Drug-induced urinary retention: a real-world pharmacovigilance study using FDA and Canada vigilance databases
Bibliographic record
Abstract
Background Urinary retention (UR) is a clinical condition where patients cannot fully empty their bladder. Although numerous drugs are associated with UR, comprehensive and reliable studies identifying drugs that induce UR are scarce. Methods This study leveraged data from the FDA Adverse Event Reporting System (FAERS) and the Canadian Vigilance Adverse Reaction (CVAR) database to explore adverse events (AEs) related to UR from 2004 to Q1 2024. The top 50 drugs were analyzed for annual reporting trends using linear regression. Disproportionality analysis using the reporting odds ratio (ROR) method, with P -values adjusted via Bonferroni correction, identified significant signals, which were then validated against drug labels and re-evaluated using the CVAR database. Time-to-onset analysis was also performed. Results From 2004 to Q1 2024, FAERS recorded 17,785,793 AEs, with 16,183 (0.09%) identified as UR cases. The median age among these cases was 65 years, with males comprising 53.4%. There were significant annual increases in UR reports associated with antineoplastic agents (0.19% per year) and antidiabetic drugs (0.09% per year), while reports linked to bronchodilators decreased (−0.53% per year). Disproportionality analysis revealed significant signals for 34 drugs (68%), with the highest RORs observed in Fesoterodine, Mirabegron, and Solifenacin. Initial signal detection identified potential new UR signals for Abiraterone, Valacyclovir, Fluoxetine, Empagliflozin, Clopidogrel, and Amlodipine, with CVAR confirming signals for Abiraterone, Fluoxetine, and Empagliflozin. The median time to onset of UR was 29 days, with over half of the cases occurring within 30 days of initiating medication. Conclusion The study identifies a rising trend in drug-related UR reports over the past 2 decades. The validation of new signals for Abiraterone, Fluoxetine, and Empagliflozin underscores the critical need for continuous drug safety monitoring and targeted research to better understand the mechanisms behind drug-induced UR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".