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

Area of Science:

• Cardiology
• Vascular Biology
• Clinical Risk Prediction

Background:

• In-stent restenosis (ISR) poses a significant clinical challenge following coronary stent implantation in patients with coronary artery disease (CAD).
• Identifying independent risk factors for ISR is crucial for developing effective preventive strategies.
• Predictive models can aid clinicians in managing patients at high risk for ISR.

Purpose of the Study:

• To identify independent risk factors associated with in-stent restenosis (ISR) after percutaneous coronary intervention (PCI).
• To develop and validate a predictive scoring model for ISR based on identified risk factors.
• To assess the clinical utility of the developed scoring model in predicting ISR.

Main Methods:

• A retrospective study involving 256 patients with CAD who underwent PCI between January 2017 and December 2018.
• Logistic regression analysis was employed to determine independent predictors of ISR.
• A scoring model was developed and its predictive performance evaluated using Receiver Operating Characteristic (ROC) curve analysis.

Main Results:

• Elevated levels of LDL-C, D-dimer, HbA1c, and uric acid were significantly higher in the ISR group compared to the non-ISR group.
• Independent risk factors for ISR included elevated LDL-C, D-dimer, HbA1c, post-procedural smoking, and poor blood pressure control.
• The developed ISR scoring model demonstrated good predictive performance with an AUC of 0.8643, 85.8% sensitivity, and 73.9% specificity.

Conclusions:

• Elevated LDL-C, D-dimer, HbA1c, post-procedural smoking, poor blood pressure control, and a family history of CAD are significant independent risk factors for ISR.
• The developed ISR scoring model serves as a practical tool for predicting ISR risk in patients undergoing PCI.
• This model can guide clinical management decisions to improve patient outcomes after coronary interventions.