	(n=1562)	(n=152)	(n=1030)	(n=204)	(n=7206)	(n=644)
	Treatment failure		755 (16.4)	61 (21.2)	287 (18.4)	32 (21.1)	263 (25.5)	34 (16.7)	1305 (18.1) 127 (19.7)
	Hospitalization related to UC or	297 (6.4)	18 (6.3)	99 (6.3)	12 (7.9)	118 (11.5)	12 (5.9)	514 (7.1)	42 (6.5)
	colectomy b
	Switch another biologic to	103 (2.2)	13 (4.5)	41 (2.6)	4 (2.6)	39 (3.8)	8 (3.9)	183 (2.5)	25 (3.9)
	or tofacitinib b
	Corticosteroids exposure	at	461 (10.0)	40 (13.9)	196 (12.5)	19 (12.5)	171 (16.6)	23 (11.3)	828 (11.5)	82 (12.7)
	week 16 b

a 16-week risk b Secondary outcomes are assessed without censoring at the first outcome occurrence in case of multiple outcome occurrences during follow-up. Abbreviation: UC, ulcerative colitis

a Assessed within 180 days before cohort entry; Abbreviations: Mono, monotherapy; Combo, combination therapy; UC, ulcerative colitis; CRP, C-reactive protein

(0.3) 2 (0.7) 3 (1.0) 2 (0.7)

Supplementary


MD, PhD Julien Kirchgesner
email: jkirchgesner@bwh.harvard.edu

PhD Rishi J Desai

MD, PhD Laurent Beaugerie

MD, ScD Sebastian Schneeweiss

MD, ScD Seoyoung C Kim

Risk of serious infections with vedolizumab versus tumor necrosis factor antagonists in patients with inflammatory bowel disease

Keywords: Kirchgesner, Desai, Kim. Supervision: Kirchgesner, Kim Inflammatory bowel disease, vedolizumab, serious infections

Background and aims: The risk of serious infections associated with vedolizumab in patients with inflammatory bowel disease (IBD) is uncertain. We assessed the risk of serious infections associated with use of vedolizumab versus anti-TNF in patients with IBD, according to IBD subtype and previous exposure to anti-TNF.

Methods: Based on two U.S. nationwide commercial insurance databases and the French nationwide health insurance database, anti-TNF naïve and experienced patients diagnosed with Crohn's disease (CD) and ulcerative colitis (UC) aged 18 years or older who initiated vedolizumab or an anti-TNF agent after 2010 were identified. Hazard ratios for serious infections comparing vedolizumab and anti-TNF were estimated in propensity score matched cohorts.

Results: Among 8768 vedolizumab and 26,656 anti-TNF initiators included after 1:4 variable ratio propensity score matching, 893 serious infections occurred during 37,725 person-years of follow-up. The risk of serious infections was not different between vedolizumab and anti-TNF in the overall IBD cohort (HR, 0.95; 95%CI, 0•79-1.13), while the risk was decreased for vedolizumab users in patients with UC (HR, 0.68; 95%CI, 0•50-0.93), but not CD (HR, 1.10; 95%CI, 0.87-1.38). In patients with UC, vedolizumab was consistently associated with lower risk of serious infections after exclusion of gastrointestinal infections (HR, 0•59; 95%CI, 0.39-0.90).

Conclusion: While the risk of serious infections associated with vedolizumab was not different compared to anti-TNF in the overall group of patients with IBD, the risk varied according to IBD subtype, by decreasing in patients with UC, but not CD. These findings may help to clarify the optimal position of vedolizumab in the therapeutic management of IBD.

Introduction

Vedolizumab is the first biologic agent approved for the treatment of both inflammatory bowel diseases (IBD), ulcerative colitis (UC) and Crohn's disease (CD), after the era of tumor necrosis factor antagonists (anti-TNF). [START_REF] Feagan | Vedolizumab as Induction and Maintenance Therapy for Ulcerative Colitis[END_REF][START_REF] Sandborn | Vedolizumab as Induction and Maintenance Therapy for Crohn's Disease[END_REF] By specifically binding to the lymphocyte α4β7 integrin, vedolizumab may impair the migration of lymphocytes into gastrointestinal mucosa and act as a gut-selective immunomodulator. Its theoretical gut-selectivity may decrease the risk of extra-intestinal immunosuppression-related adverse events, notably serious infections.

While population-based studies reported an increased risk of serious infections associated with anti-TNF, [START_REF] Kirchgesner | Risk of Serious and Opportunistic Infections Associated With Treatment of Inflammatory Bowel Diseases[END_REF] there is limited evidence on the risk associated with vedolizumab, mainly based on randomized controlled trials (RCTs) data. [START_REF] Colombel | The safety of vedolizumab for ulcerative colitis and Crohn's disease[END_REF][START_REF] Feagan | Respiratory Tract Infections in Patients With Inflammatory Bowel Disease: Safety Analyses From Vedolizumab Clinical Trials[END_REF] The most common infection in RCTs of vedolizumab was upper respiratory tract infection and one RCT reported numerically higher rates of nasopharyngitis with vedolizumab compared to placebo. [START_REF] Sandborn | Vedolizumab as Induction and Maintenance Therapy for Crohn's Disease[END_REF][START_REF] Colombel | The safety of vedolizumab for ulcerative colitis and Crohn's disease[END_REF] These results may challenge the concept of vedolizumab gut-selectivity, with additional mechanisms of action besides blocking gut lymphocytes trafficking. [START_REF] Zeissig | Vedolizumab is associated with changes in innate rather than adaptive immunity in patients with inflammatory bowel disease[END_REF] Conversely, a pooled safety analysis including RCTs did not report an increased risk of serious infections, although Clostridioides difficile infections occurred only in patients exposed to vedolizumab. [START_REF] Colombel | The safety of vedolizumab for ulcerative colitis and Crohn's disease[END_REF] However, the selected nature of patients in clinical trials, with a higher proportion of patients without comorbidities may not reflect the global risk in patients with IBD. A recent retrospective multicenter cohort study including 659 patients with CD treated with vedolizumab and followed in IBD tertiary care centers did not report differences in risk of serious infections between vedolizumab and anti-TNF, but suggested risk differences according to previous anti-TNF exposure, although lacking power to appropriately detect risk differences. [START_REF] Bohm | Comparative safety and effectiveness of vedolizumab to tumour necrosis factor antagonist therapy for Crohn's disease[END_REF] Finally, no population-based study compared the risk of serious infections between vedolizumab and anti-TNF and the optimal position of vedolizumab compared to anti-TNF remains uncertain. Large population-based studies are needed to better define the benefit-risk balance of vedolizumab.

The aim of this study was to assess the risk of serious infections associated with vedolizumab compared to anti-TNF according to IBD subtype and previous exposure to anti-TNF, using data from two large U.S. and French nationwide population-based cohorts of patients with IBD.

