Abstract 18324: Artificial Intelligence ECG as a Novel Method to Detect Cardiotoxicity After Anthracyclines

Abstract

Introduction: Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (LVEF) on cardiac imaging. Cost-effective alternatives to cardiac imaging are currently not available. Artificial intelligence (AI) models have been developed to detect a reduced LVEF from 12-lead electrocardiograms (ECGs) in the general population. Hypothesis: AI-ECG model can detect a reduced LVEF after anthracycline therapy (i.e. cardiotoxicity). Methods: We included an unbiased oncological population of 989 female breast cancer patients receiving (neo)adjuvant anthracycline chemotherapy. Average follow-up time was 9.83±4.2 years. Patients with incomplete transthoracic echocardiography (TTE) data, HER-2-directed therapy, significant comorbidities, upfront metastatic disease, second primary malignancy or pre-existing cardiovascular disease were excluded from the analyses. Patients with LVEF decline for reasons other than anthracycline-induced cardiotoxicity were also excluded. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for various levels of LVEF <50%. Results: Using 1,877 ECGs from 703 unique patients, the AI-ECG model detected an LVEF <50% and ≤35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. Conclusions: These data support the use of AI-ECG for cardiomyopathy (cardiotoxicity) screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly testing and could enable patients to monitor themselves over long periods of time.