165053 20251204150239.0 doi 10.1097/HJH.0000000000004034 sideral 146454 ART-2025-146454 eng Naderi, Hafiz Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learning 2025 Objective: Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodelling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH; however, its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes. Methods: ECG biomarkers were extracted from the 12-lead ECG of 20 439 hypertensive patients in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM), and random forest. The models were trained in 80% of the participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensive participants from the Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure. Results: Among UKB participants 19 408 had normal LV, 758 LV remodelling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable in UKB. SVM (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) was taken forward for external validation with similar results in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (hazard ratio 3.42, 95% CI 1.06–9.86). Conclusion: ECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ramírez, Julia Universidad de Zaragoza (orcid)0000-0003-4130-5866 Van Duijvenboden, Stefan Ruiz Pujadas, Esmeralda Aung, Nay Wang, Lin Chamling, Bishwas Dörr, Marcus Markus, Marcello R.P. Chahal, Choudhary Anwar A. Lekadir, Karim Petersen, Steffen E. Munroe, Patricia B. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 43, 8 (2025), 1327-1338 J. hypertens. Journal of hypertension 0263-6352 2131497 http://zaguan.unizar.es/record/165053/files/texto_completo.pdf Versión publicada 2968518 http://zaguan.unizar.es/record/165053/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:165053 articulos driver 2025-12-04-14:40:07 ARTICLE