Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review)

Risk stratification of hospital mortality in patients with ST segment elevation myocardial infarction on the electrocardiogram is an important part of the specialized medical care provision. The systematic review presents scientific literature data characterizing the predictive value of both classical prognostic scales (GRACE, CADDILLAC, TIMI risk score for STEMI, RECORD, etc.) and new risk measurement tools developed on the basis of modern machine learning techniques. Most studies on this issue are often focused on the search for new predictors of adverse events, which allow to detail the relations between indicators of the clinical and functional status of patients and the end point of the study. Here, an important task is to develop hospital mortality prognostic algorithms characterized by explainable artificial intelligence and trusted by doctors.

1. Alekyan B.G., Boytsov S.A., Manoshkina E.M., Ganyukov V.I. Myocardial revascularization in Russian Federation for acute coronary syndrome in 2016–2020. Kardiologiia 2021; 61(12): 4–15, https://doi.org/10.18087/cardio.2021.12.n1879.
2. Grabowski M., Filipiak K.J., Opolski G., Glowczynska R. How to improve prognostic value of popular risk scores used in acute coronary syndrome — a single center experience in a long term follow-up. Curr Res Cardiol 2018; 5(3): 30–33, https://doi.org/10.4172/2368-0512.1000108.
3. Fox K.A., Dabbous O.H., Goldberg R.J., Pieper K.S., Eagle K.A., Van de Werf F., Avezum A., Goodman S.G., Flather M.D., Anderson F.A. Jr, Granger C.B. 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, https://doi.org/10.1136/bmj.38985.646481.55.
4. Halkin A., Singh M., Nikolsky E., Grines C.L., Tcheng J.E., Garcia E., Cox D.A., Turco M., Stuckey T.D., Na Y., Lansky A.J., Gersh B.J., O’Neill W.W., Mehran R., Stone G.W. Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score. J Am Coll Cardiol 2005; 45(9): 1397–1405, https://doi.org/10.1016/j.jacc.2005.01.041.
5. Morrow D.A., Antman E.M., Charlesworth A., Cairns R., Murphy S.A., de Lemos J.A., Giugliano R.P., McCabe C.H., Braunwald E. TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation 2000; 102(17): 2031–2037, https://doi.org/10.1161/01.cir.102.17.2031.
6. Erlikh A.D. A scale for early assessment of risk of death or myocardial infarction during initial hospitalization of patients with acute coronary syndromes (based on data from the RECORD registry). Kardiologiia 2010; 10: 11–16.
7. Jacobs D.R. Jr, Kroenke C., Crow R., Deshpande M., Gu D.F., Gatewood L., Blackburn H. PREDICT: a simple risk score for clinical severity and long-term prognosis after hospitalization for acute myocardial infarction or unstable angina: the Minnesota heart survey. Circulation 1999; 100(6): 599–607, https://doi.org/10.1161/01.cir.100.6.599.
8. Califf R.M., Pieper K.S., Lee K.L., Van De Werf F., Simes R.J., Armstrong P.W., Topol E.J. Prediction of 1-year survival after thrombolysis for acute myocardial infarction in the global utilization of streptokinase and TPA for occluded coronary arteries trial. Circulation 2000; 101(19): 2231–2238, https://doi.org/10.1161/01.cir.101.19.2231.
9. Addala S., Grines C.L., Dixon S.R., Stone G.W., Boura J.A., Ochoa A.B., Pellizzon G., O’Neill W.W., Kahn J.K. Predicting mortality in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention (PAMI risk score). Am J Cardiol 2004; 93(5): 629–632, https://doi.org/10.1016/j.amjcard.2003.11.036.
10. De Luca G., Suryapranata H., van’t Hof A.W., de Boer M.J., Hoorntje J.C., Dambrink J.H., Gosselink A.T., Ottervanger J.P., Zijlstra F. Prognostic assessment of patients with acute myocardial infarction treated with primary angioplasty: implications for early discharge. Circulation 2004; 109(22): 2737–2743, https://doi.org/10.1161/01.CIR.0000131765.73959.87.
11. Fox K.A., Fitzgerald G., Puymirat E., Huang W., Carruthers K., Simon T., Coste P., Monsegu J., Gabriel Steg P., Danchin N., Anderson F. Should patients with acute coronary disease be stratified for management according to their risk? Derivation, external validation and outcomes using the updated GRACE risk score. BMJ Open 2014; 4(2): e004425, https://doi.org/10.1136/bmjopen-2013-004425.
12. Szabo D., Szabo A., Magyar L., Banhegyi G., Kugler S., Pinter A., Juhasz V., Ruppert M., Olah A., Ruzsa Z., Edes I.F., Szekely A., Becker D., Merkely B., Hizoh I. Admission lactate level and the GRACE 2.0 score are independent and additive predictors of 30-day mortality of STEMI patients treated with primary PCI-results of a real-world registry. PLoS One 2022; 17(11): e0277785, https://doi.org/10.1371/journal.pone.0277785.
