Research in context
Evidence before this study
Prediction of all-cause death in patients with acute coronary syndrome (ACS) has relevant implication on post-discharge follow-up and treatment approaches. Similarly, ischaemic and bleeding events after an ACS can also severely affect patient prognosis. We searched PubMed on July 30, 2020, without language or date restrictions for publications about risk scores validated to predict mortality along with bleeding and thrombotic risks in patients with ACS. We used the search terms “acute coronary syndrome”, “percutaneous coronary intervention”, “risk score”, “mortality risk score”, “bleeding risk score”, “thrombotic risk score”, and “antiplatelet therapy”. We excluded articles reporting only multivariate predictors without prediction score, papers referring only to antithrombotic management for patients with a concomitant indication to oral anticoagulation, risk prediction models for only in-hospital outcomes, and those without a validation cohort. Currently available scores conceived and validated to predict patients' mortality after all types of ACS (ie, the GRACE and TIMI risk scores) were mostly derived on cohorts of patients not treated with contemporary standards of care. Current guidelines support the use of dedicated scores (ie, PRECISE-DAPT and DAPT scores) to tailor the intensity and duration of dual antiplatelet therapy (DAPT) strategy according to the offsetting risk of ischaemia and bleeding. Nonetheless, such scores were derived from unselected cohorts of patients mostly treated with clopidogrel after percutaneous revascularisation both for stable coronary artery disease and ACS, thus limiting their accuracy in external cohorts and their applicability in clinical practice. Furthermore, all of these scores are based on linear models applied to variables frequently based on a-priori assumption. The feasibility and the effectiveness of a machine learning-based prognostic risk assessment in this setting has never been explored before.
Added value of this study
We developed and externally tested the PRAISE score, a machine learning-based model to predict the risk of 1-year post-ACS all-cause death, recurrent myocardial infarction, and major bleeding. The model was derived from a contemporary cohort of patients treated with percutaneous coronary intervention for ACS and treated according to current guidelines recommendation on DAPT. The PRAISE score showed excellent predictive abilities for all the explored endpoints both in the derivation and external validation cohort. To highlight the clinical implication of the risk estimated by the model, we suggested a stratification into three classes (low, intermediate, and high) entailing a clinically significant increase in the risk of event occurrence. According to such classification, the PRAISE scores would classify almost 10% of patients as being at high risk of 1-year post-discharge ischaemic and bleeding events, thus being candidates for a tighter follow-up. Furthermore, patients deemed at high ischaemic risk according to the PRAISE score showed a consistent prevailing ischaemic risk, regardless of their bleeding risk class, which could potentially be used to identify patients who would benefit most from an extended DAPT strategy. By contrast, patients with intermediate-to-high bleeding risk and a low ischaemic risk would benefit from a shortened DAPT strategy.
Implications of all the available evidence
In the setting of risk assessment, the machine learning approach offers a way to overcome the shortcomings of existing methods by applying computer algorithms to large datasets and capturing non-linear relationships between clinical variables. The PRAISE score is a bedside risk assessment tool that could be easily implemented in everyday clinical practice to predict patients' prognosis after ACS, along with their risk of ischaemic and bleeding events. This instrument has the potential to support clinical decision making with respect to antithrombotic treatments and follow-up planning, thus addressing the unmet need for tailored care after percutaneous coronary intervention.