Association between dual orexin receptor antagonists (DORAs) and suicidality: reports to the United States Food and Drug Administration Adverse Event Reporting System (FAERS)
Bibliographic record
Abstract
Background Package inserts for the FDA-approved dual orexin receptor antagonists (DORAs) suvorexant, lemborexant and daridorexant state that suicide risk should be monitored. It remains unknown whether suicidality is attributed to DORAs. We aim to evaluate suicidality associated with DORAs reported to the FDA Adverse Event Reporting System (FAERS).Methods The reporting odds ratio (ROR) was determined with trazodone as the control. Significant disproportionate reporting was determined when 95% confidence intervals (CIs) did not encompass 1.0. We used information components (ICs) to calculate the lower limit of the 95% CI (IC025). IC was significantly increased when the IC025 ≥0.Results Suvorexant (0.025 ROR), lemborexant (0.019 ROR), and daridorexant (0.002 ROR) were significantly associated with lower odds of reported completed suicides compared to trazodone (p < 0.05). There was no significantly increased RORs for the DORAs regarding suicidal ideation, depression suicidal, suicidal behavior and suicide attempts. Nonsignificant associations between all parameters of suicidality were observed for each DORA using IC025.Conclusion We did not find a significant association between any parameter of suicidality captured in the FAERS for each DORA. All persons treated for insomnia pharmacologically/non-pharmacologically should be evaluated for emergence/worsening of any suicidality aspect.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".