Auditory system adverse events with sacubitril/valsartan: an active-comparator restricted disproportionality analysis using the FDA adverse event reporting system database
Bibliographic record
Abstract
BACKGROUND: Sacubitril/valsartan, an angiotensin receptor-neprilysin inhibitor, is used for heart failure with reduced ejection fraction. Emerging evidence suggests potential ototoxicity, including hearing loss and vestibular disorders, which remain underreported and poorly characterized. RESEARCH DESIGN AND METHODS: Our objective was to compare auditory adverse event (AE) reports associated with sacubitril/valsartan versus those associated with lisinopril and losartan. A retrospective active-comparator disproportionality analysis was conducted using individual case safety reports (ICSRs) from the U.S. FDA Adverse Event Reporting System (FAERS) via OpenFDA (2015Q3-2023Q3). The Medical Dictionary for Regulatory Activities (MedDRA) terms identified AEs related to hearing impairment, vestibular disorders, and hypoacusis. Adjusted reporting odds ratios (aRORs) and 95% confidence intervals (CIs) were estimated using logistic regression, adjusting for age, sex, hypertension, and heart failure. Bayesian methods complemented the analysis. RESULTS: Among 55,101 ICSRs, 28,091 involved sacubitril/valsartan, with 590 (1.07%) reports of hearing impairment and 4,732 (8.58%) vestibular disorders. Compared to lisinopril, sacubitril/valsartan had higher aRORs for hearing impairment (2.35), vestibular disorders (2.58), and hypoacusis (15.03). Similar elevated risks were found versus losartan. Bayesian analysis confirmed these patterns. CONCLUSIONS: Sacubitril/valsartan may be associated with a higher risk of auditory AEs than lisinopril and losartan. These findings warrant further confirmation through pharmacoepidemiologic studies.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".