Mapping and evaluation of global and country-specific cardiovascular disease risk prediction models
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.
What are the key findings of this study?
Cardiovascular diseases are a major health issue worldwide. This review looks at different tools that predict the risk of heart disease, comparing global models and those specific to individual countries. Some models work better because they consider local factors like age and lifestyle. It's important to create these tailored models to help people get the right care, especially in local communities. 🫀