Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Autoři: Joon-myoung Kwon aff001;  Ki-Hyun Jeon aff002;  Hyue Mee Kim aff003;  Min Jeong Kim aff003;  Sungmin Lim aff003;  Kyung-Hee Kim aff002;  Pil Sang Song aff003;  Jinsik Park aff003;  Rak Kyeong Choi aff003;  Byung-Hee Oh aff003
Působiště autorů: Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea aff001;  Artificial intelligence and big data center, Sejong medical research institute, Gyeonggi, Korea aff002;  Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224502



Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI).


The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data.


In-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902–0.909] and 0.870 [0.865–0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846–0.856], 0.810 [0.803–0.819]), ACTION (0.852 [0.847–0.857], 0.806 [0.799–0.814] and TIMI score (0.781 [0.775–0.787], 0.593[0.585–0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p < 0.001).


The DAMI predicted in-hospital mortality and 12-month mortality of AMI patients more accurately than the existing risk scores and other machine-learning methods.

Klíčová slova:

Antiplatelet therapy – Blood pressure – Cardiac arrest – Creatinine – Death rates – Forecasting – Myocardial infarction – Prognosis


1. Rahimi K, Duncan M, Pitcher A, Emdin CA, Goldacre MJ. Mortality from heart failure, acute myocardial infarction and other ischaemic heart disease in England and Oxford: a trend study of multiple-cause-coded death certification. J Epidemiol Community Health. 2015;69: 1000–5. doi: 10.1136/jech-2015-205689 26136081

2. Moran AE, Tzong KY, Forouzanfar MH, Rothy GA, Mensah GA, Ezzati M, et al. Variations in ischemic heart disease burden by age, country, and income: the Global Burden of Diseases, Injuries, and Risk Factors 2010 study. Glob Heart. 2014;9: 91–9. doi: 10.1016/j.gheart.2013.12.007 24977114

3. Pilgrim T, Vranckx P, Valgimigli M, Stefanini GG, Piccolo R, Rat J, et al. Risk and timing of recurrent ischemic events among patients with stable ischemic heart disease, non-ST-segment elevation acute coronary syndrome, and ST-segment elevation myocardial infarction. Am Heart J. 2016;175: 56–65. doi: 10.1016/j.ahj.2016.01.021 27179724

4. Arnold S V, Smolderen KG, Kennedy KF, Li Y, Shore S, Stolker JM, et al. Risk factors for rehospitalization for acute coronary syndromes and unplanned revascularization following acute myocardial infarction. J Am Heart Assoc. 2015;4. doi: 10.1161/JAHA.114.001352 25666368

5. Yanishi K, Nakamura T, Nakanishi N, Yokota I, Zen K, Yamano T, et al. A Simple Risk Stratification Model for ST-Elevation Myocardial Infarction (STEMI) from the Combination of Blood Examination Variables: Acute Myocardial Infarction-Kyoto Multi-Center Risk Study Group. PLoS One. 2016;11: e0166391. doi: 10.1371/journal.pone.0166391 27835698

6. 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: 835–42. Available: doi: 10.1001/jama.284.7.835 10938172

7. Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van de Werf F, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA. 2004;291: 2727–33. doi: 10.1001/jama.291.22.2727 15187054

8. McNamara RL, Kennedy KF, Cohen DJ, Diercks DB, Moscucci M, Ramee S, et al. Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction. J Am Coll Cardiol. 2016;68: 626–635. doi: 10.1016/j.jacc.2016.05.049 27491907

9. Sun G, Shook TL, Kay GL. Inappropriate Use of Bivariable Analysis to Screen Risk Factors for Use in Multivariable Analysis. 1996;49: 907–916. doi: 10.1016/0895-4356(96)00025-x 8699212

10. Breiman L. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Stat Sci. 2001;16: 199–231. doi: 10.1214/ss/1009213726

11. Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001;54: 979–85. Available: doi: 10.1016/s0895-4356(01)00372-9 11576808

12. Mining D. Data Mining: Statistics and More? 1998;52.

13. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama. 2016;304: 649–656. doi: 10.1001/jama.2016.17216 27898976

14. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. Nature Publishing Group; 2017;542: 115–118. doi: 10.1038/nature21056 28117445

15. Kwon J-M, Lee Y, Lee Y, Lee S, Park J. An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest. J Am Heart Assoc. 2018;7: e008678. doi: 10.1161/JAHA.118.008678 29945914

16. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521: 436–444. doi: 10.1038/nature14539 26017442

17. Sim DS, Jeong MH, Kang JC. Current management of acute myocardial infarction: Experience from the Korea Acute Myocardial. J Cardiol. Japanese College of Cardiology; 2010;56: 1–7. doi: 10.1016/j.jjcc.2010.04.002 20554156

18. Schalkoff RJ. Pattern recognition—statistical, structural and neural approaches. [Internet]. New York: Wiley; 1992. Available:

19. Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 2015; doi: 10.1007/s13398-014-0173-7.2

20. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J Mach Learn Res. 2014;15: 1929–1958. doi: 10.1214/12-AOS1000

21. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: A System for Large-Scale Machine Learning TensorFlow: A system for large-scale machine learning. 12th USENIX Symp Oper Syst Des Implement (OSDI ‘16). 2016; 265–284. doi: 10.1038/nn.3331

22. Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. 2017 IEEE Int Conf Consum Electron ICCE 2017. 2014; 434–435. doi: 10.1109/ICCE.2017.7889386

