Scholarlink

<p>Whether artificial intelligence (AI) analysis of single-lead ECG (1 L ECG) can predict incident AF is unknown. In the VITAL-AF trial (ClinicalTrials.gov NCT03515057, registered 2/24/2021) of primary care patients aged ≥65 years undergoing handheld 1 L ECG screening, we tested three AI approaches to incident AF prediction, and compared the best model to the CHARGE-AF risk score. In a test set of 4,221 individuals, a published AI model trained using single standard ECG leads (“1 L ECG-AI”) provided similar 2-year AF discrimination to models trained with VITAL-AF data. In the full VITAL-AF sample of 15,694 individuals without prevalent AF (2-year incident AF 3.1%), 1 L ECG-AI with age/sex (1 L ECG-AI AS) had comparable discrimination (area under the receiver operating characteristic curve [AUROC] 0.695[0.637–0.742]; average precision [AP] 0.060[0.050–0.078]) to CHARGE-AF (AUROC 0.679[0.623-0.730]; AP 0.062[0.052–0.080], AUROC <i>p</i> = 0.46, AP <i>p</i> = 0.92). Net reclassification improvement was favorable versus age ≥65 years (0.27[0.22–0.32]). 1 L ECG-AI may increase efficiency and reach of AF screening.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

查看全文
  • Shaan Khurshid,
  • Sam F. Friedman,
  • Mostafa A. Al-Alusi,
  • Shinwan Kany,
  • Thomas Sommers,
  • Christopher D. Anderson,
  • Jennifer E. Ho,
  • David D. McManus,
  • Leila H. Borowsky,
  • Jeffrey M. Ashburner,
  • Steven A. Lubitz,
  • Steven J. Atlas,
  • Mahnaz Maddah,
  • Daniel E. Singer,
  • Patrick T. Ellinor

摘要

Whether artificial intelligence (AI) analysis of single-lead ECG (1 L ECG) can predict incident AF is unknown. In the VITAL-AF trial (ClinicalTrials.gov NCT03515057, registered 2/24/2021) of primary care patients aged ≥65 years undergoing handheld 1 L ECG screening, we tested three AI approaches to incident AF prediction, and compared the best model to the CHARGE-AF risk score. In a test set of 4,221 individuals, a published AI model trained using single standard ECG leads (“1 L ECG-AI”) provided similar 2-year AF discrimination to models trained with VITAL-AF data. In the full VITAL-AF sample of 15,694 individuals without prevalent AF (2-year incident AF 3.1%), 1 L ECG-AI with age/sex (1 L ECG-AI AS) had comparable discrimination (area under the receiver operating characteristic curve [AUROC] 0.695[0.637–0.742]; average precision [AP] 0.060[0.050–0.078]) to CHARGE-AF (AUROC 0.679[0.623-0.730]; AP 0.062[0.052–0.080], AUROC p = 0.46, AP p = 0.92). Net reclassification improvement was favorable versus age ≥65 years (0.27[0.22–0.32]). 1 L ECG-AI may increase efficiency and reach of AF screening.