Machine Learning Identifies Arrhythmogenic Features of QRS Fragmentation in Human Cardiomyopathy: Implications for Improving Risk Stratification.

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. Intra QRS peaks in the signal-averaged precordial leads were identified and their characteristics (amplitude, width, and timing within the QRS) were transformed into 4-bin histograms. Random forest models of these characteristics in each lead alongside clinical data were trained on 76 patients and tested on 19 patients with cross-validation to determine the features that predicted VA.

Results: Patients were followed up for 45 (22-48) months, and 21% had VA. The individual machine learning (ML) models determined (area under the receiver operating characteristic curve [AUROC]) intrinsic QRS peak amplitude (0.88), width (0.78), and location (0.84) to all predict VA. In a combined model including all QRS peak characteristics, peaks with amplitude < 31 μV in V1, a width of 4 to 8 ms in V1, and location in the final quarter of the QRS of V1 were most predictive. Neither clinical data nor QRS peak characteristics assessed during ventricular pacing improved VA prediction when combined with intrinsic QRS peak characteristics.

Conclusions: Arrhythmogenic QRS fragmentation is characterized by narrow, low-amplitude peaks in the terminal QRS of lead V1. These features alone without clinical variables or ventricular pacing are sufficient to accurately risk stratify CM patients.