Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy

Abstract

Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically interpretable machine learning algorithm (C5.0) could improve diagnostic performance. We analyzed ECG and VCG data from 664 patients, 42.8% of whom had Echo-LVH. The study introduced three new criteria—Marcos VCG, Marcos VCG-ECG, and Marcos VCG-ECGsp—named in honor of the software used for VCG synthesis, and compared their diagnostic performance against 23 established ECG criteria, including Cornell voltage, Peguero-Lo Presti, and Sokolow-Lyon. Marcos VCG-ECGsp, optimized for higher specificity, was included to evaluate trade-offs in performance. Validation was performed using train/test split and 10-fold cross-validation. Marcos VCG-ECG achieved higher AUC than Cornell voltage in both training (0.81 vs. 0.68, p < 0.0001) and testing (0.78 vs. 0.69, p = 0.04). The new criteria also showed superior sensitivity compared to Peguero-Lo Presti, the most sensitive traditional criterion (73.1%, 62.4%, 55.9% vs. 30.1%, p < 0.0001). While specificity was lower than Cornell (81.1% vs. 96.4%, p = 0.017), it remained acceptable, reflecting a clinically relevant trade-off favoring detection over false positives. In conclusion, integrating ECG with VCG through machine learning enances Echo-LVH detection, delivering superior sensitivity while preserving specificity. The proposed criteria are clinically interpretable, highlight the novelty of combining two electrical spectra, and hold potential to impact routine diagnostic practice.