Risk Factors of Mortality among Male Patients with Cardiovas-cular Disease in Malaysia Using Bayesian Analysis

  • Nurliyana JUHAN 1. Preparatory Centre for Science and Technology, Universiti Malaysia Sabah, Sabah, Malaysia 2. Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • Yong Zulina ZUBAIRI Mail Centre for Foundation Studies in Science, University of Malaya, Kuala Lumpur, Malaysia
  • Zarina Mohd KHALID Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • Ahmad Syadi MAHMOOD ZUHDI Cardiology Unit, University Malaya Medical Centre, Kuala Lumpur, Malaysia
Cardiovascular disease, Myocardial infarction;, Male;, Risk factors;, Bayesian


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.


Eckel RH, Jakicic JM, Ard JD et al (2014). 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: A report of the American College of cardiology/American Heart Association task force on practice guidelines. Circulation, 129(25 SUPPL. 1).

World Health Organization (2016). Fact sheet No.317: Cardiovascular diseases (CVDs). http://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

Mendis S, Puska P NB (2011). Global atlas on cardiovascular disease prevention and control. World Health Organization.


Department of Statistics Malaysia Official Portal (2016). Statistics on Causes of Death, Malaysia, 2014.


Wong RS, Ismail NA (2016). An application of Bayesian approach in modeling risk of death in an intensive care unit. PloS one, 11(3):e0151949.

Ismail NM, Khalid ZM, Ahmad N (2012). Estimating proportional hazards model using frequentist and Bayesian approaches. Malaysian Journal of Fundamental and Applied Sciences, 8(2).

Carlin BP, Hong H, Shamliyan TA (2013). Case study comparing Bayesian and frequentist approaches for multiple treatment comparisons. Rockville: Agency for Healthcare Research and Quality. USA.

Wan Ahmad WA, Sim KH (2015). Annual report of the NCVD-ACS Registry Malaysia 2011-2013. Kuala Lumpur Malaysia: National Cardiovascular Disease Database.

Zuhdi AS, Ahmad WA, Zaki RA et al (2016). Acute coronary syndrome in the elderly: the Malaysian National Cardiovascular Disease Database-Acute Coronary Syndrome registry. Singapore Medical Journal, 57(4):191.

Ganesan S, Kannan K, Victor A et al (2015). QRBBB in acute coronary syndrome: Does it matter in modern era? Angiographic correlation. Indian Heart J, 1;67:S38.

Ifedili I, Kadire S, Bob-Manuel T et al (2017). Predictors Of True St-Segment Elevation Myocardial Infarction In Cocaine Positive Patients. Journal of the American College of Cardiology, 69(11 Supplement).

Killip T, Kimball JT (1967). Treatment of myocardial infarction in a coronary care unit: a two year experience with 250 patients. The American J of Cardiology, 20(4):457-64.

Hosmer DW, Lemeshow S (2000). Logistic Regression for Matched Case Control Studies in Applied Logistic Regression, Second Edition. John Wiley & Sons. USA.

Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29-36.

Alston CL, Mengersen KL, Pettitt AN et al (2012). Case studies in Bayesian statistical modelling and analysis. John Wiley & Sons.

Spiegelhalter DJ, Best NG, Carlin BP (2002). Bayesian measures of model complexity and fit. J of the Royal Statistical Society: Series B (Statistical Methodology, 64(4):583-639.

Haque AT, Yusoff FB, Ariffin MH et al. (2016). Lipid Profile of the Coronary Heart Disease (CHD) Patients Admitted in a Hospital in Malaysia. Journal of Applied Pharmaceutical Science, 6(5): 137-142.

Gutierrez J, Alloubani A, Mari M et al (2018). Cardiovascular Disease Risk Factors: Hypertension, Diabetes Mellitus and Obesity among Tabuk Citizens in Saudi Arabia. Open Cardiovasc Med J , 12:41.

Upadhyay RP (2012). An overview of the burden of non-communicable diseases in India. Iranian Journal of Public Health, 41(3):1.

