Impact of Clopidogrel Therapy on Mortality and Cancer in Patients With Cardiovascular and Cerebrovascular Disease
Bibliographic record
Abstract
Background— Clinical trial data associate extended clopidogrel therapy with increased mortality and cancer. We sought to determine the impact of continued clopidogrel use on mortality and cancer within a patient-level meta-analysis of randomized clinical trials. Methods and Results— Meta-analytic clinical event rates for all-cause, cardiovascular, noncardiovascular, and cancer-related mortality; cancer; myocardial infarction; stroke; and fatal and major nonfatal bleeding were generated using patient-level data from 6 randomized trials comparing prolonged versus no or short-duration clopidogrel on a background of aspirin in patients with cardiovascular and cerebrovascular disease. Among 48 817 randomized patients (median follow-up 546 days), there was no difference in all-cause (7.23% versus 7.26%; P =0.97), cardiovascular (5.25% versus 5.22%; P =0.86), noncardiovascular (1.98% versus 2.03%; P =0.73), and cancer-related (0.93% versus 0.99%; P =0.59) mortality or in new cancer diagnoses (2.97% versus 2.96%; P >0.99). Rates of myocardial infarction (3.21% versus 4.05%; P <0.0001) and stroke (3.04% versus 3.75%; P <0.0001) were significantly lower in patients receiving continued clopidogrel. Fatal bleeding was more common with continued clopidogrel use (0.39% versus 0.27%; P =0.03), as were major nonfatal bleeding (4.06% versus 2.68%; P <0.0001) and intracranial hemorrhage (0.43% versus 0.30%; P =0.02). Conclusions— Across trials of cardiovascular and cerebrovascular disease, extended-duration clopidogrel on a background of aspirin has no overall effect on mortality or cancer but does reduce rates of myocardial infarction and stroke and increase rates of bleeding. These findings emphasize the need for selective use of extended clopidogrel therapy in patients in whom the risks of ischemia are not fully counterbalanced by the risks 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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".