In this study, we built a machine learning based model to predict all-cause mortality among patients with stable angina across the spectrum of dysglycemia. With glucose indices obtained from OGTT and other available clinical data, this model showed good discrimination and accuracy in predicting long-term mortality after coronary angiography. To the best of our knowledge, this study is among the first to compare state-of-the-art machine learning methods to predict survival in patients with stable angina, with an emphasis on OGTT results as important parameters.
For patients with obstructive CAD, several predictive models have been developed to predict major cardiovascular events and mortality. For example, the GRACE discharge score has been recently validated its accuracy to predict mortality 2 years after coronary angiography with an area under curve of 0.61 for patients with stable CAD . The ABC-CHD score  with risk factors identified by the Cox proportional hazards model, including age, biomarkers (N-terminal prohormone of brain natriuretic peptide and troponin-T), and clinical histories (smoking, diabetes, and presence of peripheral artery disease), has good discriminatory ability (Harrell’s C-index: 0.71) and calibration for three-year mortality. However, previous models seldom include patients with non-obstructive CAD. It has been reported that more than half of patients with angina have no obstructive CAD during coronary angiography [28–30]. Since a sizable proportion of patients have angina has non-obstructive CAD, our model, which was derived from a cohort in which almost 50% of patients had non-obstructive CAD, is more representative of real-world patients with stable angina. Even for patients with obstructive CAD, our model outperformed the GRACE discharge score to predict long-term mortality.
The predictors detected by our LASSO-derived Cox proportional hazards models were age, diuretic use, ARB use, heart rate at admission, OGTT 120 min, and OGTT 30 min. Although some are well-established risk factors for mortality and have been included in previous predictive models for patients with CAD, using OGTT results as parameters for risk stratification has not been investigated before. Our study showed high accuracy in mortality prediction with the integration of OGTT results. According to the EUROASPIRE study, OGTT 120 min is a predictor of major cardiovascular events and mortality for patients without diabetes . Chattopadhyay et al.  showed that with the adjustment of OGTT 120 min, GRACE discharge score has an improved prognostic ability for patients with acute coronary syndrome. Similar to above studies, our model containing OGTT 120 min outperformed the GRACE discharge score for predicting mortality among patients with stable angina, supporting the importance of OGTT 120 min for mortality prediction among patients with ischemic heart disease. Our model also revealed potentially unidentified predictors, such as OGTT 30 min. OGTT 30 min has predictive value for developing type 2 diabetes and is associated with inflammatory markers [33, 34]; however, its role in mortality prediction has not been previously evaluated. Based on the Shapley value derived from our model, OGTT 30 min also contributed to mortality prediction in patients with stable angina. Further prospective studies are warranted to elucidate the prognostic value of OGTT 30 min.
There are several clinical applications of this model. Our model is derived from patients with stable angina. More than half of patients with stable angina have non-obstructive CAD, and approximately 20%~40% of patients with CAD still suffer from angina symptoms after revascularization . There is heterogeneity in prognosis among patients with stable angina, and it is important to stratify their risk and tailor their management strategy. However, contemporary clinical practice mainly focuses on prevention and management of obstructive CAD , despite the fact that the risk of major cardiovascular events and mortality among patients with non-obstructive CAD is increased . Our model could help to identify patients with stable angina at a high risk of mortality. In addition, our model emphasized the importance of OGTT. Screening for dysglycemia using OGTT in patients undergoing percutaneous coronary intervention has long been proposed and is also recommended in European Society of Cardiology guidelines [36, 37]. However, adhesion to this recommendation is poor , partially because the prognostic role of OGTT is less clear. Our model, which adds prognostic value to the OGTT for patients with stable angina, could increase adhesion to this recommendation.
The major strength of this study is that we used the LASSO-derived Cox proportional hazards model for feature selection and advanced machine learning methods for model development. Only six variables were needed in our model after utilization of LASSO regularization, and most of them, except OGTT, were available from electronic health records, making it a convenient tool to implement in clinical practice. The best performing machine method in our study was RSF. Previous predictive models for CAD were usually built using Cox proportional hazards models; however, several assumptions must be met before applying the Cox proportional hazards model. Conversely, machine learning methods, such as RSF, can handle non-linear, complex relationships between features without assumptions, thus widening their clinical application. However, there are still some limitations which should be highlighted in the current study. First, this cohort has been conducted since 2009, and contemporary anti-diabetic medications, such as sodium glucose co-transporters 2 inhibitors and glucagon-like peptide-1 receptor agonists, which can reduce mortality risk in patients with type 2 diabetes, were seldom been prescribed. Second, our model has not been externally validated with other independent datasets, so its performance in other datasets is unknown.