Comparative Analysis of Cardiovascular Disease risk in a local population of Sri Lankans using risk predictors: WHO ISH charts, Original and recalibrated Framingham Equation

Background: Cardiovascular diseases (CVDs) accounted for 32% of global deaths in 2019, mostly in lower and middle-income countries and ischemic heart diseases and stroke were leading causes. Prevention relies on individual CVD risk prediction using methods like the Framingham Risk Equation, SCORE, and WHO ISH charts for effective screening and management.This study focuses on assessing and comparing CVD risk using different WHO ISH charts and both traditional and recalibrated Framingham equations and calculating the agreement between specific risk predictors. Methods: CVD risk was assessed using WHO ISH Chart with cholesterol levels of 5mmol/dl and 4mmol/dl, and individual cholesterol levels. Additionally, the non-cholesterol WHO ISH Chart was used. The four values were compared using the Cohen Kappa statistic. CVD risk was calculated using the traditional Framingham Equation and a recalibrated version with local data. These were compared with WHO ISH Chart results using Cohen Kappa statistic. The Framingham scores were categorized per WHO ISH categories. Multivariate analysis was performed using SPSS 25 to find out the association between risk factors not used for risk calculation with the predicted cardiovascular disease risk. Results: WHO ISH methods indicated a 10-year CVD risk below 10% for over 90% of the population. Original and recalibrated Framingham scores predicted 55.5% and 62.3% with less than 10% risk, respectively. Significant gender differences were noted in high CVD risk category between the original Framingham (13.8% females vs. 21.3% males) and recalibrated Framingham (9% females vs. 13.4% males) risk equation. WHO ISH cholesterol method showed substantial agreement (k=0.716, p<0.001) with WHO ISH assume 5 method, while the non-cholesterol method had slight agreement (k=0.124, p<0.01) with WHO ISH assume 4 method. Substantial agreement (k=0.613, p<0.01) was found between original and recalibrated Framingham scores. The original Framingham score had poor agreement with WHO ISH methods, but recalibration improved the values, with the highest being fair agreement (k=0.365, p<0.01) with the WHO ISH cholesterol method. Conclusion: Different CVD risk prediction methods yielded varying 10-year risk distributions. Significant differences were observed between original and recalibrated Framingham scores. Varying levels of agreement were observed between risk predictors. Associations were also found between non-calculated risk factors and predicted CVD risk.