Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses

Left ventricular hypertrophy (LVH) is a common condition with a prevalence of 15%-20% in general population. Prior studies have suggested that deep learning model (DLM)-enabled electrocardiogram (ECG) systems can aid LVH detection and cardiovascular risk assessment; however, conventional manual ECG criteria have limited sensitivity and their prognostic utility remains suboptimal. Therefore, this study aimed to develop a DLM-enabled ECG system to detect LVH and evaluate its prognostic associations with incident cardiovascular outcomes. A total of 40,736 patients from hospital A were used for model development (training and tuning) and internal validation (29,595/5,935/5,206 patients, respectively), and 6,271 patients from hospital B were used for external validation. LVH was defined by left ventricular mass index (LVMI) derived from echocardiography. Prognostic outcomes included new-onset acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AFib). In the external validation set, our AI-ECG-LVH model achieved area under the receiver operating characteristic curve (AUC) values of 0.82 in males and 0.77 in females. Furthermore, the hazard ratios for incident AMI, HF, and AFib were 2.67, 3.15, and 2.23 for AI-ECG-LVH, compared with 2.76, 3.78, and 2.25 for echocardiography-defined LVH (ECHO-LVH). Our AI-ECG-LVH model may provide a straightforward, affordable, and noninvasive approach for LVH screening and first-contact risk stratification.