13. Xiao L., Jia Y., Wang X., Huang H. The impact of preoperative fibrinogen-albumin ratio on mortality in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Clin Chim Acta 2019; 493: 8–13, https://doi.org/10.1016/j.cca.2019.02.018.
14. Klingenberg R., Aghlmandi S., Räber L., Gencer B., Nanchen D., Heg D., Carballo S., Rodondi N., Mach F., Windecker S., Jüni P., von Eckardstein A., Matter C.M., Lüscher T.F. Improved risk stratification of patients with acute coronary syndromes using a combination of hsTnT, NT-proBNP and hsCRP with the GRACE score. Eur Heart J Acute Cardiovasc Care 2018; 7(2): 129–138, https://doi.org/10.1177/2048872616684678.
15. Xiong S., Chen Q., Chen X., Hou J., Chen Y., Long Y., Yang S., Qi L., Su H., Huang W., Liu H., Zhang Z., Cai L. Adjustment of the GRACE score by the triglyceride glucose index improves the prediction of clinical outcomes in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Cardiovasc Diabetol 2022; 21(1): 145, https://doi.org/10.1186/s12933-022-01582-w.
16. Pang S., Miao G., Zhou Y., Du Y., Rui Z., Zhao X. Addition of TyG index to the GRACE score improves prediction of adverse cardiovascular outcomes in patients with non-ST-segment elevation acute coronary syndrome undergoing percutaneous coronary intervention: a retrospective study. Front Cardiovasc Med 2022; 9: 957626, https://doi.org/10.3389/fcvm.2022.957626.
17. Li Q., Ma X., Shao Q., Yang Z., Wang Y., Gao F., Zhou Y., Yang L., Wang Z. Prognostic impact of multiple lymphocyte-based inflammatory indices in acute coronary syndrome patients. Front Cardiovasc Med 2022; 9: 811790, https://doi.org/10.3389/fcvm.2022.811790.
18. Dalimunthe N.N., Alwi I., Nasution S.A., Shatri H. The role of Tei index added to the GRACE risk score for prediction of in-hospital MACE after acute myocardial infarction. Rom J Intern Med 2022; 60(4): 222–228, https://doi.org/10.2478/rjim-2022-0012.
19. Xiong S., Luo Y., Chen Q., Chen Y., Su H., Long Y., Chen X., Yang S., Qi L., Huang W., Hou J., Liu H., Cai L. Adjustment of the GRACE score by the stress hyperglycemia ratio improves the prediction of long-term major adverse cardiac events in patients with acute coronary syndrome undergoing percutaneous coronary intervention: a multicenter retrospective study. Diabetes Res Clin Pract 2023; 198: 110601, https://doi.org/10.1016/j.diabres.2023.110601.
20. Chen X., Xiong S., Chen Y., Cheng L., Chen Q., Yang S., Qi L., Liu H., Cai L. The predictive value of different nutritional indices combined with the GRACE score in predicting the risk of long-term death in patients with acute coronary syndrome undergoing percutaneous coronary intervention. J Cardiovasc Dev Dis 2022; 9(10): 358, https://doi.org/10.3390/jcdd9100358.
21. Liu X.J., Wan Z.F., Zhao N., Zhang Y.P., Mi L., Wang X.H., Zhou D., Wu Y., Yuan Z.Y. Adjustment of the GRACE score by hemoglobinA1c enables a more accurate prediction of long-term major adverse cardiac events in acute coronary syndrome without diabetes undergoing percutaneous coronary intervention. Cardiovasc Diabetol 2015; 14: 110, https://doi.org/10.1186/s12933-015-0274-4.
22. Zhang S., Wan Z., Zhang Y., Fan Y., Gu W., Li F., Meng L., Zeng X., Han D., Li X. Neutrophil count improves the GRACE risk score prediction of clinical outcomes in patients with ST-elevation myocardial infarction. Atherosclerosis 2015; 241(2): 723–728, https://doi.org/10.1016/j.atherosclerosis.2015.06.035.
23. Chen Q., Su H., Yu X., Chen Y., Ding X., Xiong B., Wang C., Xia L., Ye T., Lan K., Hou J., Xiong S., Cai L. The stress hyperglycemia ratio improves the predictive ability of the GRACE score for in-hospital mortality in patients with acute myocardial infarction. Hellenic J Cardiol 2023; 70: 36–45, https://doi.org/10.1016/j.hjc.2022.12.012.
24. Geltser B.I., Shahgeldyan K.I., Domzhalov I.G., Kuksin N.S., Kokarev E.A., Kotelnikov V.N., Rublev V.Yu. Prediction of in-hospital mortality in patients with ST-segment elevation acute myocardial infarction after percutaneous coronary intervention. Russian Journal of Cardiology 2023; 28(6): 5414, https://doi.org/10.15829/1560-4071-2023-5414.
