A Simple Model to Predict Repeat Revascularization After Drug-Eluting Stent Implantation in Patients With Stable Coronary Artery Disease. (July 2022)

• Record Type: Journal Article
• Authors: Dai, Chunfeng; Yao, Zhifeng; Chen, Zhangwei; Qian, Juying; Ge, Junbo
• Is Part Of: Angiology. Volume 73:Number 6(2022)
• Page Start: 557
• Page End: 564
• Publication Date: 2022-07
• Subjects: myocardial revascularization -- risk factors -- nomograms -- stable coronary artery disease; Blood-vessels -- Diseases -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; 616.13005
• DOI: 10.1177/00033197211052133
• Languages: English
• ISSNs: 0003-3197

Abstract

Repeat revascularization is still common in the era of drug-eluting stents (DES), especially for non-target lesions. However, few validated models exist to predict the need for revascularization. We aimed to develop and validate an easy-to-use predictive model for repeat revascularization after DES implantation in patients with stable coronary artery disease (CAD). A total of 1, 653 stable CAD patients with angiographic follow-up after DES implantation were consecutively enrolled. Split-sample testing was adopted to develop and validate the model. In the training set, male, diabetes, number of target lesions, occlusion lesion, number of non-target lesions, recurrent angina, suboptimal low density lipoprotein-cholesterol level, and high lipoprotein (a) level were independent predictors of repeat revascularization using logistic regression analyses. The established model (Model 1) yielded a bias-corrected concordance index of 0.700 (95% confidence interval: 0.667 to 0.735), with good calibration. It also performed well in the validation set. Compared with the traditional empirical model only including recurrent angina (Model 2), Model 1 had better discriminative ability and clinical usefulness. In conclusion, we established and validated a simple model including 8 easily accessible variables to predict repeat revascularization after DES implantation in stable CAD patients, contributing to better risk stratification, decision making, and patient consultation.