Infliximab-associated Gastrointestinal Obstruction: A Pharmacovigilance Analysis of the FDA Adverse Event Reporting System (2004-2024)
Bibliographic record
Abstract
Background: This study aimed to analyze the reported connection between gastrointestinal obstruction and infliximab (IFX) using data from the FDA Adverse Event Reporting System (FAERS). Methods: Using FAERS data gathered from the first quarter of 2004 (1 January 2004) to the fourth quarter of 2024 (31 December 2024), we conducted a retrospective disproportionality analysis. The standardized MedDRA query (SMQ) restricted scope was used to collect reports that identified IFX as the main suspect medication for gastrointestinal obstruction. Four statistical strategies were used in the investigation to provide robustness in signal detection: multi-item gamma Poisson shrinker (MGPS), Bayesian confidence propagation neural network (BCPNN), proportional reporting ratio (PRR), and reporting odds ratio (ROR). Time-to-onset (TTO) analysis and univariate logistic regression were used to characterize the clinical presentation and identify risk variables for IFX-associated gastrointestinal obstruction. Results: Among 84,592 reports where IFX was the primary suspect drug, 1.94% (n=1,643) were associated with gastrointestinal obstruction. A significant disproportionality signal was detected at the SMQ level (ROR 3.89, 95% CI 3.76–4.02). Key risk factors included age <65 years, female sex, reporting from Canada, and an underlying indication of inflammatory bowel disease (IBD). The median TTO varied among obstruction subtypes but generally exhibited an early failure profile. Sensitivity analysis indicated a random failure profile in certain subtypes. Conclusion: This pharmacovigilance study identifies a significant safety signal linking IFX to gastrointestinal obstruction, particularly in younger female patients with IBD. These findings underscore the necessity for heightened clinical vigilance and further investigation into the underlying mechanisms.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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".