Related Experiment Video
Updated: Mar 18, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Short-term predictive ability of selected cardiovascular risk prediction models in a rural Bangladeshi population: a
Kaniz Fatema1,2, Bayzidur Rahman3, Nicholas Arnold Zwar3
1Department of Epidemiology, Bangladesh University of Health Sciences (BUHS), 125/1, Darus Salam, Mirpur, Dhaka-1216, Bangladesh. k.fatema@unsw.edu.au.
Insights
Cardiovascular disease (CVD) risk prediction tools show good short-term accuracy in rural Bangladesh. A non-laboratory-based model, using a body shape index, is particularly effective for predicting CVD risk in women.
Area of Science:
- Public Health
- Epidemiology
- Cardiovascular Medicine
Background:
- Cardiovascular diseases (CVDs) pose a significant public health challenge globally.
- Accurate prediction of CVD risk is crucial for effective clinical management and prevention strategies.
- Existing CVD risk prediction tools have not been validated in the rural Bangladeshi population.
Purpose of the Study:
- To evaluate the predictive accuracy of laboratory-based and non-laboratory-based tools for CVD risk in rural Bangladesh.
- To test the hypothesis that these tools can predict CVDs on a short-term basis.
- To compare the performance of different risk prediction models in this specific population.
Main Methods:
- A case-cohort study involving 52,989 cohort and 439 sub-cohort participants from rural Bangladesh.
- Modified Cox Proportional Hazards model analysis with a maximum follow-up of 2.5 years.
- Coronary heart diseases (CHDs) assessed via electrocardiography in 2014, serving as a surrogate for CVDs.
- Predictive power assessed using C-statistics, ROC curves, and diagnostic test measures.
Main Results:
- All models demonstrated high negative predictive values (NPVs) ranging from 84% to 92%, with stability across models and genders.
- Sensitivity varied with risk prediction thresholds (5-30%), while NPVs and positive predictive values (PPVs) remained relatively stable.
- Hypertension and dyslipidemia were significant predictors of CHD in males, whereas the ABSI (a body shape index) was significant in females.
- All models exhibited similar C-statistics (0.611-0.685) for both genders.
- The non-laboratory-based model showed superior performance (0.685) in women compared to men.
Conclusions:
- Existing CVD risk prediction tools can identify future CHD cases with considerable confidence in the short term.
- A non-laboratory-based tool, incorporating ABSI, may offer enhanced predictive accuracy for cardiovascular events in women.
- These findings support the potential utility of accessible risk prediction tools in rural Bangladeshi settings.
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.
