Assessment of ten-year cardiovascular disease risk in Bangladesh: A comparative analysis of laboratory and non-laboratory-based WHO risk prediction models

ObjectivesThis study aimed to estimate the 10-year cardiovascular diseases (CVD) risk in Bangladeshi adults using updated 2019 WHO laboratory-based and non-laboratory-based models, identify key CVD risk determinants, and assess the agreement between two models. MethodsWe conducted a cross-sectional analysis of nationally representative Bangladesh STEPS 2018 survey data, involving 2767 adults aged 40-69 years without prior CVDs, using both versions of the updated 2019 WHO 10-year CVD risk prediction models. Logistic regression identified determinants and model agreement was assessed using Bland-Altman Plot, concordance, and Kappa statistics with STATA version 17. ResultsIn the laboratory-based model, 10.32% (95% CI: 8.78-12.11) of the population was at elevated risk ([&ge;]10%), while the non-laboratory-based model showed 9.25% (95% CI: 7.7-11.08). Males had significantly higher risk than females in both models (p<0.0001). Elevated 10-year CVD risk was associated with age increments of five years (OR=4.29, 95% CI: 3.48-5.30), being female (OR=0.31, 95% CI: 0.18-0.53), urban residence (OR=1.82, 95% CI: 1.20-2.74), residing in Dhaka division (OR=2.44, 95% CI: 1.26-4.73), and higher education (OR=2.42, 95% CI: 1.06-5.51). Key metabolic factors included high waist-hip ratio (OR=2.08, 95% CI: 1.22-3.52) and elevated triglycerides (OR=2.70, 95% CI: 1.65-4.41). Comparing models, Bland-Altman plots showed limit of agreement (-3.16, 3.52) with mean difference of 0.18, proportional bias (males: y=-0.43 + 1.11x; females: y=-0.43 + 1.16x), Lins Concordance Correlation Coefficient (LCCC) 0.90 (95% CI: 0.89-0.91), and a Kappa statistic of 0.69 (95%CI: 0.67-0.72). ConclusionA substantial portion of the adult Bangladeshi population is at elevated risk of a cardiovascular event within the next decade, with males at higher risk per both models. Sociodemographic, and metabolic factors indicating potential target groups for intervention. The non-laboratory-based model generally shows agreement with the laboratory-based model and can serve as an alternative in settings with limited laboratory resources, though caution is advised for high-risk individuals.