Prediction Model for in-Stent Restenosis Post-PCI Based on Boruta Algorithm and Deep Learning: The Role of Blood Cholesterol and Lymphocyte Ratio.

Abstract

Background: Percutaneous coronary intervention (PCI) is the primary treatment for acute myocardial infarction (AMI). However, in-stent restenosis (ISR) remains a significant limitation to the efficacy of PCI. The cholesterol-to-lymphocyte ratio (CLR), a novel biomarker associated with inflammation and dyslipidemia, may have predictive value for ISR. Deep learning-based models, such as the multilayer perceptron (MLP), can aid in establishing predictive models for ISR using CLR.

Methods: A retrospective analysis was conducted on clinical and laboratory data from 1967 patients. The Boruta algorithm was employed to identify key features associated with ISR. An MLP model was developed and divided into training and validation sets. Model performance was evaluated using ROC curves and calibration plots.

Results: Patients in the ISR group exhibited significantly higher levels of CLR and low-density lipoprotein (LDL) compared to the non-ISR group. The Boruta algorithm identified 21 important features for subsequent modeling. The MLP model achieved an AUC of 0.95 on the validation set and 0.63 on the test set, indicating good predictive performance. Calibration plots demonstrated good agreement between predicted and observed outcomes. Feature importance analysis revealed that the number of initial stent implants, hemoglobin levels, Gensini score, CLR, and white blood cell count were significant predictors of ISR. Partial dependence plots (PDP) confirmed CLR as a key predictor for ISR.

Conclusion: The CLR, as a biomarker that integrates lipid metabolism and inflammation, shows significant potential in predicting coronary ISR. The MLP model, based on deep learning, demonstrated robust predictive capabilities, offering new insights and strategies for clinical decision-making.