Mapping and evaluation of global and country-specific cardiovascular disease risk prediction models

Area of Science:

• Cardiology
• Public Health
• Epidemiology

Background:

• Cardiovascular diseases (CVDs) are a major global health burden.
• Accurate CVD risk prediction is essential for effective prevention strategies.
• Existing risk models show variable performance across diverse populations.

Purpose of the Study:

• To systematically review and evaluate global and country-specific CVD risk prediction models.
• To identify strengths, limitations, and applicability of current models.
• To inform future development of more accurate and equitable risk assessment tools.

Main Methods:

• A comprehensive literature search was performed using PubMed and Google Scholar.
• Thirty relevant studies detailing CVD risk prediction models were selected and analyzed.
• Models were evaluated based on their risk factors, discrimination (C-statistics), and validation across different cohorts.

Main Results:

• Most CVD risk models utilize traditional factors like age, sex, blood pressure, cholesterol, and smoking.
• Discrimination (C-statistics 0.66–0.80) and validation were moderate to good, but applicability varied.
• Global models like WHO/ISH and Globorisk offer regional calibration; country-specific models showed superior performance due to local data integration.

Conclusions:

• Existing CVD risk models face challenges with population variability, underrepresentation of certain groups, and exclusion of novel risk factors.
• Country-specific and regionally calibrated models demonstrate better performance than generic global models.
• Future research should focus on developing and validating localized, population-specific CVD risk models for equitable global health outcomes.