Predicting Long-term Mortality in Patients with Stable Angina Across the Spectrum of Dysglycemia: A Machine Learning Approach

Abstract Background: We aimed to develop and validate a model to predict mortality in patients with stable angina across the spectrum of dysglycemia.Methods: A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality was followed up and served as the primary endpoint. We compared the performance of different machine learning models for survival analysis and used least absolute shrinkage and selection operator (LASSO) to select important features. Performance was evaluated using Harrell’s C-index and the Brier score. The models were validated with five-fold cross validation to predict long-term mortality.Results: The features selected by LASSO were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), use of angiotensin II receptor blockers, use of diuretics, and smoking history. The best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, P < 0.001).Conclusions: We developed a machine learning model to predict long-term mortality among patients with stable angina. With the integration of OGTT, the model could help to identify patients with stable angina at high risk of mortality across the spectrum of dysglycemia.