Modern approaches to predicting the risk of adverse cardiovascular events in patients with acute coronary syndrome
https://doi.org/10.21626/vestnik/2024-3/04
EDN: LFSICQ
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Despite the progress achieved in predicting the risk of development and adverse cardiovascular outcomes, there remains a large number of patients who are not recognized using standard predictive models. Objective: to assess the possibility of using machine learning to build a multi-marker model for predicting the outcome of acute coronary syndrome in the form of acute myocardial infarction or unstable angina. Materials and methods. The development of predictive models was carried out in the Python programming language (Pandas, Seaborn and Scikit-learn libraries) using a database compiled from the results of clinical and laboratory studies of 60 patients aged 18 to 59 years with acute coronary syndrome. Results. From a total list of 181 indicators (10,860 values), 3 most informative biomarkers were selected to identify the established endpoints (patient diagnosis). The random forest classifier outperformed other models in terms of accuracy, sensitivity and specificity, which allowed us to determine the occurrence of an unfavorable outcome of ACS with 96% accuracy. Conclusion. Based on the obtained data, a computer program "Calculator for calculating the probabilistic outcome of acute coronary syndrome" was developed for practical use in healthcare institutions.
About the Authors
Irina A. Snimshchikova
Orel State University named after I.S. Turgenev (I.S. Turhenev OSU)
Russian Federation
Russian Federation
Maria O. Revyakina
Orel State University named after I.S. Turgenev (I.S. Turhenev OSU)
Russian Federation
Russian Federation
Sergey P. Stroyev
Orel State University named after I.S. Turgenev (I.S. Turhenev OSU)
Russian Federation
Russian Federation
References
1. World Heart Report 2023: Confronting the World’s Number One Killer. Geneva, Switzerland. World Heart Federation. 2023
2. Russian Statistical Yearbook. 2023. Moscow: Rosstat, 2023. 701 p. (in Russ.)
3. Healthcare in Russia. 2023. Moscow: Rosstat, 2023. 179 p. (in Russ.)
4. Montone R.A., Ford T.J., Galli M., Rinaldi R., Bland A., Morrow A., Angiolillo D.J., Berry C. et al. Stratified medicine for acute and chronic coronary syndromes: A patient-tailored approach. Prog Cardiovasc Dis. 2024;85:2-13. DOI: 10.1016/j.pcad.2024.06.003.
5. Jensen R.V., Hjortbak M.V., Bøtker H.E. Ischemic Heart Disease: An Update. Semin Nucl Med. 2020;50(3):195-207. DOI: 10.1053/j.semnuclmed.2020.02.007.
6. Shuvy M., Beeri G., Klein E., Cohen T., Shlomo N., Minha S., Pereg D. Accuracy of the Global Registry of Acute Coronary Events (GRACE) Risk Score in Contemporary Treatment of Patients With Acute Coronary Syndrome. Can J Cardiol. 2018;34(12): 1613-1617. DOI: 10.1016/j.cjca.2018.09.015.
7. Zhang Q., Gao J., Yin X., Zhang S., Wang Y., Ji H., Zhang X., Song D. et al. Risk Prediction Models for Ischemic Cardiovascular Outcomes in Patients with Acute Coronary Syndrome. Rev Cardiovasc Med. 2023;24(4):106. DOI: 10.31083/j.rcm2404106.
8. Wong Y.K., Cheung C.Y.Y., Tang C.S., Au K.W., Hai J.S.H., Lee C.H., Lau K.K. et al. Age-Biomarkers-Clinical Risk Factors for Prediction of Cardiovascular Events in Patients With Coronary Artery Disease. Arterioscler Thromb Vasc Biol. 2018;38(10):2519-2527. DOI: 10.1161/ATVBAHA.118.311726.
9. Expert Group on Biomarkers. Biomarkers in Cardiology - Part 2: In Coronary Heart Disease, Valve Disease and Special Situations. Arq Bras Cardiol. 2015;104(5):337-346. DOI: 10.5935/abc.20150061.
10. EXpert Group on Biomarkers. Biomarkers in cardiology - part 1 - in heart failure and specific cardiomyopathies. Arq Bras Cardiol. 2014;103(6):451-459. DOI: 10.5935/abc.20140184.
11. Severino P., D'Amato A., Pucci M., Infusino F., Adamo F., Birtolo L.I., Netti L., Montefusco G. et al. Ischemic Heart Disease Pathophysiology Paradigms Overview: From Plaque Activation to Microvascular Dysfunction.Int J Mol Sci. 2020;21(21):8118. DOI: 10.3390/ijms21218118.