Methods

Data sources

We conducted a cohort study using two U.S. health care claims databases, IBM MarketScan The two U.S. databases contain demographic data and longitudinal claims information, including hospitalization, outpatient visits, procedures, and pharmacy dispensing. The SNDS also includes individuals' status with respect to full reimbursement of care for severe long-term diseases (LTDs), including IBD, which allows to assess the date of IBD diagnosis. [START_REF] Kirchgesner | Therapeutic management of inflammatory bowel disease in real-life practice in the current era of anti-TNF agents: analysis of the French administrative health databases 2009-2014[END_REF] While enrollment and end of follow-up may be related to health insurance enrollment changes in MarketScan and Optum, patients in the SNDS are followed from birth to emigration or death.

The study protocol was approved by the institutional review board of the Brigham and Women's Hospital, Boston, Massachusetts, and the French Data Protection Authority. Patient informed consent was not required because the databases were deidentified.

Study Population

We identified adult (≥18 years) patients with at least one visit for IBD using the International Classification of Diseases (ICD) 9 th or 10 th Revision codes for CD and UC. In case of multiple diagnosis codes related to both CD and UC, a diagnosis of CD was retained in MarketScan and Optum. In the SNDS database, IBD diagnosis was based on previous published algorithms, [START_REF] Kirchgesner | Risk of Serious and Opportunistic Infections Associated With Treatment of Inflammatory Bowel Diseases[END_REF][START_REF] Kirchgesner | Therapeutic management of inflammatory bowel disease in real-life practice in the current era of anti-TNF agents: analysis of the French administrative health databases 2009-2014[END_REF][START_REF] Kirchgesner | Risk of acute arterial events associated with treatment of inflammatory bowel diseases: nationwide French cohort study[END_REF] and the date of IBD diagnosis was defined as the earliest diagnosis date either from hospital discharge diagnosis or from LTD diagnosis. Patients included in MarketScan and Optum databases were required to have continuous enrolment during the baseline period of 180 days before initiation of vedolizumab or one anti-TNF agent (i.e., infliximab, adalimumab, golimumab, or certolizumab pegol). In order to reduce confounding by disease duration or severity, anti-TNF naïve and experienced patients were assessed in separate cohorts. In cohorts including anti-TNF experienced patients, all patients were required to have been previously treated with at least 1 different anti-TNF from the anti-TNF agent initiated at cohort entry. Since vedolizumab is only available in patients previously treated with or intolerant to anti-TNF in France, only anti-TNF experienced patients were considered in the SNDS. Five cohorts were thus considered, three including anti-TNF experienced patients (from MarketScan, Optum, and SNDS) and two including anti-TNF naïve patients (from MarketScan and Optum).

We excluded patients with HIV infection, congenital immunodeficiency, organ transplantation, previous history of cancer (excluding non-melanoma skin cancer), and previous exposure to etanercept, natalizumab, ustekinumab, and tofacitinib. Since co-medication initiated during hospitalization, such as corticosteroids and immunomodulators, are not accurately assessed in the datasets, patients who started anti-TNF or vedolizumab during a hospitalization were also excluded. Lastly, patients with a diagnosis of rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, and psoriasis were excluded in order to only include patients among whom anti-TNF was prescribed for IBD.

Patients initiating vedolizumab or anti-TNF after January 1, 2010, were considered for inclusion, and the cohort entry date was defined as the date of treatment initiation. Treatment initiation was defined as not having used the drug of interest any time prior since first identification in the database. Patients were followed up from the day after the cohort entry date until outcome occurrence, death, switching to a drug in the other exposure group, discontinuing treatment with a grace period of 60 days, plan disenrollment (MarketScan and Optum), or end of study period, whichever occurred first. Patients were allowed to enter the study cohort only once.

Outcomes

The primary outcome was any serious infection, defined as a diagnosis of infection requiring hospitalization (related ICD codes as primary diagnosis), based on a previously published algorithm. [START_REF] Kirchgesner | Risk of Serious and Opportunistic Infections Associated With Treatment of Inflammatory Bowel Diseases[END_REF] Within the SNDS, the diagnoses and types of serious infection have been shown to be accurate in 97% and 98% of the cases, respectively. [START_REF] Sahli | Positive predictive values of selected hospital discharge diagnoses to identify infections responsible for hospitalization in the French national hospital database[END_REF] Since specific safety signals have been raised for respiratory tract and gastrointestinal infections with vedolizumab, 4 secondary outcomes were previously specified infection sites: (1) (upper and lower) respiratory tract infections; (2) gastrointestinal infections; and (3) non-respiratory, non-gastrointestinal infections (skin, urinary tract, musculoskeletal infections, sepsis, non-classified opportunistic and mycobacterial infections). Infection related ICD codes are summarized in Supplementary Table 1.

Covariates

A broad range of pre-exposure patient characteristics and markers of IBD disease severity were considered, including demographics, comorbidities (previous history of serious infections, Clostridioides difficile infection, ischemic heart disease, chronic heart failure, atrial fibrillation, peripheral vascular disease, cerebrovascular disease, chronic kidney failure, chronic liver disease, venous thromboembolism, chronic pulmonary disease, diabetes, dyslipidemia, hypertension, obesity), smoking and alcohol abuse. Comorbidities were assessed in the 180 days before cohort entry in MarketScan and Optum and in all data available in the SNDS. In the three databases, IBD disease severity and healthcare use were assessed in the 180 days before cohort entry. IBD disease severity was assessed by anti-TNF, thiopurines, methotrexate, aminosalicylates, and corticosteroids exposure, occurrence of IBD-related hospitalization or surgery, abdominal imaging, gastrointestinal endoscopy, fecal pathogen, and the number of C-reactive protein tests. Additionally, the number of previous anti-TNF agents was assessed within all data available since first identification in the database. Healthcare use was assessed by hospitalizations not related to IBD, gastroenterologist and primary care provider visits, flu and pneumococcus vaccines.

Statistical analyses

We tabulated baseline characteristics of vedolizumab and anti-TNF users, before and after propensity score (PS)-matching to adjust for baseline covariates, within each cohort.

Multivariable logistic regression models estimated patients' PS values, that is, the predicted probability of vedolizumab versus anti-TNF conditioning on all the confounders listed above.

The PS was estimated using logistic regression including all patient characteristics without further variable selection. We used PS-matching with a variable ratio up to 1:4 comparing vedolizumab and anti-TNF with a matching 'caliper' of 0.02 on the PS scale. After matching, weighted standardized differences adapted to incomplete many-to-one matching were calculated to assess balance between vedolizumab-exposed and their matched anti-TNF-exposed controls. [START_REF] Austin | Assessing balance in measured baseline covariates when using many-to-one matching on the propensity-score[END_REF] Crude incidence rates of primary and secondary outcomes were calculated. Cox proportional hazards regression models were used to estimate crude and adjusted hazard ratios (HR) comparing the risk of serious infections associated with vedolizumab versus anti-TNF in each cohort separately. Robust variance estimators were used to estimate conservative 95% confidence intervals. In order to take into account the interaction between IBD subtype and treatment exposure, PS and hazard ratios were separately estimated for each IBD subtype in addition to the overall cohort of IBD. For each IBD subtype, cohort-specific HRs were combined by an inverse variance-weighted, fixedeffects model, in anti-TNF naïve and experienced patients, and in the overall cohort.

