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

Alexandru Scafa-Udriște; Lucian Itu; Andrei Puiu; Andreea Stoian; Horatiu Moldovan; Nicoleta-Monica Popa-Fotea

doi:10.3389/fcvm.2023.1270986

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

Front Cardiovasc Med. 2023 Dec 22:10:1270986. doi: 10.3389/fcvm.2023.1270986. eCollection 2023.

Authors

Alexandru Scafa-Udriște^{1

2}, Lucian Itu^{3

4}, Andrei Puiu^{3

4}, Andreea Stoian^{3

4}, Horatiu Moldovan^{1

2}, Nicoleta-Monica Popa-Fotea^{1

2}

Affiliations

¹ Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
² Department of Cardiology, Emergency Clinical Hospital, Bucharest, Romania.
³ Department of Image Fusion and Analytics, Siemens SRL, Brasov, Romania.
⁴ Automation and Information Technology, Transilvania University of Brasov, Brasov, Romania.

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.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.