25. Erlikh A.D. Opportunity to select patients for primary invasive intervention in ACS with ST-segment elevation using “RECORD” modified scale. Trudnyy patsient 2013; 11(7): 36–39.
26. Shakhgeldyan K.I., Kuksin N.S., Domzhalov I.G., Rublev V.Y., Geltser B.I. Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions. Comput Biol Med 2024; 170: 107953, https://doi.org/10.1016/j.compbiomed.2024.107953.
27. Farooq V., van Klaveren D., Steyerberg E.W., Meliga E., Vergouwe Y., Chieffo A., Kappetein A.P., Colombo A., Holmes D.R. Jr, Mack M., Feldman T., Morice M.C., Ståhle E., Onuma Y., Morel M.A., Garcia-Garcia H.M., van Es G.A., Dawkins K.D., Mohr F.W., Serruys P.W. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet 2013; 381(9867): 639–650, https://doi.org/10.1016/S0140-6736(13)60108-7.
28. Karabağ Y., Çağdaş M., Rencuzogullari I., Karakoyun S., Artaç İ., İliş D., Yesin M., Öterkus M., Gokdeniz T., Burak C., Tanboğa I.H. Comparison of SYNTAX score II efficacy with SYNTAX score and TIMI risk score for predicting in-hospital and long-term mortality in patients with ST segment elevation myocardial infarction. Int J Cardiovasc Imaging 2018; 34(8): 1165–1175, https://doi.org/10.1007/s10554-018-1333-1.
29. McNamara R.L., Kennedy K.F., Cohen D.J., Diercks D.B., Moscucci M., Ramee S., Wang T.Y., Connolly T., Spertus J.A. Predicting in-hospital mortality in patients with acute myocardial infarction. J Am Coll Cardiol 2016; 68(6): 626–635, https://doi.org/10.1016/j.jacc.2016.05.049.
30. Rodríguez-Jiménez A.E., Negrín-Valdés T., Cruz-Inerarity H., Castellano-Gallo L.A., Chávez-González E. Prognostic scale to stratify risk of intrahospital death in patients with acute myocardial infarction with ST-segment elevation. MEDICC Rev 2020; 22(3): 46–53, https://doi.org/10.37757/MR2020.V22.N3.10.
31. Bessonov I.S., Kuznetsov V.A., Sapozhnikov S.S., Gorbatenko E.A., Shadrin A.A. The risk score for in-hospital mortality in patients with ST-segment elevation myocardial infarction. Kardiologiia 2021; 61(9): 11–19, https://doi.org/10.18087/cardio.2021.9.n1720.
32. Hadanny A., Shouval R., Wu J., Shlomo N., Unger R., Zahger D., Matetzky S., Goldenberg I., Beigel R., Gale C., Iakobishvili Z. Predicting 30-day mortality after ST elevation myocardial infarction: machine learning-based random forest and its external validation using two independent nationwide datasets. J Cardiol 2021; 78(5): 439–446, https://doi.org/10.1016/j.jjcc.2021.06.002.
33. Millo L., McKenzie A., De la Paz A., Zhou C., Yeung M., Stouffer G.A. Usefulness of a novel risk score to predict in-hospital mortality in patients ≥60 years of age with ST elevation myocardial infarction. Am J Cardiol 2021; 154: 1–6, https://doi.org/10.1016/j.amjcard.2021.05.036.
34. Tan L., Xu Q., Shi R. A nomogram for predicting hospital mortality in intensive care unit patients with acute myocardial infarction. Int J Gen Med 2021; 14: 5863–5877, https://doi.org/10.2147/IJGM.S326898.
35. Jain A., Fong H.K., Ijaz H.M., Desai R. Predictors of in-hospital mortality in young adults hospitalized with ST-elevation myocardial infarction: an artificial neural network analysis using a nationwide cohort. J Am Coll Cardiol 2022; 79(9): 1064, https://doi.org/10.1016/s0735-1097(22)02055-1.
36. Deng L., Zhao X., Su X., Zhou M., Huang D., Zeng X. Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention. BMC Med Inform Decis Mak 2022; 22(1): 109, https://doi.org/10.1186/s12911-022-01853-2.
37. Zhao P., Liu C., Zhang C., Hou Y., Zhang X., Zhao J., Sun G., Zhou J. Using machine learning to predict the in-hospital mortality in women with ST-segment elevation myocardial infarction. Rev Cardiovasc Med 2023, 24(5): 126, https://doi.org/10.31083/j.rcm2405126.
38. Li R., Shen L., Ma W., Yan B., Chen W., Zhu J., Li L., Yuan J., Pan C. Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome. Clin Cardiol 2023; 46(2): 184–194, https://doi.org/10.1002/clc.23957.
