A Risk Prediction Model for Cardiac Rehabilitation in Patients with Stable Coronary Artery Disease / 中国康复理论与实践

ABSTRACT

Objective:To create a prediction model that could be used to stratify the risk of cardiac rehabilitation in patients with stable coronary artery disease by using test data based on cardiopulmonary exercise testing (CPET) and general clinical data. Methods:A total of 114 patients with stable coronary artery disease were consecutively enrolled from the Cardiology Coronary Artery Disease Database of our hospital from December, 2014 to December, 2018, all the patients underwent CPET before coronary angiography. LASSO was used for feature selection. A nomogram was formulated based on the results of multivariate Logistic regression analysis using the RMS package of R. The predictive power was assessed with Receiver Operating Characteristic Curve. Results:Seven predictors were identified based on LASSO coronary angiography results, the maximum value of ventilatory equivalent for carbon dioxide (EqCO2max), lymphocyte count, fasting blood glucose levels, cardiac muscle enzyme positivity, blood homocysteine and blood urea nitrogen levels. Combined with clinical experience and weighting analysis, the final four factors were included for Logistic regression modeling coronary angiography results, EqCO2max, lymphocyte count and fasting blood glucose levels. The area under the curve was 0.875 for the model. Conclusion:EqCO2max and lymphocyte count are key predictors for stable coronary heart disease and can be used to identify patients at high risk for cardiac rehabilitation. A risk stratification model based on CPET and laboratory tests can be used to assess risk stratification for cardiac rehabilitation in patients with stable coronary artery disease.