Volume 21, Issue 4 (8-2018)                   J Arak Uni Med Sci 2018, 21(4): 18-29 | Back to browse issues page

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Salehi E, Hagizadeh E, Alidoosti M. Evaluation Risk Factors of Coronary Artery Disease Through Competing Risk Tree . J Arak Uni Med Sci 2018; 21 (4) :18-29
URL: http://jams.arakmu.ac.ir/article-1-5424-en.html
1- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
2- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. , hajizadeh@modares.ac.ir
3- Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
Abstract:   (3499 Views)
Background and Aim: Advances in the field of medicine over the past few decades enabled the identification of risk factors that may contribute toward the development of coronary artery disease (CHD). However, this knowledge has not yet helped in the significant reduction of CHD incidence. The purpose of this study is to assess the risk factors of coronary artery heart events, after receiving stent, by competing risks with composite events tree. We can reduce CHD incidence with control of this risk factors.
Materials and Methods: This sectional study includes the Coronary Artery Disease (CAD) patients that received Percutaneous Coronary Intervention (PCI) cure with at least planting one stent from May 21, 2007 to May 22, 2009 in Tehran heart center. We followed patients for three years. Revascularization, nonfatal myocardial infarction, and cardiac death are considered as major acute cardiovascular events (outcome). We used decision tree with competing risks with composite events model for classification of patients. The data were analyzed by IBM SPSS Statistics 24 and R 3.3.3 softwares.
Findings: Four factors including fasting blood sugar, diabetes mellitus, body mass index and age established six homogeneous subgroups of patients for nonfatal myocardial infarction and revascularization. Maximum Revascularization incidence after 50 months was 17.8% and Maximum Nonfatal myocardial infarction was 9.7%.
Conclusion: CAD patients can reduce serious cardiac events by controling their weight and diabetes status, after receiving stent.
Full-Text [PDF 2458 kb]   (2127 Downloads)    
Type of Study: Original Atricle | Subject: Cardiology
Received: 2017/11/4 | Accepted: 2018/06/11