39. Zhu X., Xie B., Chen Y., Zeng H., Hu J. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. Clin Chim Acta 2024; 554: 117776, https://doi.org/10.1016/j.cca.2024.117776.
40. Norton J.M., Reddy P.K., Subedi K., Fabrizio C.A., Wimmer N.J., Urrutia L.E. Utilization of an ICU severity of illness scoring system to triage patients with ST-elevation myocardial infarction. J Intensive Care Med 2021; 36(8): 857–861, https://doi.org/10.1177/0885066620928263.
41. Guo C., Luo X.L., Gao X.J., Wang J., Liu R., Li J., Zhang J., Yang W.X., Hu F.H., Wu Y., Yang Y.J., Qiao S.B. Comparison on the predictive value of different scoring systems for risk of short-term death in patients with acute myocardial infarction complicating cardiogenic shock. Zhonghua Xin Xue Guan Bing Za Zhi 2018; 46(7): 529–535, https://doi.org/10.3760/cma.j.issn.0253-3758.2018.07.005.
42. Chiang C.Y., Lin C.F., Liu P.H., Chen F.C., Chiu I.M., Cheng F.J. Clinical validation of the shock index, modified shock index, delta shock index, and shock index-C for emergency department ST-segment elevation myocardial infarction. J Clin Med 2022; 11(19): 5839, https://doi.org/10.3390/jcm11195839.
43. Wang S., Zhang Y., Cheng Q., Qi D., Wang X., Zhu Z., Li M., Zhang J., Hu D., Gao C. Shock index-based risk indices as prognostic predictor for in-hospital mortality in patients with ST-elevation myocardial infarction, the results from Henan STEMI registry. Preprint (Version 1) 2022 Apr 11, https://doi.org/10.21203/rs.3.rs-1512785/v1.
44. Li F., Li D., Yu J., Jia Y., Jiang Y., Chen X., Gao Y., Ye L., Wan Z., Cao Y., Zeng Z., Zeng R. Prognostic value of the Nutritional Risk Screening 2002 Scale in patients with acute myocardial infarction: insights from the retrospective multicenter study for early evaluation of acute chest pain. J Cardiovasc Nurs 2021; 36(6): 546–555, https://doi.org/10.1097/JCN.0000000000000786.
45. Jia Y., Li H., Li D., Li F., Li Q., Jiang Y., Gao Y., Wan Z., Cao Y., Zeng Z., Zeng R. Prognostic value of Braden Scale in patients with acute myocardial infarction: from the retrospective multicenter study for early evaluation of acute chest pain. J Cardiovasc Nurs 2020; 35(6): E53–E61, https://doi.org/10.1097/JCN.0000000000000735.
46. Ndrepepa G., Holdenrieder S., Kastrati A. Prognostic value of De Ritis ratio in patients with acute myocardial infarction. Clin Chim Acta 2022; 535: 75–81, https://doi.org/10.1016/j.cca.2022.08.016.
47. Rivera-Fernández R., Arias-Verdú M.D., García-Paredes T., Delgado-Rodríguez M., Arboleda-Sánchez J.A., Aguilar-Alonso E., Quesada-García G., Vera-Almazán A. Prolonged QT interval in ST-elevation myocardial infarction and mortality: new prognostic scale with QT, Killip and age. J Cardiovasc Med (Hagerstown) 2016; 17(1): 11–19, https://doi.org/10.2459/JCM.0000000000000015.
48. Kwon J.M., Jeon K.H., Kim H.M., Kim M.J., Lim S., Kim K.H., Song P.S., Park J., Choi R.K., Oh B.H. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PLoS One 2019; 14(10): e0224502, https://doi.org/10.1371/journal.pone.0224502.
49. Sherazi S.W.A., Zheng H., Lee J.Y. A machine learning-based applied prediction model for identification of acute coronary syndrome (ACS) outcomes and mortality in patients during the hospital stay. Sensors (Basel) 2023; 23(3): 1351, https://doi.org/10.3390/s23031351.
50. Kasim S., Malek S., Song C., Wan Ahmad W.A., Fong A., Ibrahim K.S., Safiruz M.S., Aziz F., Hiew J.H., Ibrahim N. In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. PLoS One 2022; 17(12): e0278944, https://doi.org/10.1371/journal.pone.0278944.
51. Singh A., Miller R.J.H., Otaki Y., Kavanagh P., Hauser M.T., Tzolos E., Kwiecinski J., Van Kriekinge S., Wei C.C., Sharir T., Einstein A.J., Fish M.B., Ruddy T.D., Kaufmann P.A., Sinusas A.J., Miller E.J., Bateman T.M., Dorbala S., Di Carli M., Liang J.X., Huang C., Han D., Dey D., Berman D.S., Slomka P.J. Direct risk assessment from myocardial perfusion imaging using explainable deep learning. JACC Cardiovasc Imaging 2023; 16(2): 209–220, https://doi.org/10.1016/j.jcmg.2022.07.017.
