SIGNIFICANCE OF LABORATORY PARAMETERS IN PROGNOSIS OF OUTCOMES OF ACUTE MYOCARDIAL INFARCTION

The study objective is to validate a mathematical model for prognosis of progression of the acute period of myocardial infarction.

Materials and methods. We examined 135 patients with acute Q-wave myocardial infarction of the left ventricle aged between 34 and 88 years (mean age 66, standard deviation 12 years), among them 64 women and 71 men. For prognosis of the outcome of the acute period of myocardial infarction we used an approach based on intellectual data analysis (data mining) in combination with mathematical methods based on decision trees.

Results. Using decision tree algorithms, we singled out laboratory parameters (attributes) which were subsequently used as input. Adequacy of classification of these attributes was determined by a contingency table. Accuracy of the obtained calculation results was 95.56 % demonstrating good agreement between the model and observed data. In a decision tree visualization, the most significant 8 laboratory parameters were determined. Significance of NO₂  metabolite was 24.9 %, triglycerides – 16.7 %, urea – 14.8 %, erythrocytes – 11.2 %, alanine aminotransferase – 9.4 %, very low density lipoproteins – 9.4 %, creatinine – 8.5 %, prothrombin index – 5.1 %. In the Rules tab, only rules 4 and 9 can be used with confidence, because their confidence level approaches 100 %, and effect cost for the fact of death was 33.59 % and 32.03 %, respectively.

Conclusion. Using a decision tree algorithm, we determined prognostically significant factors for progression of acute myocardial infarction. The following set of parameters predicts unfavorable outcome (death) with 95.56 % accuracy: NO₂  < level 22.755 mmol/l, triglycerides ≥ 1.565 mmol/l, erythrocytes < 4.91 M/uL, alanine aminotransferase < 1.23 mmol/l, urea < 7.05 mmol/l, very low-density lipoproteins < 0.965 mmol/l, creatinine ≥ 91.55 µmol/l, NO₂ level ≥ 22.755 mmol/l predicts a favorable outcome with 95.56 % accuracy. 

1. Kuz’michev D.E., Vil’tsev I.M., Skrebov R.V. et al. Myocardial infarction in clinic. Problemy ekspertizy v meditsine = Problems of Expertise in Medicine 2015;15(1–2):49–51. (In Russ.).

2. Narne P., Ponnaluri K.C., Singh S. et al. Association of the genetic variants of endothelial nitric oxide synthase gene with angiographically defined coronary artery disease and myocardial infarction in South Indian patients with type 2 diabetes mellitus. J Diabetes Complications 2013;27(3):255–61. DOI: 10.1016/j.jdiacomp. 2012.10.009.

3. Lapshina L.A., Kravchun P.G., Titova A.Ju., Glebova O.V. Significance of determination of nitritenitrate as markers of endothelial dysfunction in cardiovascular disease. Ukrains’kiy medichnyy chasopis =Ukrainian Medical Journal 2009;6(74):49–53. (In Russ.).

4. Belaya I.E. Markers of oxidativestress in patients with acute myocardial infarction combined with nonalcoholic fatty liver disease. Rossiyskiy kardiologicheskiy zhurnal = Russian Journal of Cardiology 2014;7(111): 98–104. (In Russ.).

5. Zalevskaya N.G. Modern methods of laboratory confirmation of myocardial infarction. Nauchnye vedomosti BelGU. Seriya: Medicina. Farmatsiya = Belgorod State University Scientific Bulletin. Series: Medicine. Pharmacy 2011;10(105):260–7. (In Russ.).

6. СSilivonchik N.N. Statins and liver: safety. Meditsinskie novosti = Medical News 2011;(8):24–30. (In Russ.).

7. Arutyunov G.P., Oganezova L.G. Acute myocardial infarction and renal function. Klinicheskaya nefrologiya = Clinical Nephrology 2010;(3):39–44. (In Russ.).

8. Tarasova S.A. Prediction in clinical medicine. In: Materials of the XXX International Scientific-Practical Conference “Innovations in Science” N 2 (27). Part II. Novosibirsk: SibAK, 2014. Pp. 57–60. (In Russ.)].