References
1. Clark JC, Lan VM. Heart failure patient learning needs after hospital discharge. Applied Nursing Research. 2004; 17(3):150-7.
2. Gaziano TA. Cardiovascular disease in the developing world and its cost-effective management. Circulation. 2005; 112(23): 3547-53.
3. Ramezani A, Djazayeri A, Koohdani F, Nematipour E, Javanbakht MH, Keshavarz SA, et al. Omega-3 fatty acids/vitamin e behave synergistically on adiponectin receptor-1 and adiponectin receptor-2 gene expressions in peripheral blood mononuclear cell of coronary artery disease patients. Current Topics in Nutraceuticals Research. 2015; 13(1):23.
4. Ramezani A, Koohdani F, Djazayeri A, Nematipour E, Keshavarz SA, Saboor-Yaraghi A-A, et al. Effects of administration of omega-3 fatty acids with or without vitamin E supplementation on adiponectin gene expression in PBMCs and serum adiponectin and adipocyte fatty acid-binding protein levels in male patients with CAD. Anatolian journal of cardiology. 2016; 15(12):981.
5. Fakhrzadeh H, Larijani B, Bandarian F, Adibi H, Samavat T, Malek Afzali H, et al. The relationship between ischemic heart disease and coronary risk factors in population aged over 25 in Qazvin: A population-based study. J Qazvin Univ Med Sci. 2005; 35(9):26-34.
6. Euroaspire I. Lifestyle and risk-factor management and use of drug therapies in coronary patients from 15 countries. European heart journal. 2001; 22(7):554-72.
7. Group ES. EUROASPIRE: A European Society of Cardiology survey of secondary prevention of coronary heart disease: Principal results. European Heart Journal. 1997; 18(10):1569-.
8. Kotseva K, Wood D, Backer GD, Bacquer DD, Pyörälä K, Keil U, et al. EUROASPIRE III: a survey on the lifestyle, risk factors and use of cardioprotective drug therapies in coronary patients from 22 European countries. European Journal of Cardiovascular Prevention & Rehabilitation. 2009; 16(2):121-37.
9. Kannel WB. Contributions of the Framingham Study to the conquest of coronary artery disease. American Journal of Cardiology. 1988; 62(16):1109-12.
10. Marshall T. Identification of patients for clinical risk assessment by prediction of cardiovascular risk using default risk factor values. BMC Public Health. 2008; 8(1):25.
11. Ibrahim N, Kudus A. Decision tree for prognostic classification of multivariate survival data and competing risks. 2009.
12. Leblanc M, Crowley J. Survival Trees by Goodness of Split. Journal of the American Statistical Association. 1993; 88(422): 457-67.
13. Schumacher M, Hollander N, Schwarzer G, Sauerbrei W. Handbook of Statistics in Clinical Oncology, 2006.
14. Crowley J, Hoering A. Handbook of statistics in clinical oncology: CRC Press; 2012.
15. Pallara A. Binary decision trees approach to classification: a review of CART and other methods with some applications to real data. Statistica Applicata. 1992; 4(3):255-85.
16. Tsien CL, Fraser H, Long WJ, Kennedy RL. Using classification tree and logistic regression methods to diagnose myocardial infarction. Medinfo. 1998; 98.
17. Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications. 2011; 17(8):43-8.
18. Rao RB, Krishnan S, Niculescu RS. Data mining for improved cardiac care. ACM SIGKDD Explorations Newsletter. 2006; 8(1):3-10.
19. Zavrsnik J, Kokol P, Maleiae I, Kancler K, Mernik M, Bigec M. ROSE: decision trees, automatic learning and their applications in cardiac medicine. Medinfo MEDINFO. 1994; 8:1688.
20. Luo X, Turnbull BW. Comparing two treatments with multiple competing risks endpoints. Statistica Sinica. 1999: 985-97.
21. Loh W-Y. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2011; 1(1):14-23.
22. Terada T, Johnson JA, Norris C, Padwal R, Qiu W, Sharma AM, et al. Body Mass Index Is Associated with Differential Rates of Coronary Revascularization After Cardiac Catheterization. The Canadian journal of cardiology. 2017; 33(6):822-9.
23. Poirier P, McCrindle BW, Leiter LA. Obesity—It Must Not Remain the Neglected Risk Factor in Cardiology. Canadian Journal of Cardiology. 2015; 31(2):105-8.
24. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health. 2009; 9(1):88.
25. Engelgau MM, Geiss LS, Saaddine JB, et al. THe evolving diabetes burden in the united states. Annals of Internal Medicine. 2004; 140(11):945-50.
26. Hassan A, Newman A, Ko DT, Rinfret S, Hirsch G, Ghali WA, et al. Increasing rates of angioplasty versus bypass surgery in Canada, 1994-2005. American Heart Journal. 2010; 160(5):958-65.
27. Fukumoto R, Kawai M, Minai K, Ogawa K, Yoshida J, Inoue Y, et al. Conflicting relationship between age-dependent disorders, valvular heart disease and coronary artery disease by covariance structure analysis: Possible contribution of natriuretic peptide. PloS one. 2017; 12(7): e0181206.
28. Zhang XG, Zhang YQ, Zhao DK, Wu JX, Zhao J, Jiao XM, et al. Relationship between blood glucose fluctuation and macrovascular endothelial dysfunction in type 2 diabetic patients with coronary heart disease. European review for medical and pharmacological sciences. 2014; 18(23):3593-600.
29. Ordonez C, editor Comparing association rules and decision trees for disease prediction. Proceedings of the international workshop on Healthcare information and knowledge management; 2006: ACM.
30. Ordonez C, Omiecinski E, De Braal L, Santana CA, Ezquerra N, Taboada JA, et al., editors. Mining constrained association rules to predict heart disease. Data Mining, 2001 ICDM 2001, Proceedings IEEE International Conference on; 2001: 433-440. IEEE.

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