Impact of Right Ventricular Pacing in Patients With TAVR Undergoing Permanent Pacemaker Implantation

BACKGROUND Long-term right ventricular pacing (VP) has been related to negative left ventricular remodeling and heart failure (HF), but there is a lack of evidence regarding the prognostic impact on transcatheter aortic valve replacement (TAVR) patients.OBJECTIVES The aim of the PACE-TAVI registry is to evaluate the association of high percentage of VP with adverse outcomes in patients with pacemaker implantation after TAVR.METHODS PACE-TAVI is an international multicenter registry of all consecutive TAVR patients who underwent permanent pacemaker implantation for conduction disturbances in the first 30 days after the procedure. Patients were divided into 2 subgroups according to the percentage of VP (<40% vs & GE;40%) at pacemaker interrogation. The primary endpoint was the composite of cardiovascular mortality or hospitalization for HF. RESULTS A total of 377 patients were enrolled, 158 with VP <40% and 219 with VP & GE;40%. After multivariable adjustment, VP & GE;40% was associated with a higher incidence of the primary endpoint (HR: 2.76; 95% CI: 1.39-5.51; P = 0.004), first HF hospitalization (HR: 3.37; 95% CI: 1.50-7.54; P = 0.003), and cardiovascular death (HR: 3.77; 95% CI: 1.02-13.88; P = 0.04), while the incidence of all-cause death was not significantly different (HR: 2.17; 95% CI: 0.80-5.90; P = 0.13). Patients with VP & GE; 40% showed a higher New York Heart Association functional class both at 1 year (P = 0.009) and at last available follow-up (P = 0.04) and a nonsignificant reduction of left ventricular ejection fraction (P = 0.18) on 1-year echocardiography, while patients with VP <40% showed significant improvement (P = 0.009).CONCLUSIONS In TAVR patients undergoing permanent pacemaker implantation, a high percentage of right VP at follow-up is associated with an increased risk for cardiovascular death and HF hospitalization. These findings suggest the opportunity to minimize right VP through dedicated algorithms in post-TAVR patients without complete atrioventricular block and to evaluate a more physiological VP modality in patients with persistent complete atrioventricular block. (J Am Coll Cardiol Intv 2023;16:1081-1091) & COPY; 2023 by the American College of Cardiology Foundation

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