The association of PRECISE-DAPT score with ischaemic outcomes in patients taking dual antiplatelet therapy following percutaneous coronary intervention: a meta-analysis
Bibliographic record
Abstract
AIMS: The PRECISE-DAPT (Predicting Bleeding Complication in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy) score identifies patients at high risk of bleeding complications following percutaneous coronary intervention (PCI). International guidelines recommend the PRECISE-DAPT score to identify patients at high risk for bleeding, who may benefit from shortened dual antiplatelet therapy. The association of the PRECISE-DAPT score with ischaemic outcomes remains unclear. We performed a meta-analysis investigating the association between a high PRECISE-DAPT score and ischaemic outcomes. METHODS AND RESULTS: A comprehensive literature search was conducted on articles published between 11 March 2017 and 5 June 2021. Two reviewers independently screened articles for inclusion using pre-defined criteria. The outcome measures extracted included composite ischaemic events, major bleeding events, and all-cause mortality. A random effects model was applied to obtain combined risk estimates for outcomes. From 12 included studies, there were 39 459 patients with PRECISE-DAPT <25 and 14 761 patients with PRECISE-DAPT ≥25. PRECISE-DAPT score ≥25 was associated with increased risk of composite ischaemic events [odds ratio (OR) 2.16; 95% confidence interval (CI) 1.77-2.65], myocardial infarction (OR 2.06; 95% CI 1.38-3.08), and ischaemic stroke (OR 2.90; 95% CI 1.76-4.78). Patients with a PRECISE-DAPT score ≥25 had increased risk of major bleeding (OR 3.62; 95% CI 2.62-4.99). Patients with a PRECISE-DAPT score ≥25 had higher risk of all-cause mortality (OR 5.83; 95% CI 5.37-6.33). CONCLUSION: Patients with a PRECISE-DAPT score ≥25 are at increased risk for ischaemic events, bleeding, and all-cause mortality. Prospective evaluation of a PRECISE-DAPT guided approach to antiplatelet therapy is required to demonstrate benefit in this high-risk population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.049 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".