Adverse drug events associated with tiotropium: a real-world pharmacovigilance study of FDA adverse event reporting system database
Bibliographic record
Abstract
Introduction: Tiotropium, a long-acting muscarinic antagonist, is commonly employed for the maintenance treatment of chronic obstructive pulmonary disease (COPD) and asthma. While its efficacy has been validated through numerous randomized controlled trials, safety concerns in real-world post-marketing settings necessitate further evaluation. Aim: This study aimed to analyze the adverse events (AEs) associated with tiotropium reported in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database to identify potential safety signals. Methods: A retrospective analysis was conducted on adverse reaction reports related to tiotropium in the FAERS database from the first quarter of 2004 to the fourth quarter of 2024. The AE names in the FAERS database were systematically classified using the Preferred Terms (PTs) and System Organ Classes (SOCs) provided by the latest version of the Medical Dictionary for Regulatory Activities (MedDRA 27.1). After deduplication, a combination of methods, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS), was employed for disproportionality analysis. Results: A total of 129,763 AE reports related to tiotropium were included in the analysis, affecting 65,045 patients. These reports encompassed 27 different SOC categories, identifying 264 AEs associated with tiotropium. After excluding certain AEs deemed clinically insignificant, the most common AEs reported were dyspnea (n = 8,600), cough (n = 2,440), and pneumonia (n = 2080). The AEs exhibiting the highest signal strength included aggravated dyspnea (ROR: 162.04), hoarseness (ROR: 43.42), and aggravated chronic obstructive airway disease (ROR: 43.17). Additionally, we identified potential risks not mentioned in the instructions (United States Prescribing Information and the Canadian Product Monograph), such as epiglottic cancer, halo vision, and malignant lung tumors. Conclusion: This study offers a more comprehensive understanding of tiotropium by uncovering previously unreported adverse reactions. Physicians should take these newly identified adverse reactions into account when prescribing this medication.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".