Optimal duration of dual anti-platelet therapy after percutaneous coronary intervention
Bibliographic record
Abstract
Definition of the optimal duration of dual anti-platelet therapy (DAPT) is an important clinical issue, given the large number of patients having percutaneous coronary intervention (PCI), the costs and risks of pharmacologic therapy, the consequences of stent thrombosis, and the potential benefits of DAPT in preventing ischaemic outcomes beyond stent thrombosis. Nowadays, the rationale for a prolonged duration of DAPT should be not only the prevention of stent thrombosis, but also the prevention of ischaemic events unrelated to the coronary stenosis treated with index PCI. A higher predisposition to athero-thrombosis may persist for years after an acute myocardial infarction, and even stable patients with a history of prior myocardial infarction are at high risk for major adverse cardiovascular events. Recently, results of pre-specified post-hoc analyses of randomized clinical trials, including the PEGASUS-TIMI 54 trial, have shed light on strategies of DAPT in various clinical situations, and should impact the next rounds of international guidelines, and also routine practice. Accordingly, the 2015 to 2016 the Board of the Italian Society of Cardiology addressed newer recommendations on duration of DAPT based on most recent scientific information. The document states that physicians should decide duration of DAPT on an individual basis, taking into account ischaemic and bleeding risks of any given patient. Indeed, current controversy surrounding optimal duration of DAPT clearly reflects the fact that, nowadays, a one size fits all strategy cannot be reliably applied to patients treated with PCI. Indeed, patients usually have factors for both increased ischaemic and bleeding risks that must be carefully evaluated to assess the benefit/risk ratio of prolonged DAPT. Personalized management of DAPT must be seen as a dynamic prescription with regular re-evaluations of the risk/benefit to the patient according to changes in his/her clinical profile. Also, in order to derive more benefit than harm from new treatments, a multi-parametric approach using several risk scores of the ischaemic and bleeding risks might improve the process of risk factor characterization. In patients with high ischaemic risk, particularly those with a history of myocardial infarction, the benefits of extended DAPT (particularly with ticagrelor up to 3 years) are likely to outweigh the risks.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".