A prediction model based on platelet parameters, lipid levels, and angiographic characteristics to predict in-stent restenosis in coronary artery disease patients implanted with drug-eluting stents.

BACKGROUND The present study was aimed to establish a prediction model for in-stent restenosis (ISR) in subjects who had undergone percutaneous coronary intervention (PCI) with drug-eluting stents (DESs). MATERIALS AND METHODS A retrospective cohort study was conducted. From September 2010 to September 2013, we included 968 subjects who had received coronary follow-up angiography after primary PCI. The logistic regression analysis, receiver operator characteristic (ROC) analysis, nomogram analysis, Hosmer-Lemeshow χ2 statistic, and calibration curve were applied to build and evaluate the prediction model. RESULTS Fifty-six patients (5.79%) occurred ISR. The platelet distribution width (PDW), total cholesterol (TC), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), and lesion vessels had significant differences between ISR and non-ISR groups (all P   0.5 and P < 0.05) to establish a prediction model. The prediction model showed a good value of area under curve (AUC) (95%CI): 0.72 (0.64-0.80), and its optimized cut-off was 6.39 with 71% sensitivity and 65% specificity to predict ISR. CONCLUSION The incidence of ISR is 5.79% in CAD patients with DES implantation in the Xinjiang population, China. The prediction model based on PDW, SBP, TC, LDL-C, and lesion vessels was an effective model to predict ISR in CAD patients with DESs implantation.