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Sep 2026

Speckle tracking echocardiography predicts electroanatomical scar in patients with ischaemic cardiomyopathy undergoing catheter ablation for ventricular tachycardia.

INTRODUCTION Characterisation of the myocardial substrate before ventricular arrhythmia (VA) ablation procedures is paramount for procedural planning. Cardiac magnetic resonance (CMR) can identify areas of fibrosis, but it is often not feasible. Speckle tracking echocardiography (STE) may be an alternative for identifying subtle left ventricular alterations. This study evaluated a segment- and layer-specific longitudinal strain (LS) approach for predicting electroanatomical scar. METHODS From April 2022 to May 2024, consecutive patients with ischaemic cardiomyopathy undergoing ablation for recurrent VAs were prospectively enrolled. LS and electroanatomical data were matched using an 18-segment model. Prediction of electroanatomical scar with segmental layer-specific LS was evaluated using mixed-effects logistic regression. Receiver Operating Characteristic (ROC) curve analysis was used to evaluate diagnostic performance and optimal thresholds. RESULTS Thirty-two patients were enrolled: median age was 70.3 years, median left ventricular ejection fraction was 37%, and 1458 pairs of segments were analysed. LS was impaired in segments with electroanatomical scar compared to normal segments in all myocardial layers (p < 0.0001). Endocardial strain predicted bipolar voltage-defined scar (5.0% higher odds per strain unit, p = 0.004, AUC 0.787). Midmyocardial strain predicted unipolar voltage-defined scar (5.6% higher odds per strain unit, p = 0.016, AUC 0.834). Optimal thresholds were - 5.4% for endocardial strain and - 14.6% for midmyocardial strain. CONCLUSION Segmental layer-specific STE effectively predicts electroanatomical scar in patients with ischaemic cardiomyopathy, and may be a valuable pre-procedural planning tool, particularly when CMR is unavailable.

F. A. Gabrielli, G. Bencardino, F. Ballacci et al. · 0 citations
Sep 2026

PVCuRe: a machine-learning based tool to predict left ventricular systolic function recovery in patients undergoing ablation for premature ventricular contraction.

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.

A. Saglietto, D. Penela, G. Falasconi et al. · 0 citations

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