Does a deep learning model (HTN-AI) using 12-lead ECG waveforms accurately detect hypertension and stratify cardiovascular risk in adults?
A deep learning model applied to standard 12-lead ECGs can accurately detect hypertension and independently predict incident cardiovascular events, serving as a novel digital biomarker for risk stratification.
Hypertension is a major risk factor for cardiovascular disease (CVD), yet blood pressure is measured intermittently and under suboptimal conditions. We developed a deep learning model to identify hypertension and stratify risk of CVD using 12-lead electrocardiogram waveforms. HTN-AI was trained to detect hypertension using 752,415 electrocardiograms from 103,405 adults at Massachusetts General Hospital. We externally validated HTN-AI and demonstrated associations between HTN-AI risk and incident CVD in 56,760 adults at Brigham and Women's Hospital. HTN-AI accurately discriminated hypertension (internal and external validation AUROC 0.803 and 0.771, respectively). In Fine-Gray regression analyses model-predicted probability of hypertension was associated with mortality (hazard ratio per standard deviation: 1.47 1.36-1.60, p < 0.001), HF (2.26 1.90-2.69, p < 0.001), MI (1.87 1.69-2.07, p < 0.001), stroke (1.30 1.18-1.44, p < 0.001), and aortic dissection or rupture (1.69 1.22-2.35, p < 0.001) after adjustment for demographics and risk factors. HTN-AI may facilitate diagnosis of hypertension and serve as a digital biomarker of hypertension-associated CVD.
Al‐Alusi et al. (Sat,) studied this question.