Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms

Ryuichiro Yagi, Shinichi Goto, Yukihiro Himeno, Yoshinori Katsumata, Masahiro Hashimoto, Calum A. MacRae and Rahul C. Deo ()
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Ryuichiro Yagi: Brigham and Women’s Hospital
Shinichi Goto: Brigham and Women’s Hospital
Yukihiro Himeno: Keio University School of Medicine
Yoshinori Katsumata: Keio University School of Medicine
Masahiro Hashimoto: Keio University School of Medicine
Calum A. MacRae: Brigham and Women’s Hospital
Rahul C. Deo: Brigham and Women’s Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model’s insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73–4.10; log-rank p

Date: 2024
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DOI: 10.1038/s41467-024-45733-x

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