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Cardiovascular disease (CVD) is the number one killer among women in Malaysia and globally, with over two million deaths each year. In this study, two modelling approaches namely Bayesian approach and frequentist approach were considered to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) and to obtain feasible model to fit the data. Comparisons were made to find the best model. A total of 874 STEMI female patients from 18 participating hospitals across Malaysia in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analysis were performed for both Bayesian and frequentist approaches. Six variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group were found to be significant at the multivariate analysis. The standard errors obtained from the Bayesian approach were much smaller than the frequentist approach. Also, the model fit using Bayesian approach was much better than the frequentist as the deviance value produced by the Bayesian approach was smaller. The Bayesian analysis provides a better alternative to the frequentist approach in the analysis of the risk factors associated with mortality among female CVD patients.

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.

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.

Cardiovascular disease is the leading cause of death in Malaysia and globally. This study aimed to identify associated risk factors in cardiovascular disease among ST elevation myocardial infarction (STEMI) male patients and obtain a feasible model to describe the data. A total of 16,673 STEMI male patients from 18 participating hospitals across Malaysia in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate analysis was conducted. Significant variables from the univariate analysis were further analysed by a multivariate logistic analysis to identify the prognostic factors. The most prevalent risk factor for male patients was smoking (79.3%), followed by hypertension (54.9%) and diabetes mellitus (40.4%). At univariate level, this study is consistent with the findings from the Malaysian National Health and Morbidity Survey (NHMS) where smoking is a significant risk factor. After adjustment in multivariate logistic model, the risk factors for cardiovascular death among male patients are related to age, premorbid condition such as diabetes mellitus, hypertension, family history of CVD, Killip class, type of treatment such as percutaneous coronary intervention (PCI) and relevant comorbidity such as renal disease. Drastic efforts in the management of all risk factors in males is needed to improve adherence outcomes.