Background: Heterogeneous ventricular activation can provide the substrate for ventricular arrhythmias (VA), but its manifestation on the electrocardiogram (ECG) as a risk stratifier is not well-defined.
Objective: To characterize the spatiotemporal features of QRS peaks that best predict VA in patients with cardiomyopathy (CM) using machine learning (ML).
Methods: Prospectively enrolled CM patients with prophylactic defibrillators (n=95) underwent digital, high-resolution ECG recordings during intrinsic rhythm and ventricular pacing at 100 to 120 beats/min.
Background: Atrial low-voltage areas (LVAs) in patients with atrial fibrillation increase the risk of atrial arrhythmia (AA) recurrence after pulmonary vein isolation (PVI). Contemporary LVA prediction scores (DR-FLASH, APPLE) do not include P-wave metrics. We aimed to evaluate the utility of P-wave duration/amplitude ratio (PWR) in quantifying LVA and predicting AA recurrence after PVI.
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