Publication Details

Fatema, K., Rahman, B., Zwar, N. Arnold., Milton, A. Hasnat. & Ali, L. (2016). Short-term predictive ability of selected cardiovascular risk prediction models in a rural Bangladeshi population: A case-cohort study. BMC Cardiovascular Disorders, 16 (1), 105-1-105-12.


Background: Prediction of absolute risk of cardiovascular diseases (CVDs) has important clinical and public health significance, but the predictive ability of the available tools has not yet been tested in the rural Bangladeshi population. The present study was undertaken to test the hypothesis that both laboratory-based (Framingham equation and WHO/ISH laboratory-based charts) and non-laboratory-based tools may be used to predict CVDs on a short-term basis. Methods: Data from a case-cohort study (52989 cohort and 439 sub-cohort participants), conducted on a rural Bangladeshi population, were analysed using modified Cox PH model with a maximum follow-up of 2.5 years. The outcome variable, coronary heart diseases (CHDs), was assessed in 2014 using electrocardiography, and it was used as a surrogate marker for CVDs in Bangladesh. The predictive power of the models was assessed by calculating C-statistics and generating ROC curves with other measures of diagnostic tests. Results: All the models showed high negative prediction values (NPVs, 84 % to 92 %) and these did not differ between models or gender. The sensitivity of the models substantially changed based on the risk prediction thresholds (between 5-30 %); however, the NPVs and PPVs were relatively stable at various threshold levels. Hypertension and dyslipidaemia were significantly associated with CHD outcome in males and ABSI (a body shape index) in females. All models showed similar C-statistics (0.611-0.685, in both genders). Overall, the non-laboratory-based model showed better performance (0.685) in women but equal performance in men. Conclusions: Existing CVD risk prediction tools may identify future CHD cases with fairly good confidence on a short-term basis. The non-laboratory-based tool, using ABSI as a predictor, may provide better predictive accuracy among women.



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