9. Zolin A.G., Silaeva A.Yu. Application of neural nets in medicine. Actual problems of science, economics and education of XXI century: Materials II International Scientific-Practical Conference, March 5 – September 26, 2012. Samara: Samarskij institut RGTEU, 2012. Pp. 264–71. (In Russ.)].

10. Barsegyan A.A., Kupriyanov M.S., Stepanenko V.V., Kholod I.I. Methods and models of data analysis: OLAP and Data Mining. Saint Petersburg: BKHV-Peterburg, 2004. (In Russ.).

11. Belaya I.E., Kolomiets V.I., Musaeva E.K. Prognostic markers for outcome of myocardial infarction in the acute phase. Medicina neotlozhnykh sostoyaniy = Emergency Medicine 2016;1(72):92–7. (In Russ.).

12. Brilakis E.S., Mavrogiorgos N.C., Kopecky S.L. et al. Validation of the TIMI risk score for ST-elevation acute myocardial infarction in a communitybased coronary care unit registry. Circulation 2001;104(Suppl. II):380.

13. Baptista S.B., Loureiro J.R., Thomas B. et al. PAMI risk score for mortality prediction in acute myocardial infarction treated with primary angioplasty. Rev Port Cardiol 2004;23(5):683–93. PMID: 15279453.

14. Halkin A., Singh M., Nikolsky E. et al. Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score. J Am Coll Cardiol 2005;45(9):1397–405. DOI: 10.1016/j.jacc.2005.01.041. PMID: 15862409.

15. Shmidt E.A., Berns S.A., Osokina A.V. et al. A poor outcome prediction scale in ST-segment elevation myocardial infarction patients undergoing emergency percutaneous coronary intervention. Тerapevticheskiy arkhiv = Therapeutic Archive 2014;86(4):13–8. (In Russ.).

16. Rebrova O.Yu. Statistical analysis of medical date. The using of Statistica program package. Moscow: MediaSfera, 2003. (In Russ.).

17. Task Force on the management of STsegment elevation acute myocardial infarction of the European Society of Cardiology (ESC), Steg P.G., James S.K. et al. ESC Guidelines for the management of acute myocardial infarction in patients presenting with STsegment elevation. Eur Heart J 2012;33 (20):2569–619. DOI: 10.1093/eurheartj/ehs215.

18. Novochikhina I.A., Alekseeva O.P. The significance of saliva’s fatty acids spectrum changes in course prediction of acute coronary syndrome. Nizhegorodskij medicinskij zhurnal = Nizhniy Novgorod Medical Journal 2001;(1):14–7. (In Russ.).

19. Semakina S.V., Glezer M.G. Clinicoanamnestic and laboratoryinstrumental characteristics of myocardial infarction and early posthospital prognosis for a disease at men and women. Problemy zhenskogo zdorov’ya = Women’s Health Issues 2010;5(2):12–21. (In Russ.).

20. Valueva S.V. Predictors oftwo-yearprognosis of patients mortality, whichhad amyocardial infarction, complicated by left ventricle aneurism for essential hypertension. Ukrains’kiy kardiologichny’y zhurnal = Ukrainian Cardiology Journal 2010;(5):19–22. (In Ukr.).

21. Klyuzhev V.M., Ardashev V.N., Litvinov A.M. et al. The method ofpredictionof patient’s complication bymyocardial infarction. Patent No 2197173. 2003. Available at: http://www.findpatent.ru/patent/219/2197173. html. (In Russ.).

22. Gordienko A.V., Novikov V.A., Grechanik P.M. et al. The possibilities of prediction the outcomes of myocardial infarction by heart rhythm characteristics. Voennomeditsinskiy zhurnal = Military Medical Journal 2009;330(4):77–8. (In Russ.).

23. Uskov V.M. The application of neural modeling in the diagnostics of myocardial infarct on complications. High technology in technology, medicine and education. Voronezh: VGTU, 2003. (In Russ.).

24. Sazonova N.S., Susnina A.V., Yakushin S.S. The method of prediction of producing capacity of recurrentmyocardial infarction. Patent No 2197889. 2003. Available at: http://bd.patent.su/2197000–2197999/pat/servl/servlet1ad1.html. (In Russ.).