Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy Academic Article in Scopus uri icon

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. © 2025 De la Garza-Salazar, Egenriether. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

publication date

  • October 1, 2025