Cardiovascular risk prediction in India: Comparison of the original and recalibrated Framingham prognostic models in urban populations.

Introduction: Cardiovascular diseases (CVDs) are the leading cause of death in India. The CVD risk approach is a cost-effective way to identify those at high risk, especially in a low resource setting. As there is no validated prognostic model for an Indian urban population, we have re-calibrated the original Framingham model using data from two urban Indian studies. Methods: We have estimated three risk score equations using three different models. The first model was based on Framingham original model; the second and third are the recalibrated models using risk factor prevalence from CARRS (Centre for cArdiometabolic Risk Reduction in South-Asia) and ICMR (Indian Council of Medical Research) studies, and estimated survival from WHO 2012 data for India. We applied these three risk scores to the CARRS and ICMR participants and estimated the proportion of those at high-risk (>30% 10 years CVD risk) who would be eligible to receive preventive treatment such as statins. Results: In the CARRS study, the proportion of men with 10 years CVD risk > 30% (and therefore eligible for statin treatment) was 13.3%, 21%, and 13.6% using Framingham, CARRS and ICMR risk models, respectively. The corresponding proportions of women were 3.5%, 16.4%, and 11.6%. In the ICMR study the corresponding proportions of men were 16.3%, 24.2%, and 16.5% and for women, these were 5.6%, 20.5%, and 15.3%. Conclusion: Although the recalibrated model based on local population can improve the validity of CVD risk scores our study exemplifies the variation between recalibrated models using different data from the same country. Considering the growing burden of cardiovascular diseases in India, and the impact that the risk approach has on influencing cardiovascular prevention treatment, such as statins, it is essential to develop high quality and well powered local cohorts (with outcome data) to develop local prognostic models.