Brief Report: Risk of Gastrointestinal Perforation Among Rheumatoid Arthritis Patients Receiving Tofacitinib, Tocilizumab, or Other Biologic Treatments
Bibliographic record
Abstract
OBJECTIVE: To evaluate gastrointestinal (GI) perforation in rheumatoid arthritis (RA) patients receiving tofacitinib, tocilizumab, or other biologic agents. METHODS: Using health plan data from 2006 through 2014, RA patients without prior GI perforation were identified. Those in whom treatment with tofacitinib or a biologic agent was being initiated were followed up for incident GI perforation with hospitalization. Crude incidence rates were calculated by exposure. Adjusted Cox proportional hazards models were used to evaluate the association between GI perforation and exposures. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated. RESULTS: A cohort of 167,113 RA patients was analyzed. Among them, 4,755 began treatment with tofacitinib, 11,705 with tocilizumab, 115,047 with a tumor necrosis factor inhibitor (TNFi), 31,214 with abatacept, and 4,392 with rituximab. Compared to TNFi recipients, abatacept recipients were older, tofacitinib and rituximab recipients were younger, and tocilizumab recipients were similar in age. Patients beginning treatment with a non-TNFi agent were more likely to have previously received biologic agents than patients beginning treatment with a TNFi. The incidence of GI perforation per 1,000 patient-years was 0.86 (tofacitinib), 1.55 (tocilizumab), 1.07 (abatacept), 0.73 (rituximab), and 0.83 (TNFi). Most perforations occurred in the lower GI tract: the incidence of lower GI tract perforation per 1,000 patient-years was 0.86 (tofacitinib), 1.26 (tocilizumab), 0.76 (abatacept), 0.48 (rituximab), and 0.46 (TNFi). Lower GI tract perforation risk was significantly elevated with tocilizumab treatment, and numerically elevated with tofacitinib treatment, versus treatment with TNFi. Adjusted HRs were 2.51 (95% CI 1.31-4.80) for tocilizumab and 1.94 (95% CI 0.49-7.65) for tofacitinib. Older age (HR 1.16 per 5 years [95% CI 1.10-1.22]), diverticulitis/other GI conditions (HR 3.25 [95% CI 1.62-6.50]), and prednisone use at >7.5 mg/day (HR 2.29 [95% CI 1.39-3.78]) were associated with lower GI tract perforation. The incidence of upper GI tract perforation was similar among all drug exposures. CONCLUSION: The risk of lower GI tract perforation associated with tocilizumab treatment, and possibly tofacitinib treatment, is elevated compared to that associated with TNF blockade.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".