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DOI: 10.1055/a-1863-1589
Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches
Funding None.![](https://www.thieme-connect.de/media/10.1055-s-00035026/202203/lookinside/thumbnails/10-1055-a-1863-1589_210273r-1.jpg)
Abstract
Background Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.
Objective The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome.
Methods To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist “Quality assessment of machine learning studies” was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence.
Results In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice.
Conclusion Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
Keywords
machine learning - mortality - acute coronary syndrome - myocardial infarction - prediction - major adverse cardiac eventAuthor Contributions
All authors made significant contributions to the manuscript. S.C. developed the design of the scoping review and was involved in the data screening and extraction with S.S.. S.C. conducted the medical evaluation of the included studies, and wrote the manuscript. F.S. and R.B. were involved in the medical assessment of the included studies. F.S. supervised and guided the project. S.S. and S.C. categorized the biomarkers and variables that extracted from findings. All authors provided critical revision and approved the manuscript.
Protection of Human and Animal Subjects
The current study was approved by the Human Research Ethics Committee (ethics code IR.IUMS.REC.1398.948), Iran University of Medical Sciences.
Publikationsverlauf
Eingereicht: 06. November 2021
Angenommen: 24. Mai 2022
Accepted Manuscript online:
26. Mai 2022
Artikel online veröffentlicht:
27. Juli 2022
© 2022. Thieme. All rights reserved.
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References
- 1 Willim HA, Harianto JC, Cipta H. Platelet-to-lymphocyte ratio at admission as a predictor of in-hospital and long-term outcomes in patients with st-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: a systematic review and meta-analysis. Cardiol Res 2021; 12 (02) 109-116
- 2 Paul K, Mukherjee S, Ghosh S. Evaluation and outcome of patients of STEMI with acute total occlusion of coronary artery in the setting of primary PCI, pharmaco invasive PCI and delayed PCI. J Cardiol Cardiovasc Ther 2018; 12 (05) 104-109
- 3 Quan XQ, Wang RC, Zhang Q, Zhang CT, Sun L. The predictive value of lymphocyte-to-monocyte ratio in the prognosis of acute coronary syndrome patients: a systematic review and meta-analysis. BMC Cardiovasc Disord 2020; 20 (01) 338-338
- 4 D'Ascenzo F, De Filippo O, Gallone G. et al; 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
- 5 Mozaffarian D, Benjamin EJ, Go AS. et al. Executive summary: heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation 2015; 131 (04) 434-441
- 6 Kwon JM, Jeon KH, Kim HM. et al. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PLoS One 2019; 14 (10) e0224502
- 7 Virani SS, Alonso A, Benjamin EJ. et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2020 update: a report from the American Heart Association. Circulation 2020; 141 (09) e139-e596
- 8 Li Y, Sperrin M, Ashcroft DM, van Staa TP. Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar. BMJ 2020; 371: m3919
- 9 Sherazi SWA, Jeong YJ, Jae MH, Bae JW, Lee JY. A machine learning-based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome. Health Informatics J 2020; 26 (02) 1289-1304
- 10 Sherazi SWA, Bae JW, Lee JY. A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome. PLoS One 2021; 16 (06) e0249338
- 11 Hautamäki M, Lyytikäinen L-P, Mahdiani S. et al. The association between charlson1 comorbidity index and mortality in acute coronary syndrome—the MADDEC study. Scand Cardiovasc J 2020; 54 (03) 146-152
- 12 Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS One 2017; 12 (04) e0174944
- 13 Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. Am J Epidemiol 2019; 188 (12) 2222-2239
- 14 Bazoukis G, Stavrakis S, Zhou J. et al. Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review. Heart Fail Rev 2021; 26 (01) 23-34
- 15 Banerjee A, Chen S, Fatemifar G. et al. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021; 19 (01) 85
- 16 Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110: 12-22
