Comparative Safety of Advanced Therapies in Patients With Ulcerative Colitis: An Administrative Claims-Based Study
Bibliographic record
Abstract
INTRODUCTION: We conducted a retrospective cohort study comparing the safety of advanced therapies in patients with ulcerative colitis (UC). METHODS: Using an administrative claims database (OptumLabs Data Warehouse), we identified patients with UC who initiated treatment with tumor necrosis factor-α (TNF) antagonists, anti-integrin agents, anti-interleukin, Janus kinase inhibitors (JAK), or sphingosine-1 phosphate receptor modulators between 2016 and 2022 and had followed up for at least 1 year before and after treatment initiation. We compared the risk of serious infections, venous thromboembolism, and major adverse cardiovascular events through multinomial propensity score-based inverse probability weighting, with propensity scores estimated through generalized boosted models that accounted for disease characteristics, healthcare utilization, comorbidities, and previous and concomitant medications, and the competing risk of mortality. We calculated cause-specific hazard ratios (HR) and 95% confidence intervals for multiple treatment comparisons. RESULTS: We included 9,430 patients with UC treated with TNF antagonists (n = 4,111), anti-integrins (n = 3,165), anti-ILs (n = 1,342), JAK inhibitors (n = 701), or sphingosine-1 phosphate receptor modulators (n = 111), followed over median 27 months. After adjusting for confounding variables, anti-ILs were associated with a lower risk of serious infections compared with TNF antagonists (HR, 0.66 [95% confidence interval, 0.51-0.87]) and anti-integrins (HR, 0.75 [0.57-0.98]). JAK inhibitors were associated with a lower risk of serious infections compared with TNF antagonists (HR, 0.66 [0.46-0.94]). The incidence of venous thromboembolism (incidence rate, 1.4-2.0 per 100 py) and major adverse cardiovascular event (0.5-1.0 per 100 py) was very low, without any significant differences across agents. DISCUSSION: In a real-world cohort of patients with UC, anti-ILs and JAK inhibitors were associated with a lower risk of serious infections and may offer net benefit, especially in patients with previous TNF antagonist exposure.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".