Predictive Scoring Model for In-Stent Restenosis Risk in Coronary Artery Disease Patients

BACKGROUND In-stent restenosis (ISR) after coronary stent implantation is a significant clinical challenge in patients with coronary artery disease (CAD). In this study we identified independent risk factors for ISR and developed a predictive scoring model based on these factors. MATERIAL AND METHODS We conducted a retrospective study of 256 CAD patients who underwent percutaneous coronary intervention (PCI) from January 2017 to December 2018. Based on follow-up angiography, patients were classified into ISR and non-ISR groups. Logistic regression analysis was used to identify independent predictors, and an ISR scoring model was developed. Receiver operating characteristic (ROC) curve analysis assessed predictive performance. RESULTS Compared to the non-ISR group, the ISR group had significantly higher levels of LDL-C, D-dimer, HbA1c, and uric acid (all P<0.01), along with higher rates of post-procedural smoking, poor blood pressure control, and family history of CAD. Logistic regression analysis identified elevated LDL-C (OR: 5.074), D-dimer (OR: 3.381), HbA1c (OR: 5.322), post-procedural smoking (OR: 4.364), and poor blood pressure control (OR: 5.168) as independent risk factors (all P<0.05). LDL-C showed the highest predictive value (AUC: 0.9289). The ISR scoring model achieved an AUC of 0.8643, with 85.8% sensitivity and 73.9% specificity at a cut-off of 3.0 points. CONCLUSIONS Elevated LDL-C, D-dimer, HbA1c, post-procedural smoking, poor blood pressure control, and a family history of CAD were independent risk factors for ISR. The ISR scoring model provides a practical tool for predicting ISR risk and guiding clinical management to improve outcomes in patients undergoing PCI.