52. Hizoh I., Banhegyi G., Domokos D., Major L., Andrassy P., Andreka P., Becker D., Buttl A., Cziraki A., Dezsi C.A., Edes I., Forster T., Herczeg B., Janosi A., Katona A., Kiss R., Koszegi Z., Lupkovics G., Nagy G., Nagy L., Noori E., Tomcsanyi J., Veress G., Ruzsa Z., Merkely B. TCT-804 comparative validation of the ALPHA score, a novel risk model including vascular access site for predicting 30-day mortality in patients treated with primary PCI. J Am Coll Cardiol 2018; 72 (13 Suppl): B320–B321, https://doi.org/10.1016/j.jacc.2018.08.2039.
53. Hizoh I., Domokos D., Banhegyi G., Becker D., Merkely B., Ruzsa Z. Mortality prediction algorithms for patients undergoing primary percutaneous coronary intervention. J Thorac Dis 2020; 12(4): 1706–1720, https://doi.org/10.21037/jtd.2019.12.83.
54. Zhang X.T., Lin Z.R., Zhang L., Zhao Z.W., Chen L.L. MELD-XI score predict no-reflow phenomenon and short-term mortality in patient with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. BMC Cardiovasc Disord 2022; 22(1): 113, https://doi.org/10.1186/s12872-022-02556-2.
55. Toprak K., Toprak İ.H., Acar O., Ermiş M.F. The predictive value of the HALP score for no-reflow phenomenon and short-term mortality in patients with ST-elevation myocardial infarction. Postgrad Med 2024; 136(2): 169–179, https://doi.org/10.1080/00325481.2024.2319567.
56. Safak O., Yildirim T., Emren V., Avci E., Argan O., Aktas Z., Yildirim S.E., Akgun D.E., Kisacik H.L. Prognostic nutritional index as a predictor of no-reflow occurrence in patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention. Angiology 2024; 75(7): 689–695, https://doi.org/10.1177/00033197231193223.
57. Kumar R., Ahmed I., Rai L., Khowaja S., Hashim M., Huma Z., Sial J.A., Saghir T., Qamar N., Karim M. Comparative analysis of four established risk scores for prediction of in-hospital mortality in patients undergoing primary percutaneous coronary intervention. Am J Cardiovasc Dis 2022; 12(6): 298–306.
58. Sivkov O.G. Factors associated with hospital mortality in acute myocardial infarction. Kardiologiia 2023; 63(11): 29–35, https://doi.org/10.18087/cardio.2023.11.n2406.
59. Semaan C., Charbonnier A., Pasco J., Darwiche W., Saint Etienne C., Bailleul X., Bourguignon T., Fauchier L., Angoulvant D., Ivanes F., Genet T. Risk scores in ST-segment elevation myocardial infarction patients with refractory cardiogenic shock and veno-arterial extracorporeal membrane oxygenation. J Clin Med 2021; 10(5): 956, https://doi.org/10.3390/jcm10050956.
60. Reindl M., Reinstadler S.J., Tiller C., Kofler M., Theurl M., Klier N., Fleischmann K., Mayr A., Henninger B., Klug G., Metzler B. ACEF score adapted to ST-elevation myocardial infarction patients: the ACEF-STEMI score. Int J Cardiol 2018; 264: 18–24, https://doi.org/10.1016/j.ijcard.2018.04.017.
61. Sevdımbas S., Satar S., Gulen M., Acehan S., Acele A., Koksaldı Sahin G., Aka Satar D. Blood urea nitrogen/albumin ratio on admission predicts mortality in patients with non ST segment elevation myocardial infarction. Scand J Clin Lab Invest 2022; 82(6): 454–460, https://doi.org/10.1080/00365513.2022.2122075.
62. Geltser B.I., Rublev V.Yu., Tsivanyuk M.M., Shakhgeldyan K.I. Machine learning in predicting immediate and long-term outcomes of myocardial revascularization: a systematic review. Russian Journal of Cardiology 2021; 26(8): 4505, https://doi.org/10.15829/1560-4071-2021-4505.
63. Chen P., Wang B., Zhao L., Ma S., Wang Y., Zhu Y., Zeng X., Bai Z., Shi B. Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus. BMC Cardiovasc Disord 2023; 23(1): 585, https://doi.org/10.1186/s12872-023-03626-9.
64. Xiao C., Guo Y., Zhao K., Liu S., He N., He Y., Guo S., Chen Z. Prognostic value of machine learning in patients with acute myocardial infarction. J Cardiovasc Dev Dis 2022; 9(2): 56, https://doi.org/10.3390/jcdd9020056.
