Prediction of obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome (a clinical case)
https://doi.org/10.34215/1609-1175-2026-2-82-85
Abstract
Current clinical guidelines emphasize the need for early risk stratification in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS). Meanwhile, the choice between invasive and conservative strategies for patients at low risk for adverse outcomes remains challenging. Our predictive model, developed using stochastic gradient boosting, highly accurately assesses the probability of obstructive coronary artery disease in the first hours of hospitalization. Data from three time points are sequentially integrated: the initial examination, one hour later (complete blood count), and three hours later (biochemistry and echocardiography). This article presents a clinical case that illustrates the practical application of the model.
About the Authors
M. M. Tsivanyuk
Vladivostok Clinical Hospital No 1; Far Eastern Federal University
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 Clinical Hospital No. 1.
10 Ajax Bay, Russky Island, Vladivostok, 690922; 22 Sadovaya str., Vladivostok, 690078; tel.: +7 (914) 791-60-63
B. I. Geltser
Vladivostok Clinical Hospital No 1
Russian Federation
Russian Federation
Vladivostok
References
1. Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiol. 2021;6(6):633–641. doi: 10.1001/jamacardio.2021.0122
2. Badertscher P, Boeddinghaus J, Twerenbold R, Nestelberger T, Wildi K, Wussler D, et al. Direct Comparison of the 0/1h and 0/3h Algorithms for Early Rule-Out of Acute Myocardial Infarction. Circulation. 2018;137(23):2536–2538. doi: 10.1161/CIRCULATIONAHA.118.034260
3. Mueller C, Giannitsis E, Christ M, Ordóñez-Llanos J, deFilippi C, McCord J, et al. Multicenter Evaluation of a 0-Hour/1-Hour Algorithm in the Diagnosis of Myocardial Infarction With HighSensitivity Cardiac Troponin T. Annals of emergency medicine. 2016;68(1):76-87.e4. doi: 10.1016/j.annemergmed.2015.11.013
4. Foy AJ, Liu G, Davidson WR Jr, Sciamanna C, Leslie DL. Comparative effectiveness of diagnostic testing strategies in emergency department patients with chest pain: an analysis of downstream testing, interventions, and outcomes. JAMA internal medicine. 2015;175(3):428–436. doi: 10.1001/jamainternmed.2014.7657
5. Geltser BI, Tsivanyuk MM, Shakhgeldyan KI, Emtseva ED, Vishnevskiy AA. Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome. Russian Journal of Cardiology. 2021;26(11):4494. (In Russ.). doi: 10.15829/1560-4071-2021-4494
6. Hoffmann U, Truong QA, Schoenfeld DA, Chou ET, Woodard PK, Nagurney JT, et al. Coronary CT angiography versus standard evaluation in acute chest pain. The New England journal of medicine. 2012;26;367(4):299–308. doi: 10.1056/NEJMoa1201161
7. Mahler SA, Riley RF, Hiestand BC, Russell GB, Hoekstra JW, Lefebvre CW, et al. The HEART Pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge. Circulation. Cardiovascular quality and outcomes. 2015;8(2):195–203. doi: 10.1161/CIRCOUTCOMES.114.001384
8. Tsivanyuk MM, Shakhgeldyan KI, Markov MA, Shirobokov VG, Geltser BI. Machine Learning Efficiency in Predicting Obstructive Coronary Artery Disease in Patients with Non-ST Elevation Acute Coronary Syndrome in the First Hours of Admission. Sovremennye tekhnologii v meditsine. 2025;17(3):50–60. (In Russ.) doi: 10.17691/stm2025.17.3.05
9. Tsivanyuk MM, Geltser BI, Shakhgeldyan KI, Vishnevskiy AA, Shekunova OI. Parameters of complete blood count, lipid profile and their ratios in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome. Russian Journal of Cardiology. 2022;27(8):5079. (In Russ.). doi: 10.15829/1560-4071-2022-5079
10. Fox KA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333(7578):1091. doi: 10.1136/bmj.38985.646481.55
11. Antman EM, Cohen M, Bernink PJ, McCabe CH, Horacek T, Papuchis G, et al. The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making. JAMA. 2000;284(7):835–842. doi: 10.1001/jama.284.7.835
12. Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018;319(13):1317–1318. doi: 10.1001/jama.2017.18391
13. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial Intelligence in Cardiology. JACC. 2018;71(23):2668–2679. doi: 10.1016/j.jacc.2018.03.521
14. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156–64. doi: 10.1136/heartjnl-2017-311198
15. Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nature medicine. 2019;25(1):70–74. doi: 10.1038/s41591-018-0240-2
Review
For citations:
Tsivanyuk M.M., Geltser B.I. Prediction of obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome (a clinical case). Pacific Medical Journal. 2026;(2):82-85. (In Russ.) https://doi.org/10.34215/1609-1175-2026-2-82-85
Views:
41
JATS XML
Email this article 