Abstract 14074: Machine Learning Applied on ECG Improves Cardiovascular Risk Prediction When Added to Coronary Artery Calcium Scoring in Patients With Cancer

Abstract

Introduction: Patients with cancer are at increased cardiovascular (CV) risk. Both Coronary Artery Calcium (CAC) and 12-lead ECG features predict CV risk in the general population. However, the impact of these measures has not been investigated in patients with cancer. Hypothesis: We hypothesized that machine learning-based analysis of automatically-extracted ECG features, when added to CAC, can improve CV risk stratification in patients with cancer. Methods: We analyzed 1825 cancer patients with at least 1 CV risk factor who underwent CAC scoring and a standard 12-ECG as part of the CLARIFY (ClinicalTrials.gov Identifier: NCT04075162) registry. Events were defined as myocardial infarction, revascularization, stroke or death. A total of 445 ECG features were automatically extracted using a commercially available software (ECG Muse, GE healthcare). A LASSO-Cox method was used with 10-fold cross validation to select the most important ECG features with a novel ECG score derived using individual feature coefficients. We evaluated the ability of the combined ECG score with age, sex, and CAC to prognosticate a CV event in comparison to a model that was limited to age, sex, and CAC alone. The models were compared using an ANOVA test. Results: Over a median follow-up of 14 months, 224 patients had an event. Using the LASSO-Cox model, 25 ECG features were selected. The novel ECG score was associated with CV events (HR 8.6, 95% CI: 5.1-14.3, P<0.001), which was not attenuated after adjusting for age, sex, and CACS (HR 7.3, 95% CI: 4.3-12.4, P<0.001). Addition of ECG score to a model including age, sex, and CAC improved discrimination (C-Index 0.63 vs 0.72, P=0.0001). Conclusions: A novel ML-based ECG risk score using automatically extracted ECG features can improve CV risk stratification when added to CAC and demographics in patients with cancer.