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Updated: Sep 9, 2026
Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model
Published on: September 14, 2009
In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach
Alexandru Scafa-Udriște1,2, Lucian Itu3,4, Andrei Puiu3,4
1Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Insights
Machine learning models can predict in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) in acute coronary syndrome (ACS) patients. The Random Forest model showed the best performance, identifying key predictors like the number of affected arteries and stent characteristics.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- In-stent restenosis (ISR) is a complication after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS).
- Identifying independent predictors of ISR remains challenging despite previous studies.
- Machine learning (ML) offers a novel approach to predict ISR risk.
Purpose of the Study:
- To evaluate the relationship between ISR and ACS risk factors.
- To develop and validate a machine learning-based nomogram for predicting ISR probability post-PCI.
- To identify independent predictors of ISR using ML techniques.
Main Methods:
- A cohort of 340 ACS patients with successful PCI and angiographic follow-up was analyzed.
- Four ML techniques were explored: Random Forest (RF), Support Vector Machines (SVM), Logistic Linear Regression (LLR), and Deep Neural Network (DNN).
- Twenty-one demographic, clinical, and peri-procedural features were used as input variables.
Main Results:
- The overall incidence of ISR was 17.68% (87/340 patients).
- The Random Forest model demonstrated the highest predictive performance with an Area Under the ROC Curve of 0.726.
- Statistically significant predictors of ISR included the number of affected arteries (≥2), stent generation, and stent diameter.
Conclusions:
- Machine learning models can effectively differentiate future ISR risk in patients post-PCI.
- The developed ML approach aids in better risk stratification for ISR.
- Predictors such as the number of affected arteries, stent generation, and diameter are crucial for ISR prediction.
Background:
In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML).
Objectives:
The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI).
Methods:
Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables.
Results:
The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter.
Conclusion:
ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.
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