Back

Comparing Stroke Risk in Patients Treated with Selective Serotonin Reuptake Inhibitors (SSRIs) versus Non-SSRI Antidepressants: A retrospective cohort study and meta-analysis

Yeung, P. W. C.; Sun, Q.; Su, J. C. Y.; Lai, F.

2025-10-30 epidemiology
10.1101/2025.10.28.25339028 medRxiv
Show abstract

AimsDespite 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. MethodsWe 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. Results122,679 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). ConclusionOur findings suggested that compared with non-SSRI antidepressants, SSRIs are not associated with a higher risk of stroke based on all available observational data.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.