Abstract
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality, requiring precise risk prediction models for effective prevention and management. This review maps and evaluates globally utilized and country-specific CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, PREVENT, WHO/ISH Risk Charts, INTERHEART, and SCORE2. A structured literature search was conducted using PubMed and Google Scholar, from which 30 relevant studies were selected. Most of the models integrate traditional risk factors such as age, sex, blood pressure, cholesterol, and smoking status to estimate CVD risk. While these models demonstrate moderate to good discrimination (C-statistics ranging from 0.66 to 0.80) and validation, their applicability varies across populations, with concerns about overestimation or underestimation in non-original cohorts. Notably, the WHO/ISH and Globorisk models address global diversity by incorporating regional calibrations, making them suitable for low- and middle-income countries. Similarly, the country-specific risk scores outperform global models due to their incorporation of local socio-demographics. Limitations persist across existing models, including the underrepresentation of younger individuals, ethnic minorities, and the exclusion of emerging risk factors. Future efforts must prioritize the development of locally validated, population-specific models to support equitable and effective CVD risk assessment and prevention.