The impact of selective serotonin reuptake inhibitors on the risk of intracranial haemorrhage: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction Observational studies have suggested increased risk of intracranial haemorrhage (ICrH) in patients receiving selective serotonin reuptake inhibitors (SSRIs). We sought to clarify the impact of SSRIs on ICrH, accounting for study methodology. Patients and methods A comprehensive search of Medline, Embase and the Cochrane Library from 1960 to December 2017 identified studies comparing SSRIs with control. The outcomes (first-ever and recurrent ICrH) were meta-analysed using a random effects model. Results Twenty-four observational studies and three randomised trials were available for meta-analysis, totalling 4,844,090 patient-years of follow-up. Those receiving SSRIs were more likely to be female (p = 0.01) and have depression (p < 0.001). Compared to controls, SSRI users had a significantly increased risk of ICrH (relative risk (RR) 1.26, 95%CI 1.11–1.42). Although SSRI use was associated with increased ICrH risk in those without previous ICrH (RR 1.31, 95%CI 1.15–1.48), this was not the case in those with previous ICrH (RR 0.95, 95%CI 0.83–1.09). Sensitivity analysis according to the bleeding definition reported demonstrated that although ‘haemorrhagic stroke’ was associated with SSRIs (RR 1.40, 95%CI 1.13–1.72), intracerebral haemorrhage was not (RR 1.11, 95%CI 0.86–1.42). Additional sensitivity analyses demonstrated a stronger association between SSRIs and ICrH in studies with a high (p < 0.001) compared to low risk of bias (p = 0.09) and with retrospective (p < 0.001) compared to prospective (p=0.31) study designs. Discussion Although SSRIs are associated with an increased risk of ICrH, the association is partly accounted for by important biases and other methodological limitations in the available observational data. Conclusion Our findings suggest there is insufficient high-quality data to advise restriction of SSRIs because of concern regarding ICrH risk.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".