Key result
Random forest machine learning model predicts LVEF recovery after PVC ablation with ~0.88 AUC.
Why the study?
Many patients with systolic dysfunction fail to normalize LVEF after successful PVC ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopathy exacerbated by PVCs.
Can a machine-learning model using routinely available variables accurately predict LVEF recovery in patients with LVEF <50% undergoing successful PVC ablation?
Observational (n=256)
Yes
Can a machine-learning model using routinely available variables accurately predict LVEF recovery in patients with LVEF <50% undergoing successful PVC ablation?
Effect estimate: AUC 0.88 (95% CI 0.79-0.98)
A novel machine-learning tool using widely available clinical variables can accurately predict the probability of left ventricular systolic function recovery after successful PVC ablation, potentially aiding in patient counseling and clinical decision-making.
BACKGROUND Ablation of frequent premature ventricular complexes (PVCs) can improve left ventricular ejection fraction (LVEF) in patients with systolic dysfunction, especially in suspected PVC-induced cardiomyopathy. However, many patients fail to normalize LVEF despite successful ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopathy exacerbated by PVCs. OBJECTIVE 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% undergoing successful PVC ablation at three international referral centers were included. Predictors were selected using the Boruta algorithm, and five ML models were trained. Performance was assessed with 10-fold cross-validation and ROC curve analysis. The best-performing model underwent calibration and threshold analysis and was externally validated in an independent cohort from three additional centers. RESULTS The Random Forest model showed the best performance, with an AUC of 0.88 (95% CI 0.79-0.98) in the internal test set and good calibration (Hosmer-Lemeshow p=0.562). External validation confirmed consistent discrimination (AUC 0.83, 95% CI 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 counselling.
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Saglietto et al. (2026) conducted an observational in Premature ventricular complexes with systolic dysfunction (n=256). Machine learning model (PVCuRe) was evaluated on LVEF recovery prediction performance (Area Under the Curve) (AUC 0.88, 95% CI 0.79-0.98). A Random Forest machine learning model accurately predicted LVEF recovery after PVC ablation, achieving an AUC of 0.88 (95% CI 0.79-0.98) internally and 0.83 externally.
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