Evaluation Risk Factors of Coronary Artery Disease Through Competing Risk

Abstract

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.

Keywords

• Coronary artery disease
• Data mining
• Decision trees.