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<p>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], <i>p</i> &lt; 0.001), HF (2.26 [1.90-2.69], <i>p</i> &lt; 0.001), MI (1.87 [1.69-2.07], <i>p</i> &lt; 0.001), stroke (1.30 [1.18-1.44], <i>p</i> &lt; 0.001), and aortic dissection or rupture (1.69 [1.22-2.35], <i>p</i> &lt; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月22日

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  • Mostafa A. Al-Alusi,
  • Samuel F. Friedman,
  • Shinwan Kany,
  • Joel T. Rämö,
  • Daniel Pipilas,
  • Pulkit Singh,
  • Christopher Reeder,
  • Shaan Khurshid,
  • James P. Pirruccello,
  • Mahnaz Maddah,
  • Jennifer E. Ho,
  • Patrick T. Ellinor

摘要

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.