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. 2024 Jul 5:17:1779-1786.
doi: 10.2147/RMHP.S468235. eCollection 2024.

Machine Learning-Based Prediction of In-Stent Restenosis Risk Using Systemic Inflammation Aggregation Index Following Coronary Stent Placement

Ling Hou #  1 , Jinbo Zhao #  2 , Ting He  2 , Ke Su  2 , Yuanhong Li  2
Affiliations

Machine Learning-Based Prediction of In-Stent Restenosis Risk Using Systemic Inflammation Aggregation Index Following Coronary Stent Placement

Ling Hou et al. Risk Manag Healthc Policy. .

Abstract

Introduction: Coronary artery disease (CAD) remains a significant global health challenge, with percutaneous coronary intervention (PCI) being a primary revascularization method. In-stent restenosis (ISR) post-PCI, although reduced, continues to impact patient outcomes. Inflammation and platelet activation play key roles in ISR development, emphasizing the need for accurate risk assessment tools. The systemic inflammation aggregation index (AISI) has shown promise in predicting adverse outcomes in various conditions but has not been studied in relation to ISR.

Methods: A retrospective observational study included 1712 patients post-drug-eluting stent (DES) implantation. Data collected encompassed demographics, medical history, medication use, laboratory parameters, and angiographic details. AISI, calculated from specific blood cell counts, was evaluated alongside other variables using machine learning models, including random forest, Xgboost, elastic networks, logistic regression, and multilayer perceptron. The optimal model was selected based on performance metrics and further interpreted using variable importance analysis and the SHAP method.

Results: Our study revealed that ISR occurred in 25.8% of patients, with a range of demographic and clinical factors influencing the risk of its development. The random forest model emerged as the most adept in predicting ISR, and AISI featured prominently among the top variables affecting ISR prediction. Notably, higher AISI values were positively correlated with an elevated probability of ISR occurrence. Comparative evaluation and visual analysis of model performance, the random forest model demonstrates high reliability in predicting ISR, with specific metrics including an AUC of 0.9569, accuracy of 0.911, sensitivity of 0.855, PPV of 0.81, and NPV of 0.948.

Conclusion: AISI demonstrated itself as a significant independent risk factor for ISR following DES implantation, with an escalation in AISI levels indicating a heightened risk of ISR occurrence.

Keywords: In-stent restenosis; Systemic inflammation aggregation index; coronary artery disease; machine learning models; percutaneous coronary intervention.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Flowchart of data screening and modeling.
Figure 2
Figure 2
ROC curve of the model.
Figure 3
Figure 3
Comparison of calibration curves of each model.
Figure 4
Figure 4
Importance plot of predictor variables.
Figure 5
Figure 5
Dot plot of predictor variable importance.
See this image and copyright information in PMC

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