DESIGN: Retrospective study.
SETTING: Malaysian National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry years 2006-2013, which consists of 18 hospitals across the country.
PARTICIPANTS: 7180 male patients diagnosed with STEMI from the NCVD-ACS registry.
PRIMARY AND SECONDARY OUTCOME MEASURES: A graphical model based on the Bayesian network (BN) approach has been considered. A bootstrap resampling approach was integrated into the structural learning algorithm to estimate probabilistic relations between the studied features that have the strongest influence and support.
RESULTS: The relationships between 16 features in the domain of CVD were visualised. From the bootstrap resampling approach, out of 250, only 25 arcs are significant (strength value ≥0.85 and the direction value ≥0.50). Age group, Killip class and renal disease were classified as the key predictors in the BN model for male patients as they were the most influential variables directly connected to the outcome, which is the patient status. Widespread probabilistic associations between the key predictors and the remaining variables were observed in the network structure. High likelihood values are observed for patient status variable stated alive (93.8%), Killip class I on presentation (66.8%), patient younger than 65 (81.1%), smoker patient (77.2%) and ethnic Malay (59.2%). The BN model has been shown to have good predictive performance.
CONCLUSIONS: The data visualisation analysis can be a powerful tool to understand the relationships between the CVD prognostic variables and can be useful to clinicians.
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.
OBJECTIVES: To identify the risk factors associated with mortality for each gender and compare differences, if any, among ST-elevation myocardial infarction (STEMI) patients.
DESIGN: Retrospective analysis.
SETTINGS: Hospitals across Malaysia.
PATIENTS AND METHODS: We analyzed data on all STEMI patients in the National Cardiovascular Database-Acute coronary syndrome (NCVD-ACS) registry for the years 2006 to 2013 (8 years). We collected demographic and risk factor data (diabetes mellitus, hypertension, smoking status, dyslipidaemia and family history of CAD). Significant variables from the univariate analysis were further analysed by a multivariate logistic analysis to identify risk factors and compare by gender.
MAIN OUTCOME MEASURES: Differential risk factors for each gender.
RESULTS: For the 19484 patients included in the analysis, the mortality rate over the 8 years was significantly higher in females (15.4%) than males (7.5%) (P < .001). The univariate analysis showed that the majority of male patients < 65 years while females were >=65 years. The most prevalent risk factors for male patients were smoking (79.3%), followed by hypertension (54.9%) and diabetes mellitus (40.4%), while the most prevalent risk factors for female patients were hypertension (76.8%), followed by diabetes mellitus (60%) and dyslipidaemia (38.1%). The final model for male STEMI patients had seven significant variables: Killip class, age group, hypertension, renal disease, percutaneous coronary intervention and family history of CVD. For female STEMI patients, the significant variables were renal disease, smoking status, Killip class and age group.
CONCLUSION: Gender differences existed in the baseline characteristics, associated risk factors, clinical presentation and outcomes among STEMI patients. For STEMI females, the rate of mortality was twice that of males. Once they reach menopausal age, when there is less protection from the estrogen hormone and there are other risk factors, menopausal females are at increased risk for STEMI.
LIMITATION: Retrospective registry data with inter-hospital variation.
METHODS: In this retrospective record review study, cervical cancer data obtained from Hospital UniversitiSains Malaysia (HUSM) was analysed. The data comprises of 120 patients who had been diagnosed as cervical cancer between 1(st) July 1995 and 30(th) June 2007, and obtained treatment from the hospital. The outcome variable was survival time (in months) from cervical cancer diagnosis to death. A stratified Weibull model was applied to study the effect of explanatory variable on survival time when there was time-dependent covariate in the model.
RESULTS: Stage of disease and metastases were important prognostic variables. However, metastasis had been stratified because this variable did not satisfy the proportional hazard assumption. In without metastasis stratum, patients who were diagnosed at stage III & IV are at 2.30 times the risk of death as those in stage I & II. Meanwhile, in with metastasis stratum, patients in stage III & IV group had 3.53 times the hazard faced by patients in stage I & II.
CONCLUSION: The prognosis of cervical cancer patients was dependent upon the stage at diagnosis, after the stratification of the metastasis variable. A poorer prognosis on survival was observed for patients in stage III & IV than those in stage I & II.