Abstract 15702: Leveraging Advanced AI Algorithms for Predicting Outcomes in Non-Acute Coronary Syndrome Chest Pain Patients

Abstract

Introduction: Chest pain is often encountered in emergency rooms and the detection of acute coronary syndrome (ACS) is a major focus. However, a notable percentage of patients present with a diverse range of nonACS conditions. Accurately identifying the causes and outcomes of these cases can prevent unnecessary interventions, reduce healthcare costs, and optimize resource allocation. This study aims to explore how advanced AI algorithms can enhance risk assessment, refine classification, and predict outcomes in nonACS chest pain patients using conventional ECG analysis. Methods: We studied 3458 nonACS patients referred to the Emergency Room at Instituto Dante Pazzanese de Cardiologia with chest pain. A total of 185 features, including sex, height, ECG diagnostic statements, and measures, were used. The predicted outcome was defined as hospitalization within 14 days and/or death (1 or 0). We employed the AutoGluon framework for feature engineering and early model selection. XGBoost, a tree-based model, was chosen as the architecture. Training and k-fold stratified cross-validation were performed using an oversampled balanced dataset, and evaluation metrics such as AUROC, specificity, and sensitivity were measured using the original data. Results: In this study, 18.2% (630 patients) had a positive outcome. The sex distribution was comparable between outcome groups, with men accounting for 57-58% and women for 42-43%. Significant differences (p<0.01) were observed in ECG intervals (QRS, corrected QT, RR interval, PSP) between the groups. The AI model identified important diagnostic statements, including normal ECG (19.4), atrial fibrillation (7.4), left ventricular hypertrophy (7.1), Ischemic T-wave inversion in inferior leads (6.7), T-wave changes in inferior leads (5.9), and first-degree atrioventricular block (5.8). The AI model performed exceptionally well, with a sensitivity of 97.93%, specificity of 96.08%, and an AUROC of 0.97. Conclusions: The AI model demonstrated its ability to predict outcomes in patients with acute chest pain without ACS, making it an appealing tool for effective risk stratification. The early identification provided by the AI model presents an opportunity for timely intervention to mitigate adverse outcomes.