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Updated: Aug 5, 2026
Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model
Published on: September 14, 2009
Development of a Risk Stratification Model for Coronary In-Stent Restenosis Based on Clinical, Laboratory, and
Natalya Zemlyanskaya1, Viktor Zemlyanskiy2, Marat Aripov3
1Department of General Medical Practice with a Course of Evidence-Based Medicine, NJSC "Astana Medical University", Astana 010000, Kazakhstan.
Insights
A new risk model identifies predictors of coronary in-stent restenosis (ISR) after percutaneous coronary intervention (PCI). This tool helps stratify patient risk using clinical, laboratory, and procedural factors for better long-term outcomes.
Area of Science:
- Cardiology
- Medical Technology
- Public Health
Background:
- Coronary in-stent restenosis (ISR) is a significant complication of drug-eluting stent (DES) placement during percutaneous coronary intervention (PCI), impacting long-term patient prognosis.
- Existing prediction models for ISR often utilize invasive procedural data and are primarily developed in high-income populations, limiting their broad applicability.
- There is a need for a generalized risk stratification model for ISR based on routinely accessible patient information.
Purpose of the Study:
- To identify independent predictors of coronary ISR in a diverse patient cohort from Kazakhstan.
- To develop and internally validate a clinically applicable risk stratification model for ISR.
- To utilize routinely available clinical, laboratory, and procedural factors for risk assessment.
Main Methods:
- A retrospective case-control study involving 910 patients who underwent coronary angiography post-PCI.
- Comparison of 455 patients with confirmed ISR against 455 patients without ISR.
- Multivariable binary logistic regression for identifying independent predictors; ROC analysis and bootstrap resampling for model validation.
Main Results:
- Independent predictors of ISR included prior myocardial infarction (MI), type 2 diabetes mellitus (T2DM), and smoking.
- Patients with ISR exhibited poorer inflammatory and metabolic profiles (higher NLR, MHR, atherogenic index, TyG index) and lower LVEF.
- A developed risk model incorporating T2DM, stent number, MPV, neutrophil count, HDL-C, and LVEF showed good discrimination (AUC 0.828) and 74.4% accuracy.
Conclusions:
- The developed risk stratification model demonstrates good discrimination and satisfactory internal validity.
- The model serves as a potentially valuable tool for assessing patient risk of ISR following PCI.
- External validation in independent cohorts is recommended prior to widespread clinical implementation.
Abstract:
Background: Coronary in-stent restenosis (ISR) remains a major limitation of percutaneous coronary intervention (PCI) with drug-eluting stents (DES), adversely affecting long-term outcomes. Most available prediction models rely on invasive procedural variables and have been developed predominantly in high-income populations, limiting their generalizability. This study aimed to identify independent predictors of coronary ISR and to develop and internally validate a clinically applicable risk stratification model based on routinely available clinical, laboratory, and procedural factors in a cohort of patients from Kazakhstan. Methods: In this retrospective case-control study, 910 patients with coronary artery disease (CAD) who underwent follow-up coronary angiography after PCI between January 2018 and July 2025 were included. The study comprised 455 patients with angiographically confirmed coronary in-stent restenosis and 455 patients without restenosis selected using a consecutive sampling approach. Clinical characteristics, laboratory parameters, echocardiographic findings, and angiographic data were analyzed. Independent predictors were identified using multivariable binary logistic regression. Model discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and internal validation was performed using bootstrap resampling. Results: The mean age was 62.9 ± 8.9 years, and 75.2% of patients were male. Restenosis was independently associated with prior myocardial infarction (MI) (OR 2.20; 95% CI 1.65-2.80), type 2 diabetes mellitus (T2DM) (OR 2.60; 95% CI 1.93-3.47), and smoking (OR 1.40; 95% CI 1.01-1.89). Patients with restenosis demonstrated a less favorable inflammatory and metabolic profile, including higher NLR, MHR, atherogenic index, and TyG index (all p < 0.05). LVEF was significantly lower, while multivessel disease and the number of implanted stents was higher (p < 0.001). A risk stratification model incorporating T2DM, the number of implanted stents, MPV, neutrophil count, HDL-C, LVEF demonstrated good discrimination (AUC 0.828) and 74.4% accuracy. Conclusions: The proposed model demonstrated good discrimination and satisfactory internal validity with limited optimism after internal bootstrap validation. It may serve as a useful tool for patient risk stratification after PCI. External validation in independent cohorts is required before widespread clinical implementation.
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