Average daily ischemic versus bleeding risk in patients with ACS undergoing PCI: Insights from the BleeMACS and RENAMI registries

Background: The risk of recurrent ischemia and bleeding after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) may vary during the first year of follow-up according to clinical presentation, and medical and interventional strategies. Methods: BleeMACS and RENAMI are 2 multicenter registries enrolling patients with ACS treated with PCI and clopidogrel, prasugrel, or ticagrelor. The average daily ischemic and bleeding risks (ADIR and ADBR) in the first year after PCI were the primary end points. The difference between ADBR and ADIR was calculated to estimate the potential excess of bleeding/ischemic events in a given period or specific subgroup. Results: A total of 19,826 patients were included. Overall, in the first year after PCI, the ADBR was 0.008085%, whereas ADIR was 0.008017% (P =.886). In the first 2 weeks ADIR was higher than ADBR (P =.013), especially in patients with ST-segment elevation myocardial infarction or incomplete revascularization. ADIR continued to be, albeit non-significantly, greater than ADBR up to the third month, whereas ADBR became higher, although not significantly, afterward. Patients with incomplete revascularization had an excess in ischemic risk (P =.003), whereas non–ST-segment elevation ACS patients and those on ticagrelor had an excess of bleeding (P =.012 and P =.022, respectively). Conclusions: In unselected ACS patients, ADIR and ADBR occurred at similar rates within 1 year after PCI. ADIR was greater than ADBR in the first 2 weeks, especially in ST-segment elevation myocardial infarction patients and those with incomplete revascularization. In the first year, ADIR was higher than ADBR in patients with incomplete revascularization, whereas ADBR was higher in non–ST-segment elevation ACS patients and in those discharged on ticagrelor

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