Identifying Risk Factors for Female Cardiovascular Disease Patients in Malaysia: A Bayesian Approach

IF 0.3 Q4 MATHEMATICS Matematika Pub Date : 2018-12-31 DOI:10.11113/MATEMATIKA.V34.N3.1135
Nurliyana Juhan, Y. Zubairi, Z. M. Khalid, A. S. M. Zuhdi
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Abstract

Cardiovascular disease (CVD) includes coronary heart disease, cerebrovasculardisease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All femalesface the threat of CVD. But becoming aware of symptoms and signs is a great challengesince most adults at increased risk of cardiovascular disease (CVD) have no symptoms orobvious signs especially in females. The symptoms may be identified by the assessmentof their risk factors. The Bayesian approach is a specific way in dealing with this kindof problem by formalizing a priori beliefs and of combining them with the available ob-servations. This study aimed to identify associated risk factors in CVD among femalepatients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian lo-gistic regression and obtain a feasible model to describe the data. A total of 874 STEMIfemale patients in the National Cardiovascular Disease Database-Acute Coronary Syn-drome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov ChainMonte Carlo (MCMC) simulation approach was applied in the univariate and multivariateanalysis. Model performance was assessed through the model calibration and discrimina-tion. The final multivariate model of STEMI female patients consisted of six significantvariables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killipclass and age group. Females aged 65 years and above have higher incidence of CVD andmortality is high among female patients with Killip class IV. Also, renal disease was astrong predictor of CVD mortality. Besides, performance measures for the model wasconsidered good. Bayesian logistic regression model provided a better understanding onthe associated risk factors of CVD for female patients which may help tailor preventionor treatment plans more effectively.
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识别马来西亚女性心血管疾病患者的危险因素:贝叶斯方法
心血管疾病(CVD)包括冠心病、脑血管病(中风)、外周动脉疾病和主动脉动脉粥样硬化。所有女性都面临心血管疾病的威胁。但意识到症状和体征是一个巨大的挑战,因为大多数心血管疾病风险增加的成年人没有症状或明显的体征,尤其是女性。症状可以通过评估其风险因素来识别。贝叶斯方法是处理这类问题的一种特殊方法,它将先验信念形式化,并将它们与可用的观测相结合。本研究旨在使用贝叶斯逻辑回归来确定ST段抬高型心肌梗死(STEMI)女性患者心血管疾病的相关危险因素,并获得一个可行的模型来描述数据。对2006-2013年国家心血管疾病数据库急性冠状动脉综合征(NCVD-ACS)登记的874名STEMI女性患者进行了分析。将贝叶斯马尔可夫链蒙特卡罗(MCMC)模拟方法应用于单变量和多变量分析。通过模型校准和判别来评估模型性能。STEMI女性患者的最终多变量模型由六个有意义的变量组成,即吸烟、血脂异常、心肌梗死(MI)、肾脏疾病、Killiclass和年龄组。65岁及以上的女性CVD发病率较高,Killip IV级女性患者的死亡率较高。此外,肾脏疾病是CVD死亡率的主要预测因素。此外,该模型的性能指标被认为是良好的。贝叶斯逻辑回归模型更好地了解了女性患者心血管疾病的相关风险因素,有助于更有效地制定预防或治疗计划。
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来源期刊
Matematika
Matematika MATHEMATICS-
自引率
25.00%
发文量
0
审稿时长
24 weeks
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