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

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Abstract
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 < 0.05). And these variables were independently associated with ISR (all P < 0.05). Furthermore, they were identified as predictors (all AUC > 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.
Funding Information
  • State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (SKL-HIDCA-2020-47)
  • National Natural Science Foundation of China (81560071; U1903304)
  • training program of national science foundation for distinguished young scholar (xyd2021J004)
  • the third Training Program of Tianshan Talents of Xinjiang Department of Human Resources and Social Security (No.37 [2021])

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