- 17 Maadi M, Akbarzadeh Khorshidi H, Aickelin U. A review on human-AI interaction in machine learning and insights for medical applications. Int J Environ Res Public Health 2021; 18 (04) 2121
- 18 Mehyadin AE, Abdulazeez AM. Classification based on semi-supervised learning: a review. Iraqi Journal for Computers and Informatics 2021; 47 (01) 1-11
- 19 Ldahiri A, Alrashed B, Hussain W. Trends in using IoT with machine learning in health prediction system. Forecast 2021; 3 (01) 181-206
- 20 Ganguli I, Gordon WJ, Lupo C. et al. Machine learning and the pursuit of high-value health care. NEJM Catal 2020; 1 (06) 1-14
- 21 Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015; 349 (6245): 255-260
- 22 Cho SM, Austin PC, Ross HJ. et al. Machine learning compared with conventional statistical models for predicting myocardial infarction readmission and mortality: a systematic review. Can J Cardiol 2021; 37 (08) 1207-1214
- 23 Johnson KW, Torres Soto J, Glicksberg BS. et al. Artificial intelligence in cardiology. J Am Coll Cardiol 2018; 71 (23) 2668-2679
- 24 Qiao N. A systematic review on machine learning in sellar region diseases: quality and reporting items. Endocr Connect 2019; 8 (07) 952-960
- 25 Andaur Navarro CL, Damen JAA, Takada T. et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol 2022; 22 (01) 12
- 26 Shameer K, Johnson KW, Yahi A. et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort. Pac Symp Biocomput 2017; 22: 276-287
- 27 Benedetto U, Dimagli A, Sinha S. et al. Machine learning improves mortality risk prediction after cardiac surgery: systematic review and meta-analysis. J Thorac Cardiovasc Surg 2022; 163 (06) 2075-2087.e9
- 28 Khera R, Haimovich J, Hurley NC. et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol 2021; 6 (06) 633-641
- 29 Bai Z, Lu J, Li T. et al. Clinical feature-based machine learning model for 1-year mortality risk prediction of st-segment elevation myocardial infarction in patients with hyperuricemia: a retrospective study. Comput Math Methods Med 2021; 2021: 7252280
- 30 Lopez C, Tucker S, Salameh T, Tucker C. An unsupervised machine learning method for discovering patient clusters based on genetic signatures. J Biomed Inform 2018; 85: 30-39
- 31 Womack DM, Hribar MR, Steege LM, Vuckovic NH, Eldredge DH, Gorman PN. Registered nurse strain detection using ambient data: an exploratory study of underutilized operational data streams in the hospital workplace. Appl Clin Inform 2020; 11 (04) 598-605
- 32 Shah M, Shu D, Prasath VBS, Ni Y, Schapiro AH, Dufendach KR. Machine learning for detection of correct peripherally inserted central catheter tip position from radiology reports in infants. Appl Clin Inform 2021; 12 (04) 856-863
- 33 Arfat Y, Mittone G, Esposito R, Cantalupo B, DE Ferrari GM, Aldinucci M. Machine learning for cardiology. Minerva Cardiol Angiol 2022; 70 (01) 75-91
- 34 Raza K, Singh NK. A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging 2021; 17 (09) 1059-1077
- 35 Hu D, Dong W, Lu X, Duan H, He K, Huang Z. Evidential MACE prediction of acute coronary syndrome using electronic health records. BMC Med Inform Decis Mak 2019; 19 (Suppl. 02) 61
- 36 Pieszko K, Hiczkiewicz J, Budzianowski P. et al. Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes. J Transl Med 2018; 16 (01) 334
- 37 Hu P, Xia E, Li S. et al. Network-based prediction of major adverse cardiac events in acute coronary syndromes from imbalanced EMR data. Stud Health Technol Inform 2019; 264: 1480-1481
- 38 Lin SD, Chen L, Chen W. Thermal face recognition under different conditions. BMC Bioinformatics 2021; 22 (5, suppl 5): 313
- 39 Chen C-H, Tanaka K, Kotera M, Funatsu K. Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications. J Cheminform 2020; 12 (01) 19
- 40 Lee W, Lee J, Woo SI. et al. Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction. Sci Rep 2021; 11 (01) 12886
- 41 Hernesniemi JA, Mahdiani S, Tynkkynen JA. et al. Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study. Ann Med 2019; 51 (02) 156-163
- 42 Piros P, Ferenci T, Fleiner R. et al. Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry. Knowl Base Syst 2019; 179: 1-7
- 43 Borracci RA, Higa CC, Ciambrone G, Gambarte J. Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination. Arch Cardiol Mex 2021; 91 (01) 58-65
