It has been established that the deadliest disease in the world is the cardiovascular disease (CVD) (1,2). Known as a group of disorders of the heart and blood vessels, CVD include coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease and congenital heart disease (1). Over the last decade, global number of deaths from CVD has increased by 12.5% (3). Worldwide, 17 million people die over a year and it was estimated that 23.6 million people will die by the year 2030 due to coronary heart disease and stroke (4). In Malaysia, CVD accounted for Abstract Background: Identifying risk factors associated with mortality is important in providing better prognosis to patients. Consistent with that, Bayesian approach offers a great advantage where it rests on the assumption that all model parameters are random quantities and hence can incorporate prior knowledge. Therefore, we aimed to develop a reliable model to identify risk factors associated with mortality among ST-Elevation Myocardial Infarction (STEMI) male patients using Bayesian approach. Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination. Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model. Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.

中文翻译：

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使用贝叶斯分析法分析马来西亚男性心血管疾病患者的死亡风险因素

已经确定世界上最致命的疾病是心血管疾病 (CVD) (1,2)。心血管疾病被称为一组心脏和血管疾病，包括冠心病、脑血管疾病、外周动脉疾病、风湿性心脏病和先天性心脏病（1）。在过去十年中，全球死于 CVD 的人数增加了 12.5% (3)。全世界一年内有 1700 万人死亡，据估计，到 2030 年将有 2360 万人死于冠心病和中风 (4)。在马来西亚，心血管疾病占了摘要背景：识别与死亡率相关的风险因素对于为患者提供更好的预后非常重要。与此一致，贝叶斯方法提供了一个很大的优势，它基于所有模型参数都是随机量的假设，因此可以包含先验知识。因此，我们旨在开发一个可靠的模型，以使用贝叶斯方法确定与 ST 段抬高型心肌梗塞 (STEMI) 男性患者死亡率相关的风险因素。方法：从 2006-2013 年国家心血管疾病数据库 - 急性冠状动脉综合征 (NCVD-ACS) 登记处共招募了 7180 名 STEMI 男性患者。在为 STEMI 患者开发单变量和多变量逻辑回归模型时，应用了贝叶斯马尔可夫链蒙特卡罗 (MCMC) 模拟方法。通过收敛诊断、整体模型拟合、模型校准和判别来评估模型的性能。结果：从贝叶斯多变量logistic模型中确定了STEMI男性患者心血管死亡的6个危险因素，分别为年龄、糖尿病、CVD家族史、Killip分类、慢性肺病和肾病。整体模型拟合、模型校准和判别被认为对所提出的模型有好处。结论：针对 CVD 男性患者的贝叶斯风险预测模型确定了与死亡率相关的六个风险因素。最高风险分别是 Killip 等级 (OR=18.0)、肾脏疾病 (2.46) 和年龄组 (OR=2.43)。模型校准和判别被认为对所提出的模型有好处。结论：针对 CVD 男性患者的贝叶斯风险预测模型确定了与死亡率相关的六个风险因素。最高风险分别是 Killip 等级 (OR=18.0)、肾脏疾病 (2.46) 和年龄组 (OR=2.43)。模型校准和判别被认为对所提出的模型有好处。结论：针对 CVD 男性患者的贝叶斯风险预测模型确定了与死亡率相关的六个风险因素。最高风险分别是 Killip 等级 (OR=18.0)、肾脏疾病 (2.46) 和年龄组 (OR=2.43)。