Fluoroquinolones and risk of nightmares: A literature review and disproportionality analysis using individual case safety reports from Food and Drug Administration Adverse Event Reporting System database
Bibliographic record
Abstract
Background: Fluoroquinolones (FQs) have been linked to various neuropsychiatric effects, including nightmares, mostly through case reports. However, data on nightmares remain limited and underreported. Aims: To review the literature on FQ-related nightmares and estimate the risk of nightmares associated with FQs compared to other antibiotics using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database. Methods: A literature review was conducted to identify studies on FQ-related nightmares. Active-comparator restricted disproportionality analyses were performed in FAERS (2004Q1–2023Q4) for ciprofloxacin, levofloxacin, and moxifloxacin compared to azithromycin and trimethoprim-sulfamethoxazole. We calculated reporting odds ratios (RORs), proportional reporting ratios, adjusted ROR (accounting age, sex, weight, and specific indications), and information components (IC 025 ) to detect safety signals for the Medical Dictionary for Regulatory Activities term “nightmare.” Results: The review identified seven studies, with the prevalence of nightmares ranging from 0.01% to 8% across three trials. Disproportionality analyses indicated that FQ-associated nightmare reports were 6- to 10-fold higher than those linked to azithromycin (ROR: 6.18, 95% CI: 4.14–9.23) and trimethoprim-sulfamethoxazole (ROR: 10.38, 95% CI: 4.92–21.89), largely reported by consumers. These findings were consistent across frequentist and Bayesian methods and adjusted analyses. Conclusion: FQs may increase the risk of nightmares. Our findings provide valuable insights for future research on their safety profile. Further research is needed to validate these findings and guide safe FQ use.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".