PVCuRe: a machine-learning based tool to predict left ventricular systolic function recovery in patients undergoing ablation for premature ventricular contraction.
This cohort study examined However, many patients fail to normalize LVEF despite successful ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopa. Key finding: 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.
This cohort study examined However, many patients fail to normalize LVEF despite successful ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopa. Key finding: 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.
What the study was
- Study design
- Cohort study
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Heart rhythm
Why it surfaced
High-priority cohort study in AI/ML in clinical diagnostics and imaging.
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