Additional prespecified analyses included subgroups analyses stratified on age at cohort entry (18-59; ≥ 60), gender, and previous history of serious infections. Subgroup analyses were only performed in the overall cohort due to limited study size. Several sensitivity analyses were performed to test the robustness of our results. First, we excluded gastrointestinal and mycobacterial infections from the list of serious infections. Second, only patients initiating an anti-TNF agent after May 2014 (vedolizumab approval date by the US Food and Drug Administration and European Medicine Agency) were included. Third, we excluded patients with multiple diagnosis codes related to both CD and UC from the definition of CD. Fourth, we performed a PS-matched analysis with a 1:1 fixed-ratio with a caliper of 0.02. Lastly, we assessed the risk of any serious infection within two risk periods, 180 and 365 days. In these two analyses, follow-up time ended on the earliest occurrence of the 180 th or 365 th day of follow-up, outcome occurrence, treatment discontinuation, disenrollment, death or end of study period.

Analyses were performed using SAS (version 9.4) statistical software (SAS Institute) and the validated Aetion Evidence Platform (V 4.2) using R.

Results

Patient characteristics

A total of 96 073 patients with IBD who initiated vedolizumab or an anti-TNF agent were identified across the three databases, among whom 1973 (2.0%) were hospitalized at treatment initiation. After applying exclusion criteria (Figure 1), 86 349 patients were included, initiating either vedolizumab (n=10 421) or an anti-TNF agent (n=75 928). Among patients with UC, 4987 (19.3%) were treated with vedolizumab, while 5434 (9.0%) patients with CD were treated with vedolizumab (Supplementary Tables 2 and3). Overall, 7480 (71.8%) and 27 345 (36.0%) patients were anti-TNF experienced in the vedolizumab and anti-TNF groups, respectively. In the SNDS cohort, the mean (SD) duration of IBD was 8.42 (6.10) and 7.67 (6.43) years in the vedolizumab and anti-TNF groups, respectively.

In the matched population of 35 424 patients, 26 656 anti-TNF and 8 768 vedolizumab users were included. All baseline characteristics between the two groups were balanced, with a maximum absolute standardized difference of 4% in all covariates (Table 1 andTable 2, Supplementary Figure 1). Density plots after propensity score matching are provided in Supplementary Figure 2.

Risk of serious infections

A total of 169 serious infections occurred in the vedolizumab group (6493 person-years) and 724 in the anti-TNF group (31 232 person-years) during a mean (SD) follow-up of 1.1 (1.2) years. Incidence rates of serious infections per 1000 person-years was 26.0 (95%CI, 22.4-30.3) and 23.2 (95%CI, 21.6-24.9) in vedolizumab and anti-TNF users, respectively (Table 3). Cohorts-specific incidence rates per 1000 person-years ranged from 17.8 to 38.7 in vedolizumab users and from 18.4 to 27.4 in anti-TNF users (Supplementary Table 4 to 6).

The risk of serious infections was not different between vedolizumab and anti-TNF in the overall IBD cohort (combined HR, 0.95; 95%CI, 0.79-1.13), while the risk was decreased with vedolizumab compared to anti-TNF in patients with UC (combined HR, 0.68; 95%CI, 0.50-0.93), but not CD (combined HR, 1.10; 95%CI, 0.87-1.38) (Figure 3). Crude HRs are provided in Supplementary Figure 3.

In UC, the HR for serious infections associated with vedolizumab versus anti-TNF was 0.57 (95%CI, 0.32-1.01) and 0.74 (95%CI, 0.51-1.07) in anti-TNF naïve and anti-TNF experienced patients, respectively. In CD, the HR for serious infections associated with vedolizumab versus anti-TNF was 1.32 (95%CI, 0.87-2.02) and 1.01 (95%CI, 0.77-1.33) in anti-TNF naïve and anti-TNF experienced patients, respectively. Figure 3 summarizes the HRs for serious infections by infections site. In patients with CD, the combined HR for gastrointestinal infections associated with vedolizumab versus anti-TNF was 1.37 (95%CI, 0.96-1.97), while it was 0.93 (95%CI, 0.58-1.51) for respiratory tract infections, and 1.01 (95%CI 0.70-1.44) for infections excluding respiratory and gastrointestinal infections.

In patients with UC, the combined HR for gastrointestinal infections associated with vedolizumab versus anti-TNF was 0.87 (95%CI, 0.55-1.36), while it was 0.55 (95%CI 0.25-1.20) for respiratory tract infections, and 0.65 (95%CI, 0.39-1.08) for infections excluding respiratory and gastrointestinal infections. Cohort-specific HRs for site-specific infections are provided in Supplementary Table 10.

Subgroup and sensitivity analyses

Overall, there was no statistically significant heterogeneity in the risk of serious infections across subgroups (Supplementary table 7 and Supplementary Figure 4). The combined HR for serious infections associated with vedolizumab versus anti-TNF was 1.10 (95%CI, 0.88-1.38) and 1.00 (95%CI, 0.79-1.28) in men and women, respectively. In patients aged 60 years or older, the combined HR was 1.19 (95%CI, 0.84-1.70). In patients with previous history of serious infections, the combined HR was 0.85 (95%CI, 0.56-1.27).

Findings were consistent after exclusion of gastrointestinal and mycobacterial infections (Supplementary Figure 5). The risk of serious infections was not different between vedolizumab and anti-TNF in patients with CD after excluding patients with both CD and UC diagnosis codes (HR, 1.13; 95%CI, 0.85-1.49). The protective effect associated with vedolizumab was consistent after exclusion of gastrointestinal infections in UC (HR, 0.59; 95%CI, 0.39-0.90). The various sensitivity analyses yielded consistent results (Supplementary Figure 6).

Discussion

Based on three large population-based cohorts in the U.S. and France, our findings suggest that among patients with IBD, the risk of serious infections was not decreased with vedolizumab versus anti-TNF. However, while the risk was not different in patients with CD, vedolizumab was associated with a 32% decreased risk of serious infections compared to anti-TNF in patients with UC. The risk associated with vedolizumab versus anti-TNF tended to be lower in anti-TNF naïve compared to anti-TNF experienced patients with UC, whereas the opposite was observed in patients with CD.

