P2Y12 inhibitors in acute coronary syndrome patients with renal dysfunction: an analysis from the RENAMI and BleeMACS projects

AIMS: The aim of the present study was to establish the safety and efficacy profile of prasugrel and ticagrelor in real-life acute coronary syndrome (ACS) patients with renal dysfunction. METHODS AND RESULTS: All consecutive patients from RENAMI (REgistry of New Antiplatelets in patients with Myocardial Infarction) and BLEEMACS (Bleeding complications in a Multicenter registry of patients discharged with diagnosis of Acute Coronary Syndrome) registries were stratified according to estimated glomerular filtration rate (eGFR) lower or greater than 60 mL/min/1.73 m2. Death and myocardial infarction (MI) were the primary efficacy endpoints. Major bleedings (MBs), defined as Bleeding Academic Research Consortium bleeding types 3 to 5, constituted the safety endpoint. A total of 19 255 patients were enrolled. Mean age was 63 ± 12; 14 892 (77.3%) were males. A total of 2490 (12.9%) patients had chronic kidney disease (CKD), defined as eGFR <60 mL/min/1.73 m2. Mean follow-up was 13 ± 5 months. Mortality was significantly higher in CKD patients (9.4% vs. 2.6%, P < 0.0001), as well as the incidence of reinfarction (5.8% vs. 2.9%, P < 0.0001) and MB (5.7% vs. 3%, P < 0.0001). At Cox multivariable analysis, potent P2Y12 inhibitors significantly reduced the mortality rate [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.54-0.96; P = 0.006] and the risk of reinfarction (HR 0.53, 95% CI 0.30-0.95; P = 0.033) in CKD patients as compared to clopidogrel. The reduction of risk of reinfarction was confirmed in patients with preserved renal function. Potent P2Y12 inhibitors did not increase the risk of MB in CKD patients (HR 1.00, 95% CI 0.59-1.68; P = 0.985). CONCLUSION: In ACS patients with CKD, prasugrel and ticagrelor are associated with lower risk of death and recurrent MI without increasing the risk of MB

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

Background: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods: Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings: The PRAISE score showed an AUC of 0\ub782 (95% CI 0\ub778\u20130\ub785) in the internal validation cohort and 0\ub792 (0\ub790\u20130\ub793) in the external validation cohort for 1-year all-cause death; an AUC of 0\ub774 (0\ub770\u20130\ub778) in the internal validation cohort and 0\ub781 (0\ub776\u20130\ub785) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0\ub770 (0\ub766\u20130\ub775) in the internal validation cohort and 0\ub786 (0\ub782\u20130\ub789) in the external validation cohort for 1-year major bleeding. Interpretation: A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. Funding: None

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS.Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC).Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding.Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. Copyright (C) 2021 Elsevier Ltd. All rights reserved