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

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.