In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach

Abstract

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.

Keywords: acute coronary syndrom(s); in-stent restenosis; machine learning algorithms; prediction; risk factors.

Figure 1

Schematic overview of the proposed analysis. The outer loop sets the fraction of data used and the inner loop randomly splits the dataset into a training and a testing set.

Figure 2

Machine learning performance as a function of the dataset size. y-axis shows the ROC-AUC while the x-axis shows the fraction of data used for training a DNN (blue), a LLR (orange), a SVM (green) and a RF (red) prediction model.