Two studies based on RCTs data including 2830 and 1731 patients assessed the risk of serious infections with vedolizumab. [START_REF] Colombel | The safety of vedolizumab for ulcerative colitis and Crohn's disease[END_REF][START_REF] Feagan | Respiratory Tract Infections in Patients With Inflammatory Bowel Disease: Safety Analyses From Vedolizumab Clinical Trials[END_REF] In these studies, incidence rates of serious infections were similar for vedolizumab and placebo. Whereas we reported a protective effect in patients with UC, no differences were observed in patients with CD. This difference may be related to the impact of disease activity, while IBD disease activity is one of the most consistent risk factor of infections. [START_REF] Lichtenstein | Serious infection and mortality in patients with Crohn's disease: more than 5 years of follow-up in the TREAT TM registry[END_REF] Hence, effective disease control with any agent compared to a less effective treatment would decrease the risk of infection, independently of the specific risk related to the potential treatment-related immunosuppression. In UC, a network meta-analysis reported that vedolizumab was as, or more effective for induction of remission than anti-TNF, regardless of previous treatment history with anti-TNF. [START_REF] Singh | Systematic review with network meta-analysis: first-and second-line pharmacotherapy for moderate-severe ulcerative colitis[END_REF] Recently, one head to head trial comparing vedolizumab with adalimumab in patients with UC reported that vedolizumab was superior to adalimumab for achieving clinical remission, but not corticosteroid-free clinical remission. [START_REF] Sands | Vedolizumab versus Adalimumab for Moderate-to-Severe Ulcerative Colitis[END_REF] In CD, a network meta-analysis reported that infliximab or adalimumab were more effective than vedolizumab for inducing or maintaining clinical remission in anti-TNF naïve or experienced patients. [START_REF] Singh | Systematic review and network meta-analysis: first-and second-line biologic therapies for moderate-severe Crohn's disease[END_REF] The safety profile of IBD treatments regarding serious infections was considered to be similar in CD and UC, our findings suggest that this concept should be reconsidered for vedolizumab.

Similarly, the impact of IBD disease activity may explain the observed risk differences according to previous anti-TNF exposure. Similar findings were observed in the study based on RCTs. [START_REF] Colombel | The safety of vedolizumab for ulcerative colitis and Crohn's disease[END_REF] Alongside opioids and corticosteroids use, previous exposure to anti-TNF was associated with an increased risk of serious infections in UC (HR, 1.99; 95%CI, 1.16-3.42), whereas it tends to decrease the risk in CD (HR, 0.82; 95%CI, 0.57-1.19). We also reported a trend for differences in the risk of gastrointestinal and non-gastrointestinal infections. It may highlight the fact that the gut-selectivity of vedolizumab may play a major role in the risk of infections. Further studies are required to specifically assess the risk of gastrointestinal infections with vedolizumab.

Our study has several strengths. The main strength is the generalizability and large size as we used two large U.S. and one French nationwide population-based cohorts, which was necessary to achieve an adequate study size since vedolizumab is a relatively new therapy for IBD. It is the first observational study that evaluates the risk of serious infections with vedolizumab versus anti-TNF in a population-based nationwide cohort, and analyses were adjusted for a variety of confounding factors including IBD disease severity. Aside from reducing the likelihood of confounding, the use of anti-TNF as active comparator also addressed the clinically relevant question of which treatment is preferred regarding the risk of infections for patients with IBD requiring biologics. Lastly, we conducted stratified analyses by IBD subtype, previous exposure to anti-TNF, and infection sites. These findings were consistent across several sensitivity analyses. Some limitations should be noted. First, we used diagnosis codes recorded in administrative databases to identify IBD patients, which may introduce misclassification. However, treatment with vedolizumab or anti-TNF was an additional requirement for inclusion in our cohort and combining diagnosis codes with IBD specific treatment dispensing is shown to have a positive predicted value of 90% in identifying cases in a previous validation study. [START_REF] Liu | Validity of computerized diagnoses, procedures, and drugs for inflammatory bowel disease in a northern California managed care organization[END_REF] Although identification of infection was based on discharge diagnosis, the validity of these outcomes was assessed in the SNDS, with more than 95% accuracy of recorded cases and infection types. [START_REF] Sahli | Positive predictive values of selected hospital discharge diagnoses to identify infections responsible for hospitalization in the French national hospital database[END_REF] In addition, incidence rates of serious infections in patients exposed to anti-TNF reported in the three databases are similar to those reported in registries of patients with IBD treated with anti-TNF. [START_REF] Lichtenstein | Serious infection and mortality in patients with Crohn's disease: more than 5 years of follow-up in the TREAT TM registry[END_REF] The large sample size allowed us to assess the risk to the main infections sites, although further studies are required to assess the specific risks of opportunistic infections associated with vedolizumab. While patients are followed from birth to emigration or death in the SNDS database, patients could have been censored due to health insurance enrollment changes in Optum and MarketScan. It may explain the relatively short mean follow-up time of one year. The U.S databases only included commercially insured patients, while the SNDS database included all French residents, since universal healthcare is guaranteed for all French residents. However, these limitations did not apply in the SNDS database, and results were consistent across databases.

Additionally, serious infections mainly occurred in the first year after treatment introduction. [START_REF] Kirchgesner | Risk of Serious and Opportunistic Infections Associated With Treatment of Inflammatory Bowel Diseases[END_REF] Due to differences in health insurance enrollment, comorbidities were assessed in the 180 days prior to cohort entry in Optum and MarketScan, while all data available were considered in the SNDS database. However, IBD disease severity prior cohort entry and rates of comorbidities were mainly similar across the three databases. Endoscopic and histological data were not available, and residual confounding by these factors cannot be entirely ruled out. IBD disease activity was based on hospitalization, surgery, and corticosteroids exposure.

Although this definition may have excluded some mild cases of active disease, this is unlikely to have biased our results since only severe disease activity increases the risk of serious infections. [START_REF] Lichtenstein | Serious infection and mortality in patients with Crohn's disease: more than 5 years of follow-up in the TREAT TM registry[END_REF] Patients who started anti-TNF or vedolizumab during an hospitalization were excluded, since co-medication initiated during hospitalization are not accurately assessed in the datasets. While excluding such patients may affect the generalizability of the findings, patients excluded due to inpatient treatment initiation only accounted for 2.0% of the total cohort. The vast majority of patients included were previously exposed to only one anti-TNF agent, so we were not able to perform subgroup analyses according to the number of previous anti-TNF agents prescribed. Further studies are required to address this question. Although overlapping at the individual health plan level is expected to be minimal across different health plans contributing information to the 2 databases, it is possible that some patients have duplicate person-time in Optum and MarketScan. The exact proportion of those potential duplicates is unknown; however, in a previously published study of two large commercial insurance databases was estimated to be small, [START_REF] Broder | Treatments, complications, and healthcare utilization associated with acromegaly: a study in two large United States databases[END_REF] suggesting potentially minimal impact on the CIs but not on the point estimates.

Conclusion

Based on three large population-based cohorts of IBD patients in both US and France including more than 10 000 patients exposed to vedolizumab, the risk of serious infections associated with vedolizumab was not lower versus anti-TNF in the overall group of patients with IBD.

However, vedolizumab was associated with a decreased risk of serious infections compared with anti-TNF among patients with UC, but not CD. These findings may help to clarify the optimal position of vedolizumab in the therapeutic management of IBD.

Supplementary table 1. All diagnoses of infections with related ICD-10 and -9 codes included as any serious infection with the subdivision into site-specific groups

(

  MarketScan) 2002-2017 and Optum Clinformatics (Optum) 2004-2019, and the French administrative health database 2006-2018 (Système National des Données de Santé [SNDS]). Patients enrolled in the MarketScan and Optum databases are representative of a commercially insured population in the U.S.; the SNDS covers 95% of the French population.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Study Population Flowchart

Figure 1 Figure 2 .Figure 3 .