23. Nair V, Hinton GE. Rectified Linear Units Improve Restricted Boltzmann Machines. Proc 27th Int Conf Mach Learn. 2010; 807–814. doi:

24. Jayalakshmi T, Santhakumaran A. Statistical Normalization and Backpropagation for Classification. Int J Comput Theory Eng. 2011;3: 89–93. doi: 10.7763/IJCTE.2011.V3.288

25. RUMELHART DE, HINTON GE, WILLIAMS RJ. Learning Internal Representations by Error Propagation. Readings in Cognitive Science. Elsevier; 1988. pp. 399–421. doi: 10.1016/B978-1-4832-1446-7.50035–2

26. Kuhn M, Johnson K. Applied Predictive Modeling [Internet]. New York, NY: Springer New York; 2013. doi: 10.1007/978-1-4614-6849-3

27. Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Crit Care Med. 2016;44: 368–74. doi: 10.1097/CCM.0000000000001571 26771782

28. Shouval R, Hadanny A, Shlomo N, Iakobishvili Z, Unger R, Zahger D, et al. Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: An Acute Coronary Syndrome Israeli Survey data mining study. Int J Cardiol. Elsevier Ireland Ltd; 2017;246: 7–13. doi: 10.1016/j.ijcard.2017.05.067 28867023

29. Liaw a, Wiener M. Classification and Regression by randomForest. R news. 2002;2: 18–22. doi: 10.1177/154405910408300516

30. Khalilia M, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak. BioMed Central Ltd; 2011;11: 51. doi: 10.1186/1472-6947-11-51 21801360

31. Calcagno V, Mazancourt C De. glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models. J Stat Softw. 2010;34: 1–29. doi: 10.18637/jss.v034.i12

32. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27: 861–874. doi: 10.1016/j.patrec.2005.10.010

33. Elbarouni B, Goodman SG, Yan RT, Welsh RC, Kornder JM, Deyoung JP, et al. Validation of the Global Registry of Acute Coronary Event (GRACE) risk score for in-hospital mortality in patients with acute coronary syndrome in Canada. Am Heart J. 2009;158: 392–9. doi: 10.1016/j.ahj.2009.06.010 19699862

34. Fox KAA, Fitzgerald G, Puymirat E, Huang W, Carruthers K, Simon T, et al. 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: e004425. doi: 10.1136/bmjopen-2013-004425 24561498

35. D’Ascenzo F, Biondi-Zoccai G, Moretti C, Bollati M, Omedè P, Sciuto F, et al. TIMI, GRACE and alternative risk scores in Acute Coronary Syndromes: a meta-analysis of 40 derivation studies on 216,552 patients and of 42 validation studies on 31,625 patients. Contemp Clin Trials. 2012;33: 507–14. doi: 10.1016/j.cct.2012.01.001 22265976

36. Yan YL, Qiu B, Hu LJ, Jing XD, Liu YJ, Deng SB, et al. Efficacy and safety evaluation of intensive statin therapy in older patients with coronary heart disease: a systematic review and meta-analysis. Eur J Clin Pharmacol. 2013;69: 2001–9. doi: 10.1007/s00228-013-1570-0 23942733

37. Bohula EA, Giugliano RP, Cannon CP, Zhou J, Murphy SA, White JA, et al. Achievement of dual low-density lipoprotein cholesterol and high-sensitivity C-reactive protein targets more frequent with the addition of ezetimibe to simvastatin and associated with better outcomes in IMPROVE-IT. Circulation. 2015;132: 1224–33. doi: 10.1161/CIRCULATIONAHA.115.018381 26330412

38. Wallentin L, Becker RC, Budaj A, Cannon CP, Emanuelsson H, Held C, et al. Ticagrelor versus clopidogrel in patients with acute coronary syndromes. N Engl J Med. 2009;361: 1045–57. doi: 10.1056/NEJMoa0904327 19717846

39. Roe MT, Armstrong PW, Fox KAA, White HD, Prabhakaran D, Goodman SG, et al. Prasugrel versus clopidogrel for acute coronary syndromes without revascularization. N Engl J Med. 2012;367: 1297–309. doi: 10.1056/NEJMoa1205512 22920930

40. Song PS, Ryu DR, Kim MJ, Jeon KH, Choi RK, Park JS, et al. Risk Scoring System to Assess Outcomes in Patients Treated with Contemporary Guideline-Adherent Optimal Therapies after Acute Myocardial Infarction. Korean Circ J. 2018;48: 492–504. doi: 10.4070/kcj.2017.0128 29856143

41. Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35: 1798–1828. doi: 10.1109/TPAMI.2013.50 23787338

42. Mo D. A survey on deep learning: One small step toward AI. Tech Report, Univ New Mex Dept Comput Sci. 2012; 1–16. Available:

43. Bengio Y, {LeCun} Y, Lecun Y. Scaling Learning Algorithms towards AI. Large Scale Kernel Mach. 2007; 321–360. doi:

44. Hall MA, Holmes G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng. 2003;15: 1437–1447. doi: 10.1109/TKDE.2003.1245283

45. Wolpert DH. The Supervised Learning No-Free-Lunch Theorems. Proc 6th Online World Conf Soft Comput Ind Appl. 2001; 10–24. Available:

46. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. 2016; doi: 10.1007/978-3-319-16817-3

47. Fong RC, Vedaldi A. Interpretable Explanations of Black Boxes by Meaningful Perturbation. Proc IEEE Int Conf Comput Vis. 2017;2017-Octob: 3449–3457. doi: 10.1109/ICCV.2017.371

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