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 and affordability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging-based risk biomarkers, and its association with CTRCD. Methods: Across five hospitals of a 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), as well as a (negative) control cohort of patients 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 the association between the model's predictions and: i) global longitudinal strain (GLS) measurements within 15 days; ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction (LVEF) decrease to <50%), and LVEF decrease <40%. In a negative control analysis of patients receiving ICI, we correlated baseline AI-ECG LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with progressively worse GLS (-18% [IQR: -20 to -17%] for AI-ECG predictions <0.1, to -12% [-15 to -9%] for >=0.5 (p<0.001), n=7,271). 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 CTRCD (HRadjusted 2.22 [95%CI: 1.63-3.02]), and LVEF <40% (HRadjusted 4.76 [95%CI: 2.62-8.66], p<0.001), 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 risk (HRadjusted 1.36 [0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

Application of AI in detection of breast cancer with laboratory results monitoring

“…By leveraging these advanced technologies, researchers and healthcare providers can improve early detection and decision making, develop more precise diagnostic tools, and personalize treatment strategies for patients with breast cancer. This innovative approach has the potential to significantly impact patient outcomes and contribute to the advancement of breast cancer research and care [10][11][12].…”

“…Treatment options for breast cancer vary depending on the type and stage of cancer but it often includes surgery, chemotherapy, radiation therapy, and hormone therapy. Multidisciplinary approaches involving healthcare professionals from various specialties are commonly used to provide comprehensive care for patients with breast cancer [10][11][12].…”

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment

“…Recent studies also suggest that the AI-ECG signature for LVSD identifies other subtle markers of LV dysfunction, including abnormal LV strain and diastolic function, especially among those with a positive screen but preserved LVEF. 26 Of note, each of these is known to be associated with elevated HF risk. 27 Given the increasing accessibility of single-lead ECGs, we tested the hypothesis that an AI model developed to detect the cross-sectional signature of LVSD from single-lead ECG tracings can identify the risk of developing HF.…”

“…Recent studies also suggest that the AI-ECG signature for LVSD identifies other subtle markers of LV dysfunction, including abnormal LV strain and diastolic function, especially among those with a positive screen but preserved LVEF. 26 Of note, each of these is known to be associated with elevated HF risk. 27…”