 123 Figure 1. Study Population Flowchart

Page 12 .

 12 Supplementary Figure 1. Standardized differences before and after propensity score matching in anti-TNF experienced patients (MarketScan [A], Optum[B], and SNDS [C]) and anti-TNF naïve patients (MarketScan [C], Optum[D]) Page 13. Supplementary Figure 2. Density plots after propensity score matching in anti-TNF experienced patients (MarketScan [A], Optum[B], and SNDS [C]) and anti-TNF naïve patients (MarketScan [C], Optum[D]) Page 14. Supplementary Figure 3. Combined hazard ratios for serious infections associated with vedolizumab compared to anti-TNF according to IBD subtype and previous exposure to anti-TNF: crude analysis Page 15. Supplementary Figure 4. Combined hazard ratios for serious infections associated with vedolizumab compared to anti-TNF among patient subgroups based on sex, age, and previous history of serious infections Page 16. Supplementary Figure 5. Combined hazard ratios for serious infections associated with vedolizumab compared to anti-TNF excluding gastrointestinal infections (A) and mycobacterial infections (B), according to IBD subtype and previous exposure to anti-TNF Page 17. Supplementary Figure 6. Sensitivity Analysis: combined hazard ratios for serious infections associated with vedolizumab compared to anti-TNF according to IBD subtype and previous exposure to anti-TNF. (A) Inclusion of anti-TNF users from May 2014; (B) Propensity score matching with a 1:1 fixed ratio; (C) 180 days risk period analysis; (D) 365 days risk period analysis.

Table Legends :

 Legends Table 1. Characteristics of study patients, propensity score-matched with a 1:4 variable ratio

Table 2 .

 2 Characteristics of anti-TNF naïve patients, propensity score-matched with a 1:4 variable ratio

Table 3 .

 3 Incidence of serious infections according to treatment exposure during follow-up after propensity score-matching with a 1:4 variable ratio

Table 1 . Characteristics of study patients, propensity score-matched with a 1:4 variable ratio Anti-TNF experienced, No. (%)

 1

	MarketScan	Optum	SNDS

Table 1 . Characteristics of study patients, propensity score-matched with a 1:4 variable ratio (continued) Anti-TNF experienced, No. (%)

 1

	MarketScan	MarketScan

a Assessed in all data available. b Assessed within 180 days before cohort entry

Table 2 . Characteristics of anti-TNF naïve patients, propensity score-matched with a 1:4 variable ratio Anti-TNF naïve, No. (%)

 2

	MarketScan	Optum

Table 2 . Characteristics of anti-TNF naïve patients, propensity score-matched with a 1:4 variable ratio

 2

	(continued)
			Anti-TNF naïve, No. (%)
		MarketScan	Optum
	Variable	Anti-TNF (n=5210)	Vedolizumab (n=1304)	Anti-TNF (n=6046)	Vedolizumab (n=1630)
	Comorbidities
	Venous thromboembolism	101 (1.9)	28 (2.1)	179 (3.0)	57 (3.5)
	Chronic pulmonary disease	677 (13.0)	176 (13.5)	949 (15.7)	273 (16.7)
	Clostridioides difficile infection	227 (4.4)	60 (4.6)	207 (3.4)	64 (3.9)
	Diabetes	419 (8.0)	106 (8.1)	632 (10.5)	187 (11.5)
	Dyslipidemia	1022 (19.6) 263 (20.2) 1598 (26.4) 464 (28.5)
	Hypertension	1352 (26.0) 334 (25.6) 1901 (31.4) 550 (33.7)
	Obesity	360 (6.9)	90 (6.9)	494 (8.2)	140 (8.6)
	Smoking	284 (5.5)	79 (6.1)	435 (7.2)	119 (7.3)
	Alcohol abuse	22 (0.4)	6 (0.5)	53 (0.9)	13 (0.8)
	Healthcare use characteristics b
	Hospitalizations not related to IBD	292 (5.6)	79 (6.1)	553 (9.1)	163 (10.0)
	Gastroenterologist visits, mean (SD)	3.96 (4.86) 4.02 (4.89) 5.64 (6.30) 5.69 (6.45)
	Primary care provider visits, mean (SD)	2.69 (6.08) 2.68 (7.48) 4.55 (11.1) 4.98 (11.1)
	Flu vaccine	1087 (20.9) 266 (20.4)	937 (15.5)	256 (15.7)
	Pneumococcus vaccine	1055 (20.2) 259 (19.9)	486 (8.0)	141 (8.7)

a Assessed in all data available. b Assessed within 180 days before cohort entry

Table 3 . Incidence of serious infections according to treatment exposure during follow-up after propensity score- matching with a 1:4 variable ratio

 3

				Anti-TNF				Vedolizumab
		N	Person -years	Cases	Incidence rates (95% CI) a	N	Person -years	Cases	Incidence rates (95% CI) a
	Inflammatory bowel disease
	Anti-TNF experienced	15400 21116	469	22.2 (20.3-24.3) 5834	4841	127	26.2 (22.0-31.2)
	Anti-TNF naïve	11256 10116	255	25.2 (22.3-28.5) 2934	1652	42	25.4 (18.8-34.4)
	Combined	26656 31232	724	23.2 (21.6-24.9) 8768	6493	169	26.0 (22.4-30.3)
	Crohn's disease
	Anti-TNF experienced	10100 13616	316	23.2 (20.8-25.9) 3917	2460	70	28.5 (22.5-36.0)
	Anti-TNF naïve	5832	5546	123	22.2 (18.6-26.5) 1517	803	28	34.9 (24.1-50.5)
	Combined	15932 19162	439	22.9 (20.9-25.2) 5434	3263	98	30.0 (24.6-36.6)
	Ulcerative colitis
	Anti-TNF experienced	5119	7155	132	18.4 (15.6-21.9) 2606	2279	41	18.0 (13.2-24.4)
	Anti-TNF naïve	5312	4643	109	23.5 (19.5-28.3) 1422	846	14	16.5 (9.80-27.9)
	Combined	10431 11798	241	20.4 (18.0-23.2) 4028	3125	55	17.6 (13.5-22.9)

  Supplementary table 1. All diagnoses of infections with related ICD-10 and -9 codes included as any serious infection with the subdivision into site-specific groups Page 4-5. Supplementary table 2. Characteristics of anti-TNF experienced patients, before propensity score-matching Page 6-7. Supplementary table 3. Characteristics of anti-TNF naïve patients, before propensity score-matching Page 8. Supplementary table 4. Incidence of serious infections according to treatment exposure in the overall IBD cohort after propensity score-matching with a 1:4 variable ratio Page 9. Supplementary table 5. Incidence of serious infections according to treatment exposure in patients with Crohn's disease after propensity score-matching with a 1:4 variable ratio Page 10. Supplementary table 6. Incidence of serious infections according to treatment exposure in patients with ulcerative colitis after propensity score-matching with a 1:4 variable ratio Page 11. Supplementary table 7. Database specific hazard ratio of serious infections in patients treated with vedolizumab versus anti-TNF among patient subgroups based on sex, age, and previous history of serious infections

table 2 . Characteristics of anti-TNF experienced patients, before propensity score-matching Anti-TNF experienced

