Heart rhythm · 2026
PVCuRe: A machine-learning-based tool to predict left ventricular systolic function recovery in patients undergoing ablation for premature ventricular contraction.
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Saglietto Andrea, Penela Diego, Falasconi Giulio, Francia Pietro, Soto-Iglesias David, Bisbal Felipe, Silva Etel, Tritto Massimo, Latini Alessia Chiara, Tonello Bruno, Micari Antonino, Amata Francesco, Festa Pasquale Alessandro, Cersosimo Angelica, Soro Paolo, Sanchez-Mollà Lautaro, De Lucia Carmine, Penna Mattia, Turturiello Dario, Marti-Almor Julio, Acosta Juan, Aguinaga Luis, Fernandez-Armenta Juan, Berruezo Antonio
Аннотация
BACKGROUND: Ablation of frequent premature ventricular complexes (PVCs) can improve left ventricular ejection fraction (LVEF) in patients with systolic dysfunction, especially in those with suspected PVC-induced cardiomyopathy. However, many patients fail to achieve normalization of LVEF despite successful ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopathy exacerbated by PVCs.
OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning (ML) model using routinely available clinical, echocardiographic, and electrocardiographic variables to predict LVEF recovery after PVC ablation.
METHODS: In this retrospective multicenter study, 256 patients with LVEF <50% who underwent successful PVC ablation at 3 international referral centers were included. Predictors were selected using the Boruta algorithm, and 5 ML models were trained. Performance was assessed with 10-fold cross-validation and receiver operating characteristic curve analysis. The best-performing model underwent calibration and threshold analysis and was externally validated in an independent cohort from 3 additional centers.
RESULTS: The Random Forest model showed the best performance, with an area under the curve of 0.88 (95% confidence interval = 0.79-0.98) in the internal test set and good calibration (Hosmer-Lemeshow P = .562). External validation confirmed consistent discrimination (area under the curve = 0.83, 95% confidence interval = 0.72-0.95). Key predictors included baseline PVC burden, QRS duration in sinus rhythm, and preprocedural LVEF.
CONCLUSION: This ML-based tool, built on widely available variables, accurately estimates the probability of LVEF recovery after PVC ablation and may support clinical decision making and patient counseling.
OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning (ML) model using routinely available clinical, echocardiographic, and electrocardiographic variables to predict LVEF recovery after PVC ablation.
METHODS: In this retrospective multicenter study, 256 patients with LVEF <50% who underwent successful PVC ablation at 3 international referral centers were included. Predictors were selected using the Boruta algorithm, and 5 ML models were trained. Performance was assessed with 10-fold cross-validation and receiver operating characteristic curve analysis. The best-performing model underwent calibration and threshold analysis and was externally validated in an independent cohort from 3 additional centers.
RESULTS: The Random Forest model showed the best performance, with an area under the curve of 0.88 (95% confidence interval = 0.79-0.98) in the internal test set and good calibration (Hosmer-Lemeshow P = .562). External validation confirmed consistent discrimination (area under the curve = 0.83, 95% confidence interval = 0.72-0.95). Key predictors included baseline PVC burden, QRS duration in sinus rhythm, and preprocedural LVEF.
CONCLUSION: This ML-based tool, built on widely available variables, accurately estimates the probability of LVEF recovery after PVC ablation and may support clinical decision making and patient counseling.