A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Source: npj Digital Medicine
Institutions: Broad Institute, Massachusetts General Hospital, Massachusetts Institute of Technology, University Medical Center Hamburg-Eppendorf, University of Helsinki, Institute for Molecular Medicine Finland, HiLIFE – Elämäntieteiden Instituutti, University of California, San Francisco, Beth Israel Deaconess Medical Center
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Awards: G1062825794, G1189786236, G1445764998, G1723146954, G1742652086, G2581677850, G3105548227, G3553811666, Genomics and Epigenetics of Atrial Fibrillation, Eicosanoid Profiles as Determinants of HFpEF, G3897532131, G426421887, G4405986302, G4411418652, G475755283, G5003834007, Deep learning von Echokardiographie zur Risikoprädiktion von Vorhofflimmern, Using Electrocardiogram Genetics to Inform Arrhythmia Risk, Advancing Atrial Fibrillation Precision Medicine, Integrated, Multidisciplinary Training in Cardiovascular Research, Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, G733687432, G7389137789, Mentoring in Patient-Oriented and Translational HFpEF Research, G8767518440, Deep Learning to Identify Digital Biomarkers of Cardiovascular Disease, G9000002183, G9000017272, G9000058572, G9000068663, G9000143380, G9000146605, G9000208878, G9000265122, Long-Term Endothelial Effects of COVID-19 in Obesity
OpenAlex ID: W4407855620