 2

	Subgroup of infection	Infections sites	Diagnoses	ICD-10	ICD-9
					052.1; 055.2; 480; 481; 482.0; 482.1; 482.2;
			Pneumonia	A48.1; B01.2; B05.2; B25.0; J10-J18	482.3; 482.4; 482.81; 482.82; 482.83; 482.84; 482.89; 482.9; 483.0; 483.1; 483.8;
					484.1; 484.7; 484.8; 485; 486; 487; 488
		Lower respiratory tract infections	Other acute lower respiratory infections	A37; A42.0; B39-B40; B44; B48.5; B58.3; B59; B95.3; J20-J22; U04	033; 039.1; 041.2; 115; 116.0; 117.3; 130.4; 466; 484.3; 484.6
			Abscessus pulmonis	J85	513
			Empyema pleurae	J86	510
			Mastoiditis	H70	383.0
			Nasopharyngitis	A36.1	032.1; 032.2
	Respiratory tract
	infections		Sinusitis	J01	461
			Pharyngitis	J02	034.0
			Pharyngeal, retropharyngeal
		tract infections Upper respiratory	parapharyngeal abscess and	J36;J39.0-J39.1	032.1; 475; 478.21; 478.22; 478.24; 478.71
			Tonsillitis	A36.0; J03	032.0; 463
			Laryngitis and tracheitis	A36.2; J04-J05	032.3; 464; 465; 572.0
			Acute upper respiratory
			infections	A36.8-A36.9; J06	032.81; 032.82; 032.83; 032.84; 0.32.9; 465
			of multiple and unspecified sites
			Infection of ear and acute otitis media	H60.0-H60.3; H65.1; H66; H68.0	380.1; 382.0
			Intestinal infectious disease	A00-A08	001-009; 040.4
					070.0; 070.1; 070.20; 070.21; 070.30;
			Viral hepatitis	B15; B17; B25.1	070.31; 070.41-070.43; 070.51; 070.52;
	GI infections	GI infections			070.53
			Cholangitis	K80-K810; K830, K87.00; B25.8	574.0; 574.3; 574.6; 575.0; 576.1; 576.1
			Liver abscess	K750	572.0
			Infectious esophagitis	B00.8; K23.80	054.6; 054.7
			Erysipelas	A46	035
			Dermatophytosis and other
			superficial	B35-B36	110-111
			mycoses
		Skin and subcutaneous tissue	Cellulitis and abscess	L02-L03	680-682
		infections	Herpes virus	B00-B02; B05-B06; B08-B09; A60;	052.2; 052.8; 052.9; 053.2; 053.7; 053.8; 053.9; 054.1; 054.2; 054.5; 054.8; 054.9
			Other local infections of skin, oral tissue and subcutaneous tissue	A36.3; K11.3-K12.2; L00-L01;L04-L05; L08; L30.3; M72.6	032.85; 527.3; 528.3; 683; 684; 685; 686; 695.81; 728.86
	Other infections		Nephritis Acute prostatitis and prostate abscess	N10 N41.0; N41.2; N41.3	590 601.0-601.3
			Cystitis	N30.0	595.0
			Salpingitis and oophoritis	N70.0	614.0
		Urinary tract infections	Syphilis	A50-A53; I98.0	090-097
			Gonorrhea	A54	098
			Chlamydia	A55-A56	099.1
			Orchitis and epididymitis	N45	604
			Other urinary tract infections	N39.0; N73.3; N77.1	599.0; 614.3; 614.5

table 2 . Characteristics of anti-TNF experienced patients, before propensity score-matching (continued) Anti-TNF experienced

 2

	.26

table 3 . Characteristics of anti-TNF naïve patients, before propensity score- matching Anti-TNF naïve MarketScan Optum

 3

	Variable	Anti-TNF (n=34 601)	Vedolizumab (n=1305)	Std. Diff.	Anti-TNF (n=13 982)	Vedolizumab (n=1636)	Std. Diff.
	Age, mean (SD), year	40.1 (14.7)	42.7 (14.7)	0.17	41.7 (15.8)	48.3 (18.1)	0.40
	Sex, n (%)
	Male	16 801 (48.6)	646 (49.5)	-0.02	6996 (50.0)	(48.7)	0.03
	Female	17 800 (51.4)	659 (50.5)	-0.02	6986 (50.0)	(51.3)	0.03
	IBD diagnosis, n (%)
	Crohn's disease	24 252 (70.1)	694 (53.2)	-0.37	9555 (68.3)	(50.3)	-0.38
	Ulcerative colitis	10 349 (29.9)	611 (46.8)	-0.37	4427 (31.7)	(49.7)	-0.38
	Thiopurines, n (%)
	within 180-31 days before cohort entry	8440 (24.4)	257 (19.7)	-0.11	3372 (24.1)	(15.9)	-0.19
	within 30 days before cohort entry	8038 (23.2)	223 (17.1)	-0.15	3345 (23.9)	(13.3)	-0.25
	Methotrexate, n (%)
	within 180-31 days before cohort entry	509 (1.5)	43 (3.3)	0.15	163 (1.2)	39 (2.4)	0.11
	within 30 days before cohort entry	632 (1.8)	43 (3.3)	0.11	231 (1.7)	41 (2.5)	0.07
	Corticosteroids (oral), n (%)
	within 180-31 days before cohort entry	18 871 (54.5)	731 (56.0)	0.03	7963 (57.0)	(54.5)	-0.05
	within 30 days before cohort entry	17482 (50.5)	611 (46.8)	-0.07	7363 (52.7)	(46.1)	-0.13
	Aminosalicylates (oral), n (%) a	16 616 (48.0)	547 (41.9)	-0.12	6901 (49.4)	(44.9)	-0.09
	Antibiotics, n (%) a	16 627 (48.1)	596 (45.7)	-0.05	7194 (51.5)	(42.4)	-0.18
	Opioids, n (%) a	14 299 (41.3)	473 (36.2)	-0.10	5071 (36.3)	(25.4)	-0.23
	IBD disease activity assessment a
	Hospitalization related to IBD, n (%)	6963 (20.1)	164 (12.6)	-0.19	2851 (20.4)	(12.3)	-0.20
	Surgery related to IBD, n (%)	1121 (3.2)	27 (2.1)	-0.07	769 (5.5)	41 (2.5)	-0.14
	Abdominal imaging, n (%)	11 419 (33.0)	351 (26.9)	-0.13	6280 (44.9)	(36.9)	-0.16
	Lower GI endoscopy, n (%)	19 966 (57.7)	680 (52.1)	-0.11	8510 (60.9)	(58.6)	-0.05
	Upper GI endoscopy, n (%)	5430 (15.7)	144 (11.0)	-0.13	2359 (16.9)	(13.9)	-0.08
	No. of CRP tests ordered, mean (SD)	0.79 (1.16)	1.05 (1.63)	0.22	1.26 (1.85)	1.46 (1.93)	0.11
	Fecal pathogen tests ordered, n (%)	8437 (24.4)	339 (26.0)	0.04	3963 (28.3)	(33.1)	0.10
	Serious infection, n (%)
	More than 180 days before cohort entry	1017 (2.9)	62 (4.8)	0.11	394 (2.8)	77 (4.7)	0.11
	within 180 days before cohort entry	609 (1.8)	19 (1.5)	-0.02	245 (1.8)	36 (2.2)	0.03
	Comorbidities, n (%)
	Ischemic heart disease	924 (2.7)	46 (3.5)	0.05	530 (3.8)	94 (5.7)	0.10
	Chronic heart failure	836 (2.4)	45 (3.4)	0.07	466 (3.3)	77 (4.7)	0.07
	Atrial fibrillation	445 (1.3)	21 (1.6)	0.03	240 (1.7)	59 (3.6)	0.14
	Peripheral vascular disease	506 (1.5)	24 (1.8)	0.03	282 (2.0)	63 (3.9)	0.12
	Cerebrovascular disease	262 (0.8)	19 (1.5)	0.08	174 (1.2)	43 (2.6)	0.12
	Chronic kidney disease	510 (1.5)	40 (3.1)	0.13	353 (2.5)	86 (5.3)	0.17

