Risk of bleeding associated with combined use of selective serotonin reuptake inhibitors and antiplatelet therapy following acute myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Patients prescribed antiplatelet treatment to prevent recurrent acute myocardial infarction are often also given a selective serotonin reuptake inhibitor (SSRI) to treat coexisting depression. Use of either treatment may increase the risk of bleeding. We assessed the risk of bleeding among patients taking both medications following acute myocardial infarction. METHODS: We conducted a retrospective cohort study using hospital discharge abstracts, physician billing information, medication reimbursement claims and demographic data from provincial health services administrative databases. We included patients 50 years of age or older who were discharged from hospital with antiplatelet therapy following acute myocardial infarction between January 1998 and March 2007. Patients were followed until admission to hospital due to a bleeding episode, admission to hospital due to recurrent acute myocardial infarction, death or the end of the study period. RESULTS: The 27,058 patients in the cohort received the following medications at discharge: acetylsalicylic acid (ASA) (n = 14,426); clopidogrel (n = 2467), ASA and clopidogrel (n = 9475); ASA and an SSRI (n = 406); ASA, clopidogrel and an SSRI (n = 239); or clopidogrel and an SSRI (n = 45). Compared with ASA use alone, the combined use of an SSRI with antiplatelet therapy was associated with an increased risk of bleeding (ASA and SSRI: hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.08-1.87; ASA, clopidogrel and SSRI: HR 2.35, 95% CI 1.61-3.42). Compared with dual antiplatelet therapy alone (ASA and clopidogrel), combined use of an SSRI and dual antiplatelet therapy was associated with an increased risk of bleeding (HR 1.57, 95% CI 1.07-2.32). INTERPRETATION: Patients taking an SSRI together with ASA or dual antiplatelet therapy following acute myocardial infarction were at increased risk of bleeding.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".