Opportunistic Infections in Patients with Inflammatory Bowel Disease Treated with Advanced Therapies: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Advanced therapies for inflammatory bowel disease [IBD] could potentially lead to a state of immunosuppression with an increased risk of opportunistic infections [OIs]. We aimed to provide an update on the incidence of OIs among adult IBD patients in randomized controlled trials [RCTs] of approved biologics and small-molecule drugs [SMDs]. Also, we aimed to describe OI definitions utilized in RCTs, to ultimately propose a standardized definition. METHODS: Electronic databases were searched from January 1, 1990, until April 16, 2022. Our primary outcome was incidence rate of overall OIs among IBD patients exposed and unexposed to biologics or SMDs. We also describe specific OIs reported in included trials, as well as definitions of OIs within studies when provided. RESULTS: Ninety studies were included. The incidence rates of reported OIs were 0.42 and 0.21 per 100 person-years in patients exposed to advanced therapies and placebo, respectively. This was highest for anti-tumour necrosis factors [0.83 per 100 person-years] and Janus kinase inhibitors [0.55 per 100 person-years] and lowest for anti-integrins and ozanimod. On meta-analysis, no increased risk of OIs was observed. None of the studies provided a detailed definition of OIs, or a comprehensive list of infections considered as OIs. CONCLUSION: Different mechanisms of action may have specific OI profiles. In the absence of a uniform definition of OIs, these estimates are less reliable. We propose a definition to be used in future studies to help provide standardized reporting. When using this definition, we saw significant differences in incidence rates of OIs across mechanisms of action.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".