Prediction of left main coronary artery lesion in patients with stable coronary artery disease using artificial intelligence technology

Aim. To develop a software package that predicts the presence of significant stenosis of the left main coronary artery (LMCA) in patients with stable coronary artery disease (CАD) based on clinical and instrumental signs using machine learning (ML) methods.

Material and methods. The study included 208 patients with stable CAD, hospitalized for planned invasive coronary angiography (ICA). Based on the results of ICA, two groups of patients were identified: the main group - 104 patients with hemodynamically significant stenosis of the LMCA and the control group - 104 patients without obstructive lesions of the coronary arteries. Stenosis of the LMCA of more than 50% was considered significant. Based on the study of case histories, an Excel database was created containing information on 107 clinical and instrumental features of all patients. Based on the results of a preliminary significance analysis, 63 potentially important features were selected for further use in the classifier model. Then, 59 of the most important features from the point of view of ML were identified. Given the nature of the features under consideration, gradient boosting was chosen as the ML algorithm. The programs developed during the study were written in the Python programming language in the PyCharm integrated programming environment.

Results. Based on the most informative features, models for predicting LMCA damage in patients with CAD were constructed. Gradient boosting was determined as the optimal ML algorithm. Three of its implementations were considered: LightGBM (Light Gradient Boosting Machine), XGBoost (Extreme Gradient Boosting) and CatBoost (Categorical Boosting). The CatBoost model turned out to be the most effective for classifying the presented objects. The software package using the CatBoost model demonstrated the accuracy of the classifier prediction of 86.2% on the test data set with a sensitivity and specificity of 77% and 76.9%, respectively. According to the constructed learning curves, it was found that further expansion of the training data volume will improve the quality of the model.

Conclusion. The developed software package for identifying hemodynamically significant stenosis of the LMCA in patients with CAD has good prognostic accuracy with the prospect of further training. The obtained solution can be integrated into the diagnostic process as part of an application for a personal computer or a web interface in order to provide support for medical decision-making.

1. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100-2132. DOI:10.1016/S0140-6736(24)00367-2.

2. Vrints C, Andreotti F, Koskinas KC, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes. Eur Heart J. 2024;45(36):3415-3537. DOI:10.1093/eurheartj/ehae177.

3. Davidson LJ, Cleveland JC, Welt FG, et al. A Practical Approach to Left Main Coronary Artery Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 2022;80(22):2119-2134. DOI:10.1016/j.jacc.2022.09.034.

4. Virani SS, Newby LK, Arnold SV, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2023;148(9):e9-e119. DOI:10.1161/CIR.0000000000001168.

5. Senior R, Reynolds HR, Min JK, et al. Predictors of Left Main Coronary Artery Disease in the ISCHEMIA Trial. J Am Coll Cardiol. 2022;79(7):651-661. DOI:10.1016/j.jacc.2021.11.052.

6. Liu H, Xu Z, Sun C, et al. A Variant in COX-2 Gene Is Associated with Left Main Coronary Artery Disease and Clinical Outcomes of Coronary Artery Bypass Grafting. Biomed Res Int. 2017;2017:2924731. DOI:10.1155/2017/2924731.

7. Liu R, Liu H, Gu H, et al. A Polymorphism in Hepatocyte Nuclear Factor 1 Alpha, rs7310409, Is Associated with Left Main Coronary Artery Disease. Biochem Res Int. 2014;2014:924105. DOI:10.1155/2014/924105.

8. Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41(3):407-477. DOI:10.1093/eurheartj/ehz425.

9. Lee GK, Hsieh YP, Hsu SW, et al. Value of ST-segment change in lead aVR in diagnosing left main disease in Non-ST-elevation acute coronary syndrome-A meta-analysis. Ann Noninvasive Electrocardiol. 2019;24(6):e12692. DOI:10.1111/anec.12692.

10. Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;144(22):e368-e454. DOI:10.1161/CIR.0000000000001029.

11. Neumann FJ, Sousa-Uva M, Ahlsson A, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40(2):87-165. DOI:10.1093/eurheartj/ehy394.

