Clinics (Sao Paulo). 2026 Jan 8;81:100856. doi: 10.1016/j.clinsp.2025.100856. Online ahead of print.
ABSTRACT
OBJECTIVE: To assess whether electrocardiographic markers of Global Electrical Heterogeneity (GEH) improve the identification of patients requiring tertiary care, either alone or combined with an explainable machine learning model, compared with standard ECG features and clinical risk factors in a real-world tertiary cardiology population.
METHODS: Patients were forwarded to a specific evaluation in a cardiology-specialized hospital performed an ECG and data collection. A series of follow-up attendances occurred in periods of 6-months, 12-months and 15-months to check for cardiovascular-related events (mortality or new nonfatal cardiovascular events (Stroke, MI, PCI, CS), as identified during 1-year phone follow-ups. The first attendance ECG was measured by a specialist and processed in order to obtain the Global Electric Heterogeneity (GEH) using the Kors Matriz. The ECG measurements, GEH parameters, and risk factors were combined for training multiple instances of XGBoost decision tree models. Each instance was optimized for the AUCPR, and the instance with the highest AUC was chosen as representative of the model. The importance of each parameter for the winner tree model was compared to better understand the improvement from using GEH parameters.
RESULTS: GEH parameters were statistically significant in this population (p < 0.001), particularly the QRST angle and SVG magnitude. The combined model integrating GEH, standard ECG features, and clinical risk factors achieved the best performance, with a sensitivity of 94.1 %, specificity of 30.8 %, AUC of 67.6 %, and F2 score of 0.62. SVG feature importance and SHAP analyses were consistent with the statistical findings, indicating that the model’s decision patterns align with clinically relevant information and reinforce the role of GEH features. The modeling approach was carefully designed to prevent overfitting, ensure generalizability, and facilitate implementation through its decision tree architecture.
CONCLUSION: VCG-derived features may improve the identification of patients requiring tertiary care, either alone or integrated into an explainable and robust machine learning model trained on real-world data. Its clinical value will ultimately depend on prospective validation and seamless integration within existing care pathways.
PMID:41512369 | DOI:10.1016/j.clinsp.2025.100856
