A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-02-22
DOI
https://doi.org/10.1038/s41746-025-01491-8
Citations
10
Primary Topic
Heart Rate Variability and Autonomic Control
Type
article
Field-Weighted Citation Impact
13.88

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article

A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram

Jennifer E. Ho, Christopher Reeder, James P. Pirruccello, Shaan Khurshid et al.
10 citations
npj Digital Medicine
Heart Rate Variability and Autonomic Control
13.88
article

A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram

Jennifer E. Ho, Christopher Reeder, James P. Pirruccello, Shaan Khurshid, Joel Rämö, Mostafa A. Al‐Alusi, Sam Friedman, Patrick T. Ellinor, Shinwan Kany, Daniel Pipilas, Mahnaz Maddah, Pulkit Singh
article en
10 citations

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.

npj Digital MedicineVol. 8(1)
Broad Institute (US), Beth Israel Deaconess Medical Center (US), University of Helsinki (FI), Universität Hamburg (DE), University of California, San Francisco (US), Foundation Center (US), Massachusetts General Hospital (US), University Medical Center Hamburg-Eppendorf (DE), Institute for Molecular Medicine Finland (FI)
American Heart Association, European Commission, Deutsche Forschungsgemeinschaft, Sigrid Juséliuksen Säätiö, National Institutes of Health
Good health and well-being
Openalex Percentile: Top 1%
Heart Rate Variability and Autonomic Control
13.88
DOIOpen Access PDFOpenAlex
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