Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

ABSTRACT Background Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. Objectives To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD. Methods Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days ( n =7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction LVEF<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis. Results Higher AI-ECG LVSD predictions were associated with worse GLS (−18% IQR:-20 to −17% for predictions<0.1, to −12% IQR:-15 to −9% for ≥0.5 ( p <0.001)). In 1,308 patients receiving AC/TZM (age 59 IQR:49-67 years, 999 76.4% women, 80 IQR:42-115 follow-up months) a positive baseline AI-ECG LVSD screen was associated with ∼2-fold and ∼4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 95%CI:1.63-3.02), and LVEF<40% (adj.HR 4.76 95%CI:2.62-8.66), respectively. Among 2,056 patients receiving ICI (age 65 IQR:57-73 years, 913 44.4% women, follow-up 63 IQR:28-99 months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 95%CI:0.47-3.93). Conclusion AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure. CONDENSED ABSTRACT There is an unmet need for scalable and affordable biomarkers to stratify the risk of cancer therapeutics-related cardiac dysfunction (CTRCD). In this hospital system-based, decade-long cohort of patients without cardiomyopathy receiving anthracyclines or trastuzumab, a validated artificial intelligence algorithm applied to baseline electrocardiographic (AI-ECG) images identified individuals with a 2-fold and 4.8-fold risk of developing any cardiomyopathy or left ventricular ejection fraction <40%, respectively. This supports a role for AI-ECG interpretation of images as a scalable approach for the baseline risk stratification of patients initiating cardiotoxic chemotherapy.