Abstract
BACKGROUND: Risk prediction models for cardiovascular diseases (CVDs) have been widely applied in clinical practice and in designing prevention policies globally, yet their accuracy across different regions with distinct epidemiological profiles remains uncertain. We examined the regional variation in risk distribution and agreement between these models. METHODS: We analysed 53 nationally representative health surveys in seven regions. Using the World Health Organization (WHO), SCORE2, and Framingham CVD risk prediction models, we estimated the respondents' 10-year CVD risk and categorised them into low-, moderate-, or high-risk groups. RESULTS: We included 86 430 individuals aged 40-69 years without a history of CVD in our analysis. Globally, CVD risk estimates differed substantially across models (WHO: 7.75%; 95% confidence interval (CI) = 7.70-7.80; SCORE2: 3.72%; 95% CI = 3.69-3.75; Framingham: 12.42%; 95% CI = 12.34-12.50). We also noted regional disparities in identifying moderate- and high-risk subjects, particularly in South Asia (WHO: 12.57%; 95% CI = 11.63-13.51; SCORE2: 18.24%; 95% CI = 17.14-19.33; Framingham: 29.40%; 95% CI = 28.11-30.70), sub-Saharan Africa (WHO: 16.30%; 95% CI = 15.78-16.83; SCORE2: 22.69%; 95% CI = 22.09-23.28; Framingham: 33.85%; 95% CI = 33.18-34.52), East Asia & the Pacific (WHO: 21.06%; 95% CI = 20.57, 21.55; SCORE2: 31.03%; 95% CI = 30.47, 31.59; Framingham: 45.54%; 95% CI = 44.93-46.14), and Latin America & the Caribbean (WHO: 23.09%; 95% CI = 21.48-24.70; SCORE2: 41.56%; 95% CI = 39.68-43.44; Framingham: 55.83%; 95% CI = 53.94-57.72), with greater than two-fold differences across models. Agreement in classifying individuals into low-, moderate-, or high-risk groups remained relatively high across risk models (63.1%), but varied considerably across regions, from 73.91% in South Asia to 47.54% in Latin America & the Caribbean. CONCLUSIONS: The CVD risk estimates produced by the WHO, SCORE2, and Framingham models varied significantly across regions, with poor consistency in identifying at-risk individuals in some regions. These discrepancies may lead to undertreatment and inefficient use of otherwise limited healthcare resources. Region-specific adaptations are needed to enhance risk targeting, promote equity, and improve the overall effectiveness of primary prevention.