Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG).
This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs.
We used electronic medical records from 5 hospitals and identified ECGs from adults with documented PVCs. Internal training and testing were performed at one hospital. External validation was performed with the others. The primary outcome was first diagnosis of LVEF ≤40% within 6 months. The dataset included 383,514 ECGs, of which 14,241 remained for analysis. We analyzed area under the receiver operating curves and explainability plots for representative patients, algorithm prediction, PVC burden, and demographics in a multivariable Cox model to assess independent predictors for cardiomyopathy.
Among the 14,241-patient cohort (age 67.6 ± 14.8 years; female 43.8%; White 29.5%, Black 8.6%, Hispanic 6.5%, Asian 2.2%), 22.9% experienced reductions in LVEF to ≤40% within 6 months. The model predicted reductions in LVEF to ≤40% with area under the receiver operating curve of 0.79 (95% CI: 0.77-0.81). The gradient weighted class activation map explainability framework highlighted the sinus rhythm QRS complex-ST segment. In patients who underwent successful PVC ablation there was a post-ablation improvement in LVEF with resolution of cardiomyopathy in most (89%) patients.
Deep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden. Model prediction performed well across sex and race, relying on the QRS complex/ST-segment in sinus rhythm, not PVC morphology.
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MV-WBSTE-002 Rev A
