Abstract 13412: Diagnostic Performance of Machine Learning on 12-Lead Electrocardiogram for Predicting Multi-Vessel Coronary Vasospastic Angina

Abstract

Introduction: Multi-vessel coronary vasospastic angina (multi-VSA) is life-threatening disease. We tried to predict multi-VSA by machine learning (ML) on 12-lead electrocardiogram (ECG). Hypothesis: Machine learning on 12-lead ECG has powerful diagnostic value for multi-VSA. Methods: We recruited 227 consecutive sinus-rhythm patients (63.6±12.9years, 136men) who underwent acetylcholine-provocation test in coronary angiography (CAG). Multi-VSA was defined as spasm in at least 2 major branches. ECG was recorded before CAG in no chest pain period. ML was performed on table data of ECG parameters using several ensemble learning methods. Results: 79 patients (35%) showed multi-VSA, and univariate logistic regression analysis extracted 23 significant but weak predictors, the highest area under receiver operating characteristics curve (AUROC) was 0.673. Conversely, ML demonstrated high diagnostic performance (AUROC of extra trees classifier: 0.817). Shapley additive explanation method showed male, QTc, J wave in lead II, and low amplitude of Q wave in lead I/aVL played essential roles to build the ML model. Conclusion: Several parameters of 12-lead ECG in multi-VSA patients contains potential features of VSA, and their aggregation and ensemble learning can predict VSA with high diagnostic performance.