206 Artificial intelligence–enabled electrocardiography for hypertension diagnosis

Background Hypertension underpins significant global morbidity and mortality. Hypertension-mediated organ damage is an early indicator of serious cardiovascular sequelae, with multinational guidelines advocating aggressive treatment where detected. Artificial intelligence-enabled electrocardiogram (AI-ECG) offers novel strategies to facilitate personalised hypertensive risk assessments, early intervention, and remote disease monitoring in hypertension care. Purpose We aimed to develop and explore an AI-ECG model designed to diagnose hypertension from 12 lead ECGs. Methods We developed a deep neural network AI-ECG model, AI Risk Estimator for hypertension diagnosis (AIRE-HTN) from 12 lead ECGs. The model uses a residual network with sigmoid classification layer for prevalent hypertension diagnosis. It was derived from 1,163,401 ECGs from a real-world secondary care setting in the USA. Model development took 60% of ECGs (50/10% train/test split, n = 113,723 unique patients) and internally validated on 40% (n = 75,815 unique patients). The results were externally validated in the UK Biobank (UKB) volunteer cohort (n = 65,610 participants). Results AIRE-HTN demonstrates state-of-the-art performance in identifying existing hypertension (area under receiver operating characteristic curve (AUROC): USA cohort 0.77 (0.77–0.78), UKB: 0.71 (0.70–0.71)). AIRE-HTN works in the absence of both structural left ventricular hypertrophy (LVH) by echocardiography (AUROC 0.78 (0.77–0.80)), electrophysiological LVH (Sokolow <3.5 mV, AUROC 0.78 (0.77–0.80)) and in those with cardiologist-reported normal ECGs (AUROC 0.78 (0.77–0.80). Conclusion We have developed AIRE-HTN, the first large scale AI-ECG model for hypertension diagnosis from standard 12 Lead ECGs. It operates effectively even in the absence of both structural and electrophysiological LVH, as well as in cases with cardiologist-reported normal ECGs. This suggests the morphological ECG phenotype the model derives as related to hypertension is beyond conventional human understanding of structural adaptation to hypertension. These findings pose an opportunity to rethink cardiovascular hypertension-mediated organ damage. Further work is planned for hypertension diagnosis prediction and further in-depth AIRE-HTN model exploration. Conflict of Interest None