table 3 . Characteristics of anti-TNF naïve patients, before propensity score- matching (continued) Anti-TNF naïve MarketScan Optum

 3

	Variable	Anti-TNF (n=34 601)	Vedolizumab (n=1305)	Std. Diff.	Anti-TNF (n=13 982)	Vedolizumab (n=1636)	Std. Diff.
	Comorbidities, n (%)
	Chronic liver disease	911 (2.6)	27 (2.1)	-0.04	804 (5.8)	146 (8.9)	0.13
	Venous thromboembolism	822 (2.4)	28 (2.1)	-0.02	372 (2.7)	57 (3.5)	0.05
	Chronic pulmonary disease	4095 (11.8)	176 (13.5)	0.05	1886 (13.5)	274 (16.7)	0.09
	Clostridioides difficile infection	1020 (2.9)	60 (4.6)	0.10	485 (3.5)	67 (4.1)	0.03
	Diabetes	2302 (6.7)	106 (8.1)	0.06	1149 (8.2)	190 (11.6)	0.12
	Dyslipidemia	6266 (18.1)	263 (20.2)	0.05	2859 (20.4)	467 (28.5)	0.20
	Hypertension	7732 (22.3)	334 (25.6)	0.08	3505 (25.1)	553 (33.8)	0.20
	Obesity	1609 (4.7)	90 (6.9)	0.11	1010 (7.2)	140 (8.6)	0.05
	Smoking	2512 (7.3)	79 (6.1)	-0.05	1569 (11.2)	119 (7.3)	-0.13
	Alcohol abuse	231 (0.7)	6 (0.5)	-0.03	168 (1.2)	13 (0.8)	-0.04
	Healthcare use characteristics a
	Hospitalization not related to IBD, n (%)	3058 (8.8)	79 (6.1)	-0.10	1281 (9.2)	164 (10.0)	0.03
	No. of gastroenterologist visits, mean (SD)	4.11 (4.94)	4.02 (4.89)	-0.02	6.05 (6.63)	5.68 (6.45)	-0.06
	No. of primary care provider visits, mean (SD)	2.77 (6.92)	2.68 (7.48)	-0.01	4.54 (10.2)	4.97 (11.1)	0.04
	Flu vaccine, n (%)	4879 (14.1)	266 (20.4)	0.18	2097 (15.0)	256 (15.6)	0.02
	Pneumococcus vaccine, n (%)	3342 (9.7)	259 (19.8)	0.34	1098 (7.9)	141 (8.6)	0.03

Abbreviations: Std. Diff., absolute standardized differences. SD: standard deviation. a Assessed within 180 days before cohort entry Supplementary

table 4 . Incidence of serious infections according to treatment exposure in the overall IBD cohort after propensity score-matching with a 1:4 variable ratio

 4

					Overall cohort
				Anti-TNF				Vedolizumab
		N	Person-years	Cases	Incidence rates (95% CI) a	N	Person-years	Cases	Incidence rates (95% CI) a
	Serious infections, overall
	Anti-TNF experienced
	MarketScan	6627	6836	187	27.4 (23.7-31.6)	2133	1265	49	38.7 (29.3-51.3)
	Optum	3673	4086	94	23.0 (18.8-28.2)	1609	1022	23	22.5 (15.0-33.9)
	SNDS	5100	10194	188	18.4 (16.0-21.3)	2092	2554	55	21.5 (16.5-28.0)
	Combined	15400	21116	469	22.2 (20.3-24.3)	5834	4841	127	26.2 (22.0-31.2)
	Anti-TNF naïve
	MarketScan	5210	4718	113	24.0 (19.9-28.8)	1304	729	13	17.8 (10.4-30.7)
	Optum	6046	5398	142	26.3 (22.3-31.0)	1630	923	29	31.4 (21.8-45.2)
	Combined	11256	10116	255	25.2 (22.3-28.5)	2934	1652	42	25.4 (18.8-34.4)
	Combined	26656	31232	724	23.2 (21.6-24.9)	8768	6493	169	26.0 (22.4-30.3)
	Respiratory tract infections
	Anti-TNF experienced
	MarketScan	6627	6945	30	4.32 (3.02-6.18)	2133	1282	8	6.24 (3.12-12.5)
	Optum	3673	4126	22	5.33 (3.51-8.10)	1609	1033	5	4.84 (2.01-11.6)
	SNDS	5100	10355	53	5.12 (3.91-6.70)	2092	2587	14	5.41 (3.20-9.14)
	Combined	15400	21426	105	4.90 (4.05-5.93)	5834	4902	27	5.51 (3.78-8.03)
	Anti-TNF naïve
	MarketScan	5210	4784	21	4.39 (2.86-6.73)	1304	729	3	4.12 (1.33-12.8)
	Optum	6046	5451	36	6.60 (4.76-9.16)	1630	927	7	7.55 (3.60-15.8)
	Combined	11256	10235	57	5.57 (4.30-7.22)	2934	1656	10	6.04 (3.25-11.2)
	Combined	26656	31661	162	5.12 (4.39-5.97)	8768	6558	37	5.64 (4.09-7.79)
	GI infections
	Anti-TNF experienced
	MarketScan	6627	6897	77	11.2 (8.93-14.0)	2133	1274	25	19.6 (13.3-29.0)
	Optum	3673	4112	47	11.4 (8.59-15.2)	1609	1026	14	13.6 (8.08-23.0)
	SNDS	5100	10363	39	3.76 (2.75-5.15)	2092	2579	20	7.76 (5.00-12.0)
	Combined	15400	21372	163	7.63 (6.54-8.89)	5834	4879	59	12.1 (9.37-15.6)
	Anti-TNF naïve
	MarketScan	5210	4766	42	8.81 (6.51-11.9)	1304	729	7	9.60 (4.58-20.1)
	Optum	6046	5435	50	9.20 (6.97-12.1)	1630	925	11	11.9 (6.59-21.5)
	Combined	11256	10201	92	9.02 (7.35-11.1)	2934	1654	18	10.9 (6.86-17.3)
	Combined	26656	31573	255	8.08 (7.14-9.13)	8768	6533	77	11.8 (9.43-14.7)
	Other infections
	Anti-TNF experienced
	MarketScan	6627	6918	84	12.1 (9.80-15.0)	2133	1278	17	13.3 (8.27-21.4)
	Optum	3673	4127	27	6.54 (4.49-9.54)	1609	1030	5	4.85 (2.02-11.7)
	SNDS	5100	10301	107	10.4 (8.59-12.6)	2092	2584	23	8.90 (5.91-13.4)
	Combined	15400	21346	218	10.2 (8.94-11.7)	5834	4892	45	9.20 (6.87-12.3)
	Anti-TNF naïve
	MarketScan	5210	4753	50	10.5 (7.97-13.9)	1304	729	4	5.49 (2.06-14.6)
	Optum	6046	5441	62	11.4 (8.88-14.6)	1630	928	12	12.9 (7.34-22.8)
	Combined	11256	10194	112	11.0 (9.13-13.2)	2934	1657	16	9.66 (5.92-15.8)
	Combined	26656	31540	330	10.5 (9.39-11.7)	8768	6549	61	9.31 (7.25-12.0)