65. D’Ascenzo F., De Filippo O., Gallone G., Mittone G., Deriu M.A., Iannaccone M., Ariza-Solé A., Liebetrau C., Manzano-Fernández S., Quadri G., Kinnaird T., Campo G., Simao Henriques J.P., Hughes J.M., Dominguez-Rodriguez A., Aldinucci M., Morbiducci U., Patti G., Raposeiras-Roubin S., Abu-Assi E., De Ferrari G.M.; PRAISE study group. 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, https://doi.org/10.1016/S0140-6736(20)32519-8.
66. Zhao J., Zhao P., Li C., Hou Y. Optimized machine learning models to predict in-hospital mortality for patients with ST-segment elevation myocardial infarction. Ther Clin Risk Manag 2021; 17: 951–961, https://doi.org/10.2147/TCRM.S321799.
67. Shakhgeldyan K.I., Geltser B.I., Kuksin N.S., Domzhalov I.G. Multilevel categorization of continuous variables in the tasks of explaining predictive estimates of machine learning models in clinical medicine. Vrach i informatsionnye tekhnologii 2023; 3: 44–57, https://doi.org/10.25881/18110193_2023_3_44.
68. Ong A.T., Serruys P.W., Mohr F.W., Morice M.C., Kappetein A.P., Holmes D.R. Jr, Mack M.J., van den Brand M., Morel M.A., van Es G.A., Kleijne J., Koglin J., Russell M.E. The SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) study: design, rationale, and run-in phase. Am Heart J 2006; 151(6): 1194–1204, https://doi.org/10.1016/j.ahj.2005.07.017.
69. Lee S., Chu Y., Ryu J., Park Y.J., Yang S., Koh S.B. Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis. Yonsei Med J 2022; 63(Suppl): S93–S107, https://doi.org/10.3349/ymj.2022.63.S93.
70. Popat A., Yadav S., Patel S.K., Baddevolu S., Adusumilli S., Rao Dasari N., Sundarasetty M., Anand S., Sankar J., Jagtap Y.G. Artificial intelligence in the early prediction of cardiogenic shock in acute heart failure or myocardial infarction patients: a systematic review and meta-analysis. Cureus 2023; 15(12): e50395, https://doi.org/10.7759/cureus.50395.
71. Zhang Z., Zhao Y., Canes A., Steinberg D., Lyashevska O.; written on behalf of AME Big-Data Clinical Trial Collaborative Group. Predictive analytics with gradient boosting in clinical medicine. Ann Transl Med 2019; 7(7): 152, https://doi.org/10.21037/atm.2019.03.29.
72. Wu Y., Chen H., Li L., Zhang L., Dai K., Wen T., Peng J., Peng X., Zheng Z., Jiang T., Xiong W. Construction of novel gene signature-based predictive model for the diagnosis of acute myocardial infarction by combining random forest with artificial neural network. Front Cardiovasc Med 2022; 9: 876543, https://doi.org/10.3389/fcvm.2022.876543.
73. Chang Y., Antonescu C., Ravindranath S., Dong J., Lu M., Vicario F., Wondrely L., Thompson P., Swearingen D., Acharya D. Early prediction of cardiogenic shock using machine learning. Front Cardiovasc Med 2022; 9: 862424, https://doi.org/10.3389/fcvm.2022.862424.
74. Gong M., Liang D., Xu D., Jin Y., Wang G., Shan P. Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning approach. Comput Biol Med 2024; 170: 107950, https://doi.org/10.1016/j.compbiomed.2024.107950.
75. Oliveira M., Seringa J., Pinto F.J., Henriques R., Magalhães T. Machine learning prediction of mortality in acute myocardial infarction. BMC Med Inform Decis Mak 2023; 23(1): 70, https://doi.org/10.1186/s12911-023-02168-6.
76. Khera R., Haimovich J., Hurley N.C., McNamara R., Spertus J.A., Desai N., Rumsfeld J.S., Masoudi F.A., Huang C., Normand S.L., Mortazavi B.J., Krumholz H.M. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol 2021; 6(6): 633–641, https://doi.org/10.1001/jamacardio.2021.0122.
77. Lee H.C., Park J.S., Choe J.C., Ahn J.H., Lee H.W., Oh J.H., Choi J.H., Cha K.S., Hong T.J., Jeong M.H.; Korea Acute Myocardial Infarction Registry (KAMIR) and Korea Working Group on Myocardial Infarction (KorMI) Investigators. Prediction of 1-year mortality from acute myocardial infarction using machine learning. Am J Cardiol 2020; 133: 23–31, https://doi.org/10.1016/j.amjcard.2020.07.048.
78. Aziz F., Malek S., Ibrahim K.S., Raja Shariff R.E., Wan Ahmad W.A., Ali R.M., Liu K.T., Selvaraj G., Kasim S. Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach. PLoS One 2021; 16(8): e0254894, https://doi.org/10.1371/journal.pone.0254894.