12. Saeedbakhsh S., Sattari M., Mohammadi M., Najafian J., Mohammadi F. Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest. Adv Biomed Res. 2023;12:51. DOI: 10.4103/abr.abr_383_21.
13. Song L., Li Y., Nie S., Feng Z., Liu Y., Ding F., Gong L., Liu L., et al. Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study. Clin Cardiol. 2023;46(12):1594-1602. DOI: 10.1002/clc.24127.
14. D'Ascenzo F., De Filippo O., Gallone G., Mittone G., Deriu M.A., Iannaccone M., Ariza-Solé A., Liebetrau C. et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021;397(10270):199-207. DOI: 10.1016/S0140-6736(20)32519-8.
15. Collazo R.A., Pessôa L.A.M., Bahiense L., Pereira B. de B., Reis A.F. dos, Silva N.S. et al. A Comparative study between artificial neural network and support vector machine for acute coronary syndrome prognosis. Pesqui Oper. 2016;36(2):321-343. DOI: 10.1590/0101-7438.2016.036.02.0321
16. Zhang X., Wang X., Xu L., Liu J., Ren P., Wu H. The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis. Eur J Med Res. 2023;28(1):451. DOI: 10.1186/s40001-023-01027-4.
17. Na L., Lin J., Kuiwu Y. Risk prediction model for major adverse cardiovascular events (MACE) during hospitalization in patients with coronary heart disease based on myocardial energy metabolic substrate. Front Cardiovasc Med. 2023;10:1137778. DOI: 10.3389/fcvm.2023.1137778.
18. Tao K., Li J., Li J., Shan W., Yan H., Lu Y. Estimation of Heart Rate Using Regression Models and Artificial Neural Network in Middle-Aged Adults. Front Physiol. 2021;12:742754. DOI: 10.3389/fphys.2021.742754.
19. Li R., Shen L., Ma W., Yan B., Chen W., Zhu J., Li L., Yuan J. et al. Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome. Clin Cardiol. 2023;46(2):184-194. DOI: 10.1002/clc.23957.
20. Snimshchikova I.A., Plotnikova M.O. Role of morphogenic proteins of the WNT signaling pathway in coronary artery disease. Medical Immunology (Russia). 2023;25(4):985-990. DOI: 10.15789/1563-0625-ROM-2835.
21. Plotnikova M.O., Snimshchikova I. A., Shkodkina S.A. Diagnostic significance of oxidative stress in patients with coronary heart disease. Medical & pharmaceutical journal «Pulse». 2022;24(5):63-67 (in Russ.). DOI: 10.26787/nydha-2686-6838-2022-24-5-63-67. EDN: LZZWAZ.
22. Nohria R., Viera A.J. Acute Coronary Syndrome: Diagnosis and Initial Management. Am Fam Physician. 2024;109(1):34-42.
23. Byrne R.A., Rossello X., Coughlan J.J., Barbato E., Berry C., Chieffo A., Claeys M.J., Dan G.A. et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720-3826. DOI: 10.1093/eurheartj/ehad191.
24. Garg P., Morris P., Fazlanie A.L., Vijayan S., Dancso B., Dastidar A.G., Plein S., Mueller C. et al. Cardiac biomarkers of acute coronary syndrome: from history to high-sensitivity cardiac troponin.Intern Emerg Med. 2017;12(2):147-155. DOI: 10.1007/s11739-017-1612-1.
25. Celik S., Giannitsis E., Wollert K.C., Schwöbel K., Lossnitzer D., Hilbel T., Lehrke S. et al. Cardiac troponin T concentrations above the 99th percentile value as measured by a new high-sensitivity assay predict long-term prognosis in patients with acute coronary syndromes undergoing routine early invasive strategy. Clin Res Cardiol. 2011;100(12): 1077-1085. DOI: 10.1007/s00392-011-0344-x.
26. Wu A.H. Early detection of acute coronary syndromes and risk stratification by multimarker analysis. Biomark Med. 2007;1(1):45-57. DOI: 10.2217/17520363.1.1.45.
27. The program "Calculator for calculating the probabilistic outcome of acute coronary syndrome". Plotnikova M.O., Stroev S.P., Snimshchikova I.A., Vishnevsky V.I., Dorofeeva V.I., Kiselyov D.P., Chestnykhina A.D. No. 2022668140 Certificate of state registration of a computer program of the Russian Federation. Oct 4, 2022 (in Russ.)
Review
For citations:
Snimshchikova I.A., Revyakina M.O., Stroyev S.P. Modern approaches to predicting the risk of adverse cardiovascular events in patients with acute coronary syndrome. Humans and their health. 2024;27(3):39-50. (In Russ.) https://doi.org/10.21626/vestnik/2024-3/04. EDN: LFSICQ
Views:
137
Email this article 