Real-world novel adverse drug reactions (ADRs) associated with indacaterol
Bibliographic record
Abstract
Pharmacovigilance plays a key role in monitoring drug safety. Indacaterol, an inhaled ultra-long-acting β2-agonist, used for chronic obstructive pulmonary disease (COPD) and asthma, has limited post-marketing safety data. This study aims to assess the real-world safety profile of indacaterol using data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Adverse event reports (AERs) were retrieved from the FAERS database from Q4 2003 to Q3 2024. OpenVigil 2.1, a web-based pharmacovigilance tool, was used to extract and process data. AERs were categorized using the Standardized MedDRA Queries (SMQs) framework. Disproportionality analysis (DPA) was performed using the Proportional Reporting Ratio (PRR) method. Events with PRR > 2 and chi-square (χ²) > 4 were flagged as potential safety signals per the Evans criteria (Evans, S. J. et al. Pharmacoepidemiol Drug Saf 2001;10:483-6). The study has received ethics approval. 3,869 cases involving 151 adverse effects were identified where indacaterol was the primary suspect. DPA flagged 23 (15.2%) SMQ terms. Indacaterol was associated with arrhythmias (tachyarrhythmias, supraventricular tachyarrhythmias, ventricular tachyarrhythmias), cardiac failure, and shock-associated circulatory conditions, contrary to randomized controlled trial data from the drug label. Previously unreported adverse effects such as lens disorders, dementia, and hearing impairment were identified, and known adverse events of glaucoma were corroborated. This study provides new insights into the safety profile of indacaterol, identifying potential ADRs not previously reported in clinical trials. Further research is necessary to confirm these findings.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".