79. Liu R., Wang M., Zheng T., Zhang R., Li N., Chen Z., Yan H., Shi Q. An artificial intelligence-based risk prediction model of myocardial infarction. BMC Bioinformatics 2022; 23(1): 217, https://doi.org/10.1186/s12859-022-04761-4.
80. Tarabanis C., Kalampokis E., Khalil M., Alviar C.L., Chinitz L.A., Jankelson L. Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction. Cardiovasc Digit Health J 2023; 4(4): 126–132, https://doi.org/10.1016/j.cvdhj.2023.06.001.
81. Hosmer D.W. Jr, Lemeshow S., Sturdivant R.X. Applied logistic regression, 3rd edition: guide to logistic regression modeling. John Wiley & Sons, Inc; 2013; 177 p.
82. Alizadehsani R., Abdar M., Roshanzamir M., Khosravi A., Kebria P.M., Khozeimeh F., Nahavandi S., Sarrafzadegan N., Acharya U.R. Machine learning-based coronary artery disease diagnosis: a comprehensive review. Comput Biol Med 2019; 111: 103346, https://doi.org/10.1016/j.compbiomed.2019.103346.
83. Wang C.H., Wang H.T., Wu K.H., Cheng F.J., Cheng C.I., Kung C.T., Chen F.C. Comparison of different risk scores for prediction of in-hospital mortality in STEMI patients treated with PPCI. Emerg Med Int 2022; 2022: 5389072, https://doi.org/10.1155/2022/5389072.
84. Ji C., Song F., Huang X., Qu X., Qiu N., Zhu J. Comparison of the predictive value of the modified CADILLAC, GRACE and TIMI risk scores for the risk of short-term death in patients with acute ST segment elevation myocardial infarction after percutaneous coronary intervention. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2023; 35(3): 299–304, https://doi.org/10.3760/cma.j.cn121430-20220727-00696.
85. Ali M., Lange S.A., Wittlinger T., Lehnert G., Rigopoulos A.G., Noutsias M. In-hospital mortality after acute STEMI in patients undergoing primary PCI. Herz 2018; 43(8): 741–745, https://doi.org/10.1007/s00059-017-4621-y.
86. Kocas B.B., Cetinkal G., Kocas C., Arslan S., Abaci O., Dalgic Y., Ser O.S., Batit S., Yildiz A., Dogan S.M. Usefulness of the SYNTAX score II to predict in-hospital and long-term mortality in ST-segment elevation myocardial infarction patients undergoing primary percutaneous coronary intervention. Sisli Etfal Hastan Tip Bul 2022; 56(2): 182–188, https://doi.org/10.14744/SEMB.2021.33410.
87. Saygi M., Tanalp A.C., Tezen O., Pay L., Dogan R., Uzman O., Karabay C.Y., Tanboga I.H., Kacar F.O., Karagoz A. The prognostic importance of the Naples prognostic score for in-hospital mortality in patients with ST-segment elevation myocardial infarction. Coron Artery Dis 2024; 35(1): 31–37, https://doi.org/10.1097/MCA.0000000000001285.
88. Szabó G.T., Ágoston A., Csató G., Rácz I., Bárány T., Uzonyi G., Szokol M., Sármán B., Jebelovszki É., Édes I.F., Czuriga D., Kolozsvári R., Csanádi Z., Édes I., Kőszegi Z. Predictors of hospital mortality in patients with acute coronary syndrome complicated by cardiogenic shock. Sensors (Basel) 2021; 21(3): 969, https://doi.org/10.3390/s21030969.
89. El Farissi M., Zimmermann F.M., De Maria G.L., van Royen N., van Leeuwen M.A.H., Carrick D., Carberry J., Wijnbergen I.F., Konijnenberg L.S.F., Hoole S.P., Marin F., Fineschi M., Pijls N.H.J., Oldroyd K.G., Banning A.P., Berry C., Fearon W.F. The index of microcirculatory resistance after primary PCI: a pooled analysis of individual patient data. JACC Cardiovasc Interv 2023; 16(19): 2383–2392, https://doi.org/10.1016/j.jcin.2023.08.030.
90. Lu X., Lin X., Cai Y., Zhang X., Meng H., Chen W., Yu P., Chen X. Association of the triglyceride-glucose index with severity of coronary stenosis and in-hospital mortality in patients with acute ST elevation myocardial infarction after percutaneous coronary intervention: a multicentre retrospective analysis cohort study. BMJ Open 2024; 14(3): e081727, https://doi.org/10.1136/bmjopen-2023-081727.
91. Huang L., Zhang J., Huang Q., Cui R., Chen J. In-hospital major adverse cardiovascular events after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction: a retrospective study under the China chest pain center (standard center) treatment system. BMC Cardiovasc Disord 2023; 23(1): 198, https://doi.org/10.1186/s12872-023-03214-x.
