Development and Validation of a Predictive Model for Chronic Kidney Disease After Percutaneous Coronary Intervention in Chinese

Abstract

Aim There is no model for predicting the outcomes for coronary heart disease (CHD) patients with chronic kidney disease (CKD) after percutaneous coronary intervention (PCI). To develop and validate a model to predict major adverse cardiovascular events (MACEs) in patients with comorbid CKD and CHD undergoing PCI. Methods We enrolled 1714 consecutive CKD patients who underwent PCI from January 1, 2008 to December 31, 2017. In the development cohort, we used least absolute shrinkage and selection operator regression for data dimension reduction and feature selection. We used multivariable logistic regression analysis to develop the prediction model. Finally, we used an independent cohort to validate the model. The performance of the prediction model was evaluated with respect to discrimination, calibration, and clinical usefulness. Results The predictors included a positive family history of CHD, history of revascularization, ST segment changes, anemia, hyponatremia, transradial intervention, the number of diseased vessels, dose of contrast media >200 ml, and coronary collateral circulation. In the validation cohort, the model showed good discrimination (area under the receiver operating characteristic curve, 0.612; 95% confidence interval: 0.560, 0.664) and good calibration (Hosmer-Lemeshow test, P  =  0.444). Decision curve analysis demonstrated that the model was clinically useful. Conclusions We created a nomogram that predicts MACEs after PCI in CHD patients with CKD and may help improve the screening and treatment outcomes.