Machine learning models for assessing pre-test probability of intact coronary arteries in patients with non-ST-segment elevation myocardial infarction
https://doi.org/10.34215/1609-1175-2026-2-56-60
Abstract
Objective: To develop machine learning (ML) models for assessing the pretest probability of intact coronary arteries (CA) in patients with non–ST-segment elevation myocardial infarction (NSTEMI).
Materials and methods. A single-center prospective study included 257 patients with NSTEMI who underwent invasive coronary angiography (ICA). Clinical and demographic characteristics, laboratory data, electrocardiogram and echocardiogram results were analyzed. ML methods, including multivariate logistic regression and stratified 10-fold cross-validation, were used to develop predictive models for pre-test diagnosis of intact CA.
Results. ICA revealed intact CA in 13.2% of patients. A multivariate logistic regression-based ML model exhibited the highest prognostic accuracy (AUC = 0.87), with the following predictors: age, low-density lipoprotein cholesterol, aortic root diameter, total protein concentration, total bilirubin, blood monocyte count, ST-segment depression, and fibrinogen level.
Conclusion. Using multifactorial ML models improves the accuracy of the pretest diagnosis of intact CA in patients with NSTEMI. Thus, personalized treatment strategies can be selected for patients with this disease phenotype.
About the Authors
B. I. Geltser
Far East Federal University
Russian Federation
Russian Federation
Russky Island, Vladivostok
M. M. Tsivanyuk
Far East Federal University; Vladivostok Clinical Hospital № 1
Russian Federation
Russian Federation
Mikhail M. Tsivanyuk - Cand. Sci. (Med.), Senior Researcher at the Laboratory for Big Data Analysis in Healthcare and Medicine, Far EFU; Head of the Department of Department of Interventional Radiology, Vladivostok CH № 1
10 Ajax Bay, Russky Island, Vladivostok 690922; 22 Sadovaya str., Vladivostok 690078; tel.: +7 (914) 791-60-63
K. I. Shakhgeldyan
Far East Federal University; Vladivostok State University
Russian Federation
Russian Federation
Russky Island, Vladivostok
D. Ya. Zhukov
Vladivostok State University
Russian Federation
Russian Federation
Vladivostok
V. V. Mostovaya
Far East Federal University
Russian Federation
Russian Federation
Russky Island, Vladivostok
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Review
For citations:
Geltser B.I., Tsivanyuk M.M., Shakhgeldyan K.I., Zhukov D.Ya., Mostovaya V.V. Machine learning models for assessing pre-test probability of intact coronary arteries in patients with non-ST-segment elevation myocardial infarction. Pacific Medical Journal. 2026;(2):56-60. (In Russ.) https://doi.org/10.34215/1609-1175-2026-2-56-60
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