92. Vatan M.B., Çakmak A.C., Ağaç S., Eynel E., Erkan H. The systemic immune-inflammation index predicts impaired myocardial perfusion and short-term mortality in ST-segment elevation myocardial infarction patients. Angiology 2023; 74(4): 365–373, https://doi.org/10.1177/00033197221106886.
93. Kim K., Kang M.G., Park H.W., Koh J.S., Park J.R., Hwang S.J., Hwang J.Y. Prognostic utility of culprit SYNTAX score in patients with cardiogenic shock complicating ST-segment elevation myocardial infarction. Am J Cardiol 2021; 154: 14–21, https://doi.org/10.1016/j.amjcard.2021.05.035.
94. Biccirè F.G., Farcomeni A., Gaudio C., Pignatelli P., Tanzilli G., Pastori D. D-dimer for risk stratification and antithrombotic treatment management in acute coronary syndrome patients: asystematic review and metanalysis. Thromb J 2021; 19(1): 102, https://doi.org/10.1186/s12959-021-00354-y.
95. Bessonov I.S., Sapozhnikov S.S., Shadrin A.A., Kashtanov M.G., Popov S.V. Effect of the “door-to-balloon” time on the results of treatment of patients with ST-segment elevation myocardial infarction, depending on the duration of the pre-hospital delay. Kardiologiia 2023; 63(6): 28–36, https://doi.org/10.18087/cardio.2023.6.n2245.
96. Wang K., Zeng D., Chen Z., Yu W. Impact of left atrial diameter on all-cause mortality of patients with STEMI undergoing primary percutaneous coronary intervention. Saudi Med J 2023; 44(12): 1260–1268, https://doi.org/10.15537/smj.2023.44.12.20230235.
97. Goins A.E., Rayson R., Caughey M.C., Sola M., Venkatesh K., Dai X., Yeung M., Stouffer G.A. Correlation of infarct size with invasive hemodynamics in patients with ST-elevation myocardial infarction. Catheter Cardiovasc Interv 2018; 92(5): E333–E340, https://doi.org/10.1002/ccd.27625.
98. Frydland M., Møller J.E., Wiberg S., Lindholm M.G., Hansen R., Henriques J.P.S., Møller-Helgestad O.K., Bang L.E., Frikke-Schmidt R., Goetze J.P., Udesen N.L.J., Thomsen J.H., Ouweneel D.M., Obling L., Ravn H.B., Holmvang L., Jensen L.O., Kjaergaard J., Hassager C. Lactate is a prognostic factor in patients admitted with suspected ST-elevation myocardial infarction. Shock 2019; 51(3): 321–327, https://doi.org/10.1097/SHK.0000000000001191.
99. Geltser B.I., Shakhgeldyan K.I., Rublev V.Yu., Domzhalov I.G., Tsivanyuk M.M., Shekunova O.I. Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods. Russian Journal of Cardiology 2023; 28(4): 5302, https://doi.org/10.15829/1560-4071-2023-5302.
100. Paolisso P., Foà A., Bergamaschi L., Angeli F., Fabrizio M., Donati F., Toniolo S., Chiti C., Rinaldi A., Stefanizzi A., Armillotta M., Sansonetti A., Magnani I., Iannopollo G., Rucci P., Casella G., Galiè N., Pizzi C. Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: MINOCA versus MIOCA. Cardiovasc Diabetol 2021; 20(1): 192, https://doi.org/10.1186/s12933-021-01384-6.
101. Zweck E., Thayer K.L., Helgestad O.K.L., Kanwar M., Ayouty M., Garan A.R., Hernandez-Montfort J., Mahr C., Wencker D., Sinha S.S., Vorovich E., Abraham J., O’Neill W., Li S., Hickey G.W., Josiassen J., Hassager C., Jensen L.O., Holmvang L., Schmidt H., Ravn H.B., Møller J.E., Burkhoff D., Kapur N.K. Phenotyping cardiogenic shock. J Am Heart Assoc 2021; 10(14): e020085, https://doi.org/10.1161/JAHA.120.020085.
102. Shetty M.K., Kunal S., Girish M.P., Qamar A., Arora S., Hendrickson M., Mohanan P.P., Gupta P., Ramakrishnan S., Yadav R., Bansal A., Zachariah G., Batra V., Bhatt D.L., Gupta A., Gupta M. Machine learning based model for risk prediction after ST-elevation myocardial infarction: insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry. Int J Cardiol 2002; 362: 6–13, https://doi.org/10.1016/j.ijcard.2022.05.023.

Geltser B.I., Domzhalov I.G., Shakhgeldyan K.I., Kuksin N.S., Kokarev E.A., Pak R.L., Kotelnikov V.N. Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review). Sovremennye tehnologii v medicine 2024; 16(4): 61, https://doi.org/10.17691/stm2024.16.4.07