The Incidence and Possible Predictors of Coronary Restenosis

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

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.

Colchicine Use in Acute Coronary Syndromes: An Update

Coronary artery disease is the leading cause of death worldwide, and its main pathological substrate is represented by atherosclerosis. Inflammation is a major promoter of the atherosclerotic process and is involved in both the initiation and progression of atherosclerosis, as well as in the occurrence of fatal complications. Until the present moment, Colchicine Cardiovascular Outcomes Trial is the largest trial to demonstrate a major benefit of low-dose colchicine on major adverse cardiac events in patients with recent myocardial infarction (MI), but the mechanisms behind this relation are not completely known. The purpose of this review is to emphasize the possible pathways through which colchicine improves the clinical outcomes in the acute setting of acute coronary syndromes by referring to the results of the studies published in the past 5 years. Aside from its stated systemic anti-inflammatory effect, colchicine could be a valuable addition to the therapeutic approach of acute MI by reducing the infarct size, stabilizing the coronary plaque, as well as reducing platelet aggregation. Moreover, colchicine may improve endothelial function, reduce the transcoronary release of cytokines, and prevent a rise in inflammatory markers after percutaneous coronary intervention, thus diminishing the residual inflammatory risk.