- 44 Payrovnaziri SN, Barrett LA, Bis D, Bian J, He Z. Enhancing prediction models for one-year mortality in patients with acute myocardial infarction and post myocardial infarction syndrome. Stud Health Technol Inform 2019; 264: 273-277
- 45 Raza SA, Thalib L, Al Suwaidi J. et al. Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine-learning methods. Expert Syst 2019; 36 (04) e12413
- 46 Itzahki Ben Zadok O, Ben-Gal T, Abelow A. et al. Temporal trends in the characteristics, management, and outcomes of patients with acute coronary syndrome according to their Killip class. Am J Cardiol 2019; 124 (12) 1862-1868
- 47 Hashmi KA, Adnan F, Ahmed O. et al. Risk assessment of patients after st-segment elevation myocardial infarction by Killip classification: an institutional experience. Cureus 2020; 12 (12) e12209
- 48 Shin S, Austin PC, Ross HJ. et al. Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality. ESC Heart Fail 2021; 8 (01) 106-115
- 49 Aziz F, Malek S, Ibrahim KS. et al. Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach. PLoS One 2021; 16 (08) e0254894
- 50 Kim YJ, Saqlian M, Lee JY. Deep learning-based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining. Pers Ubiquitous Comput 2019; 26: 259-267
- 51 Salehnasab C, Hajifathali A, Asadi F, Roshandel E, Kazemi A, Roshanpoor A. Machine learning classification algorithms to predict AGVHD following allo-HSCT: a systematic review. Methods Inf Med 2019; 58 (06) 205-212
- 52 Layeghian Javan S, Sepehri MM, Aghajani H. Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework. J Biomed Inform 2018; 88: 70-89
- 53 Bala W, Steinkamp J, Feeney T. et al. A web application for adrenal incidentaloma identification, tracking, and management using machine learning. Appl Clin Inform 2020; 11 (04) 606-616
- 54 Bartlett P, Freund Y, Lee WS. et al. Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 1998; 26 (05) 1651-1686
- 55 Subasi A. Machine learning techniques. In: Subasi A. ed. Practical Machine Learning for Data Analysis Using Python. Academic Press; 2020: 91-202
- 56 Mangold C, Zoretic S, Thallapureddy K, Moreira A, Chorath K, Moreira A. Machine learning models for predicting neonatal mortality: a systematic review. Neonatology 2021; 118 (04) 394-405
- 57 Zhang Z, Beck MW, Winkler DA, Huang B, Sibanda W, Goyal H. written on behalf of AME Big-Data Clinical Trial Collaborative Group. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann Transl Med 2018; 6 (11) 216
- 58 Teng AK, Wilcox AB. A review of predictive analytics solutions for sepsis patients. Appl Clin Inform 2020; 11 (03) 387-398
- 59 Poon AIF, Sung JJY. Opening the black box of AI-Medicine. J Gastroenterol Hepatol 2021; 36 (03) 581-584
- 60 Sariyar M, Holm J. Medical Informatics in a tension between black-box AI and trust. Stud Health Technol Inform 2022; 289 (289) 41-44
- 61 Stewart J, Lu J, Goudie A. et al. Applications of machine learning to undifferentiated chest pain in the emergency department: a systematic review. PLoS One 2021; 16 (08) e0252612
- 62 Korach ZT, Cato KD, Collins SA. et al. Unsupervised machine learning of topics documented by nurses about hospitalized patients prior to a rapid-response event. Appl Clin Inform 2019; 10 (05) 952-963
- 63 Myers PD, Huang W, Anderson F, Stultz CM. Choosing clinical variables for risk stratification post-acute coronary syndrome. Sci Rep 2019; 9 (01) 14631
- 64 Li X, Liu H, Yang J, Xie G, Xu M, Yang Y. Using machine learning models to predict in-hospital mortality for ST-elevation myocardial infarction patients. Stud Health Technol Inform 2017; 245: 476-480
- 65 Lee HC, Park JS, Choe JC. et al; 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
- 66 Li YM, Jiang LC, He JJ, Jia KY, Peng Y, Chen M. Machine learning to predict the 1-year mortality rate after acute anterior myocardial infarction in Chinese patients. Ther Clin Risk Manag 2020; 16: 1-6
- 67 Duan H, Sun Z, Dong W, Huang Z. Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome. BMC Med Inform Decis Mak 2019; 19 (01) 5
- 68 Mansoor H, Elgendy IY, Segal R, Bavry AA, Bian J. Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: a machine learning approach. Heart Lung 2017; 46 (06) 405-411
- 69 Hu D, Huang Z, Chan TM, Dong W, Lu X, Duan H. Utilizing Chinese admission records for mace prediction of acute coronary syndrome. Int J Environ Res Public Health 2016; 13 (09) 912