Sun LY, Lee EW, Zahra A et al (2015). Risk Factors of Cardiovascular Disease and Their Related Socio-Economical, Environmental and Health Behavioral Factors: Focused on Low-Middle Income Countries- A Narrative Review Article. Iran J Public Health. 2015;44(4):435–44.

Lloyd-Jones DM, Nam BH, D'Agostino Sr RB et al (2004). Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. Jama, 291(18):2204-11.

Leander K, Hallqvist J, Reuterwall C et al (2001). Family history of coronary heart disease, a strong risk factor for myocardial infarction interacting with other cardiovascular risk factors: results from the Stockholm Heart Epidemiology Program (SHEEP). Epidemiology, 12(2):215-21.

Bertuzzi M, Negri E, Tavani A et al (2003). Family history of ischemic heart disease and risk of acute myocardial infarction. Preventive Medicine, 37(3):183-7.

Imes CC, Lewis FM (2014). Family history of cardiovascular disease (CVD), perceived CVD risk, and health-related behavior: A review of the literature. The Journal of Cardiovascular Nursing, 29(2):108.

Watanabe R, Tanaka T, Aita K et al (2015). Osteoporosis is highly prevalent in Japanese males with chronic obstructive pulmonary disease and is associated with deteriorated pulmonary function. J Bone Miner Metab, 33(4):392–400.

Aryal S, Diaz-Guzman E, Mannino DM (2014). Influence of sex on chronic obstructive pulmonary disease risk and treatment outcomes. Int J Chron Obstruct Pulmon Dis, 9:1145–54.

Selvarajah S, Fong AY, Selvaraj G et al (2012). An Asian validation of the TIMI risk score for ST-segment elevation myocardial infarction. PLoS One, 7(7):1–7.

Bian S, Guo H, Ye P et al (2012). Serum uric Acid level and diverse impacts on regional arterial stiffness and wave reflection. Iranian Journal of Public Health, 41(8):33.

Löfman I, Szummer K, Hagerman I et al (2016). Prevalence and prognostic impact of kidney disease on heart failure patients. Open heart, 3(1):e000324.

Lu HT, Nordin RB (2013). Ethnic differences in the occurrence of acute coronary syndrome: results of the Malaysian National Cardiovascular Disease (NCVD) Database Registry (March 2006-February 2010). BMC cardiovascular disorders., 13(1):97.

Shiraishi J, Kohno Y, Nakamura T et al (2014). Predictors of in-hospital outcomes after primary percutaneous coronary intervention for acute myocardial infarction in patients with a high Killip class. Internal Medicine, 53(9):933-9.

Liu CW, Liao PC, Chen KC et al (2017). Relationship of serum uric acid and Killip class on mortality after acute ST-segment elevation myocardial infarction and primary percutaneous coronary intervention. International Journal of Cardiology, 226:26-33.

Gelman A, Carlin JB, Stern HS et al (2013). Bayesian data analysis. Boca Raton, FL: CRC press.

Mulder J, Wagenmakers EJ (2016). Editors’ introduction to the special issue “Bayes factors for testing hypotheses in psychological research: Practical relevance and new developments”. Journal of Mathematical Psychology, 72:1-5.

Spiegelhalter DJ, Abrams KR, Myles JP (2004). Bayesian approaches to clinical trials and health-care evaluation. John Wiley & Sons.

McCarthy MA (2007). Bayesian methods for ecology. Cambridge University Press.

Besag J, York J, Mollié A (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 1;43(1):1-20.

Torman V, & Camey SA (2015). Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes. Emerging themes in epidemiology, 12(1), 8.

How to Cite
JUHAN N, ZUBAIRI YZ, KHALID ZM, MAHMOOD ZUHDI AS. Risk Factors of Mortality among Male Patients with Cardiovas-cular Disease in Malaysia Using Bayesian Analysis. Iran J Public Health. 49(9):1642-1649.
Original Article(s)