12. Sumin AN. Role of clinical evaluation in the identification of coronary obstructive disorders in patients with stable coronary artery disease. Part I. Russian Journal of Cardiology. 2019;(5):95-100. DOI:10.15829/1560-4071-2019-5-95-100.

13. Tsivanyuk MM, Geltser BI, Shakhgeldyan KI, et al. Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome. Russian Journal of Cardiology. 2022;27(6):5036. DOI:10.15829/1560-4071-2022-5036.

14. Oise GP, Oyedotun SA, Nwabuokei OC, et al. Enhanced prediction of coronary artery disease using logistic regression. FUDMA Journal of Sciences. 2025;9(3):201-208. DOI:10.33003/fjs-2025-0903-3263.

15. Nuchanat P, Methavigul K. Predictive model for left main coronary artery or triple vessel disease in patients with chronic coronary syndromes. Asian Biomed (Res Rev News). 2024;18(4):180-185. DOI:10.2478/abm-2024-0024.

16. Abdualimov TP, Obrezan AG. Detection of Coronary Artery Lesions Using Deep Learning Algorithms. Cardiology: News, Opinions, Training. 2021;9(2):9-13. DOI:10.33029/2309-1908-2021-9-2-9-13.

17. Garavand A, Behmanesh A, Aslani N, et al. Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art. International Journal of Intelligent Systems. 2023;4:1-19. DOI:10.1155/2023/6442756.

18. Patel MR, Calhoon JH, Dehmer GJ, et al. ACC/AATS/AHA/ASE/ASNC/SCAI/SCCT/STS 2017 Appropriate Use Criteria for Coronary Revascularization in Patients With Stable Ischemic Heart Disease: A Report of the American College of Cardiology Appropriate Use Criteria Task Force, American Association for Thoracic Surgery, American Heart Association, American Society of Echocardiography, American Society of Nuclear Cardiology, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and Society of Thoracic Surgeons. J Am Coll Cardiol. 2017;69(17):2212-2241. DOI:10.1016/j.jacc.2017.02.001.

19. National standard of the Russian Federation GOST R 59921.5—2022 “Artificial intelligence systems in clinical medicine. Part 5. Requirements for the structure and application of dataset for training and testing algorithms” dated 01.06.2022. Available from: https://docs.cntd.ru/document/1200183858.

20. Maron DJ, Mancini GBJ, Hartigan PM, et al. Healthy Behavior, Risk Factor Control, and Survival in the COURAGE Trial. J Am Coll Cardiol. 2018;72(19):2297-2305. DOI:10.1016/j.jacc.2018.08.2163.

21. Maron DJ, Hochman JS, Reynolds HR, et al. Initial Invasive or Conservative Strategy for Stable Coronary Disease. N Engl J Med. 2020;382(15):1395-1407. DOI:10.1056/NEJMoa1915922.

22. Thompson A, Fleischmann KE, Smilowitz NR, et al. 2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2024;84(19):1869-1969. DOI:10.1016/j.jacc.2024.06.013.

23. Khawaja M, Britt M, Khan MA, et al. Left Main Coronary Artery Disease: A Contemporary Review of Diagnosis and Management. Rev Cardiovasc Med. 2024;25(2):66. DOI:10.31083/j.rcm2502066.

24. Fajadet J, Chieffo A. Current management of left main coronary artery disease. Eur Heart J. 2012;33(1):36-50b. DOI:10.1093/eurheartj/ehr426.

25. Algamal AM, Salem MA, Bedier AI, et al. Clinical and angiographic profile of left main coronary artery disease in patients with chronic coronary syndrome: a retrospective study. Egypt Heart J. 2025;77(1):17. DOI:10.1186/s43044-025-00615-5.

26. Geltser BI, Tsivanyuk MM, Shakhgeldyan KI, Rublev VYu. Machine learning as a tool for diagnostic and prognostic research in coronary artery disease. Russian Journal of Cardiology. 2020;25(12):3999. DOI:10.15829/1560-4071-2020-3999.

27. Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21. DOI:10.3389/fnbot.2013.00021.

28.