a Incidence rate (IR) is per 1000 person-years.

Supplementary

table 5 . Incidence of serious infections according to treatment exposure in patients with Crohn's disease after propensity score-matching with a 1:4 variable ratio

 5

					Crohn's disease
				Anti-TNF			Vedolizumab
		N	Person-years	Cases	Incidence rates (95% CI) a	N	Person-years	Cases	Incidence rates (95% CI) a
	Serious infections, overall
	Anti-TNF experienced
	MarketScan	4615	4881	124	25.4 (21.3-30.3) 1306	781	36	46.1 (33.2-63.9)
	Optum	2754	3194	76	23.8 (19.0-29.8)	954	576	13	22.6 (13.1-38.9)
	SNDS	2731	5541	116	20.9 (17.5-25.1) 1657	1103	21	19.0 (12.4-29.2)
	Combined	10100	13616	316	23.2 (20.8-25.9) 3917	2460	70	28.5 (22.5-36.0)
	Anti-TNF naïve
	MarketScan	2761	2666	45	16.9 (12.6-22.6)	694	373	10	26.8 (14.4-49.8)
	Optum	3071	2880	78	27.1 (21.7-33.8)	823	430	18	41.9 (26.4-66.4)
	Combined	5832	5546	123	22.2 (18.6-26.5) 1517	803	28	34.9 (24.1-50.5)
	Combined	15932	19162	439	22.9 (20.9-25.2) 5434	3263	98	30.0 (24.6-36.6)
	Respiratory tract infections
	Anti-TNF experienced
	MarketScan	4615	4948	22	4.45 (2.93-6.75) 1300	795	5	6.29 (2.62-15.1)
	Optum	2754	3225	20	6.20 (4.00-9.61)	927	581	3	5.16 (1.67-16.0)
	SNDS	2731	5662	34	6.00 (4.29-8.40)	893	1114	6	5.39 (2.42-12.0)
	Combined	10100	13835	76	5.49 (4.39-6.88) 3120	2490	14	5.62 (3.33-9.49)
	Anti-TNF naïve
	MarketScan	2761	2684	12	4.47 (2.54-7.87)	692	374	2	5.35 (1.34-21.4)
	Optum	3071	2918	24	8.22 (5.51-12.3)	819	433	6	13.9 (6.23-30.8)
	Combined	5832	5602	36	6.43 (4.64-8.91) 1511	807	8	9.91 (4.96-19.8)
	Combined	15932	19437	112	5.76 (4.79-6.93) 4631	3297	22	6.67 (4.39-10.1)
	GI infections
	Anti-TNF experienced
	MarketScan	4615	4922	49	9.96 (7.52-13.2) 1300	788	17	21.6 (13.4-34.7)
	Optum	2754	3220	35	10.9 (7.80-15.1)	927	577	8	13.9 (6.93-27.7)
	SNDS	2731	5659	21	3.71 (2.42-5.69)	893	1114	6	5.39 (2.42-12.0)
	Combined	10100	13801	105	7.61 (6.28-9.21) 3120	2479	31	12.5 (8.80-17.8)
	Anti-TNF naïve
	MarketScan	2761	2678	14	5.23 (3.10-8.83)	692	374	5	13.4 (5.56-32.1)
	Optum	3071	2928	22	7.51 (4.95-11.4)	819	432	5	11.6 (4.82-27.8)
	Combined	5832	5606	36	6.42 (4.63-8.90) 1511	806	10	12.4 (6.68-23.1)
	Combined	15932	19407	141	7.27 (6.16-8.57) 4631	3285	41	12.5 (9.19-17.0)
	Other infections
	Anti-TNF experienced
	MarketScan	4615	4932	56	11.4 (8.74-14.8) 1300	790	15	19.0 (11.4-31.5)
	Optum	2754	3232	22	6.81 (4.48-10.3)	927	581	3	5.16 (1.67-16.0)
	SNDS	2731	5602	70	12.5 (9.89-15.8)	893	1111	9	8.10 (4.22-15.6)
	Combined	10100	13766	148	10.8 (9.15-12.6) 3120	2482	27	10.9 (7.46-15.9)
	Anti-TNF naïve
	MarketScan	2761	2681	19	7.09 (4.52-11.1)	692	374	3	8.02 (2.59-24.9)
	Optum	3071	2907	35	12.0 (8.64-16.8)	819	434	8	18.4 (9.22-36.9)
	Combined	5832	5588	54	9.66 (7.40-12.6) 1511	808	11	13.6 (7.54-24.6)
	Combined	15932	19354	202	10.4 (9.09-12.0) 4631	3290	38	11.6 (8.40-15.9)

a Incidence rate (IR) is per 1000 person-years.

a Incidence rate (IR) is per 1000 person-years.

Julien Kirchgesner received research support from the French National Society of Gastroenterology (SNFGE). Rishi J. Desai has received research support to the Brigham and Women's Hospital from Novartis, Vertex, and Bayer for unrelated studies. Laurent Beaugerie has received Sebastian Schneeweiss is participating in investigator-initiated grants to the Brigham and Women's Hospital from Bayer, Vertex, and Boehringer Ingelheim unrelated to the topic of this study. He is a consultant to Aetion Inc., a software manufacturer of which he owns equity. Seoyoung C. Kim has received research support to the Brigham and Women's Hospital from Pfizer, Roche, AbbVie, and Bristol-Myers Squibb for unrelated studies. The investigators conducted the research independently through funding from internal sources of the Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School. Julien Kirchgesner received research support from the French National Society of Gastroenterology (SNFGE).

Supplementary Material

Supplementary table 1. All diagnoses of infections with related ICD-10 and -9 codes included as any serious infection with the subdivision into site-specific groups (continued)