Comparing Stroke Risk in Patients Treated with Selective Serotonin Reuptake Inhibitors (SSRIs) versus Non-SSRI Antidepressants: A retrospective cohort study and meta-analysis
Bibliographic record
Abstract
ABSTRACT Aims Despite the presence of studies indicating a potential elevated risk of stroke associated with selective serotonin reuptake inhibitors (SSRIs), current evidence is inconclusive. This study aims to evaluate the stroke risk associated with SSRI use using non-SSRI antidepressants as comparator through retrospective cohort study and meta-analysis of observational studies. Methods We extracted data from a territory-wide public healthcare database in Hong Kong to conduct a retrospective cohort study of patients aged 18+ years who started on SSRI or non-SSRI antidepressants between January 2018 to April 2024. Poisson regression with robust variance estimation was conducted to estimate the incidence rate ratio of stroke in SSRI users using non-SSRI users as active comparator. We subsequently conducted a systematic review and meta-analysis based on the current cohort study and all existing published observational data. Quality of studies was assessed using the Newcastle-Ottawa Scale. Results 122,679 individuals were included in the cohort study, among which 55,279 were SSRI users. SSRI users had an adjusted HR of 0.95 (95% CI 0.77-1.20) for stroke compared to non-SSRI users, suggesting a non-significant lower risk of stroke. Findings were consistent across subgroups by stroke types (i.e. ischemic stroke and hemorrhagic stroke). The result of our cohort study was aggregated with 5 other observational studies, and a pooled estimates of RRs were extracted (RR 0.93, 95% CI 0.81-1.07). Conclusion Our findings suggested that compared with non-SSRI antidepressants, SSRIs are not associated with a higher risk of stroke based on all available observational data.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.051 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".