MATERIALS AND METHODS: This cross-sectional study was carried out on 226 respondents, using a questionnaire which had 4 sections: socio-demographic data, personal information, family information and social information. Data was analyzed using SPSS® version 16. For categorical variables, comparisons were made using Chi-square and for numerical variables a t-test was performed.
RESULTS: The current smoker prevalence rate was 20.8% which showed a significant association between smoking and individual factors: level of knowledge on the effects of smoking (p < 0.05), significant association was seen between smoking and marital status of parents, smoking status of male siblings and various other aspects of the individuals themselves.
CONCLUSIONS: Concerted efforts involving various parties should be taken to curb or prevent this problem or the number of teenage smokers in the country will increase. This in the long run will invite problems to the well being of the adolescents themselves, their families, community and the nation as a whole.
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.