000165053 001__ 165053
000165053 005__ 20251204150239.0
000165053 0247_ $$2doi$$a10.1097/HJH.0000000000004034
000165053 0248_ $$2sideral$$a146454
000165053 037__ $$aART-2025-146454
000165053 041__ $$aeng
000165053 100__ $$aNaderi, Hafiz
000165053 245__ $$aDiagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learning
000165053 260__ $$c2025
000165053 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165053 5203_ $$aObjective: 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.
000165053 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I
000165053 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000165053 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165053 700__ $$0(orcid)0000-0003-4130-5866$$aRamírez, Julia$$uUniversidad de Zaragoza
000165053 700__ $$aVan Duijvenboden, Stefan
000165053 700__ $$aRuiz Pujadas, Esmeralda
000165053 700__ $$aAung, Nay
000165053 700__ $$aWang, Lin
000165053 700__ $$aChamling, Bishwas
000165053 700__ $$aDörr, Marcus
000165053 700__ $$aMarkus, Marcello R.P.
000165053 700__ $$aChahal, Choudhary Anwar A.
000165053 700__ $$aLekadir, Karim
000165053 700__ $$aPetersen, Steffen E.
000165053 700__ $$aMunroe, Patricia B.
000165053 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000165053 773__ $$g43, 8 (2025), 1327-1338$$pJ. hypertens.$$tJournal of hypertension$$x0263-6352
000165053 8564_ $$s2131497$$uhttps://zaguan.unizar.es/record/165053/files/texto_completo.pdf$$yVersión publicada
000165053 8564_ $$s2968518$$uhttps://zaguan.unizar.es/record/165053/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165053 909CO $$ooai:zaguan.unizar.es:165053$$particulos$$pdriver
000165053 951__ $$a2025-12-04-14:40:07
000165053 980__ $$aARTICLE