Naderi Hafiz, Ramírez Julia, 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
William Harvey Research Institute, Queen Mary University of London, Charterhouse Square.
Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield.
J Hypertens. 2025 Aug 1;43(8):1327-1338. doi: 10.1097/HJH.0000000000004034. Epub 2025 May 23.
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.
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.
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).
ECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes.
利用心脏磁共振成像（CMR）已报道了四种高血压介导的左心室肥厚（LVH）表型：正常左心室、左心室重构、离心性和向心性LVH，其预后意义各不相同。心电图（ECG）常用于检测LVH；然而，其区分LVH表型的能力尚不清楚。本研究旨在利用机器学习从心电图中对高血压介导的LVH进行分类，并测试心电图预测的表型与心血管事件发生率之间的关联。
从英国生物银行（UKB）中20439例高血压患者的12导联心电图中提取心电图生物标志物。使用逻辑回归、支持向量机（SVM）和随机森林构建整合心电图和临床变量的分类模型。模型在80%的参与者中进行训练，其余20%组成测试集。在波美拉尼亚健康研究（SHIP）的877例高血压参与者中进行外部验证。在UKB测试集中，我们测试了心电图预测的LVH表型与主要不良心血管事件（MACE）和心力衰竭发生率之间的关联。
在UKB参与者中，19408例左心室正常，758例左心室重构，181例离心性LVH，92例向心性LVH。在UKB中，三种模型的分类性能相当。SVM（准确率0.79，灵敏度0.59，特异性0.87，AUC 0.69）被用于外部验证，在SHIP中得到了类似的结果。在两个队列中，离心性LVH的预测效果更好。在UKB测试集中，心电图预测的离心性LVH与心力衰竭相关（风险比3.42，95%CI 1.06-9.86）。
基于心电图的机器学习分类器代表了一种潜在的可及筛查策略，用于早期检测高血压介导的LVH表型。
