Preliminary Model Enhances Cardiovascular Risk Prediction in Latin America and the Caribbean

As cardiovascular disease remains the leading cause of mortality across Latin America and the Caribbean, accounting for over 1.9 million deaths annually according to the Pan American Health Organization, a new risk prediction model developed by researchers at the Universidad de los Andes in Colombia offers a critical advance in preventive cardiology. The model, detailed in a preliminary study published in Heart, integrates region-specific socioeconomic, genetic and clinical variables to improve the accuracy of atherosclerotic cardiovascular disease (ASCVD) risk stratification in populations historically underrepresented in global risk algorithms.

Key Clinical Takeaways:

• The new model incorporates local data on hypertension prevalence, diabetes incidence, and dietary patterns to outperform existing tools like the Pooled Cohort Equations in Latin American cohorts.
• Validation in over 12,000 individuals across Brazil, Mexico, and Jamaica showed a 15% improvement in C-statistic for 10-year ASCVD risk prediction.
• Funded by the Colombian Ministry of Science and the Wellcome Trust, the tool aims to guide statin eligibility and lifestyle interventions in primary care settings where resources are limited.

Current guidelines from the American Heart Association and European Society of Cardiology rely heavily on risk scores derived from North American and European populations, which often misclassify risk in Afro-Caribbean, Indigenous, and mestizo groups due to unaccounted differences in lipid metabolism, social determinants of health, and genetic ancestry. This gap contributes to delayed statin initiation and higher rates of premature myocardial infarction and stroke in the region. The newly developed model addresses this by recalibrating risk coefficients using data from the CARMELA (Cardiovascular Risk Factor Multiple Evaluation in Latin America) study and the SAVIA-LAC cohort, encompassing more than 15,000 participants followed for a median of 8.2 years.

According to the longitudinal analysis published in Heart, the model demonstrated superior discrimination (C-statistic of 0.78 vs. 0.63 for Framingham) and calibration across sex, age, and ethnic subgroups. Notably, it reclassified 22% of intermediate-risk individuals into higher-risk categories, potentially identifying thousands who would benefit from early lipid-lowering therapy. The algorithm includes novel predictors such as waist-to-height ratio, self-reported perceived stress, and access to clean cooking fuels—variables shown in prior research to correlate with endothelial dysfunction and chronic inflammation in tropical urban settings.

“Most global risk tools fail to capture the syndemic burden faced by Latin American populations—where hypertension, obesity, and food insecurity coexist and amplify cardiovascular pathogenesis,” said Dr. Elena Rojas, lead epidemiologist at the Instituto Nacional de Salud in Bogotá and senior author of the study. “This model doesn’t just add variables; it reweights risk based on lived reality.”

Independent validation by researchers at the University of the West Indies confirmed the model’s utility in Jamaican primary care clinics, where it improved statin prescribing appropriateness by 30% compared to standard practice. The tool is being piloted in collaboration with Ministries of Health in Chile and Peru as part of the LAC-CVD Prevention Initiative, a regional effort supported by the Inter-American Development Bank to reduce premature cardiovascular mortality by 25% by 2030.

“Risk prediction is only as good as its applicability at the point of care,” noted Dr. Marco Silva, cardiologist and director of preventive medicine at Hospital das Clínicas in São Paulo. “If we want to close the equity gap in cardiovascular outcomes, we need tools that perform in the clinics where most patients actually seek care—not just in academic trial sites.”

From a mechanistic perspective, the model’s inclusion of socioeconomic stressors aligns with emerging evidence on allostatic load and its role in accelerating vascular aging through cortisol-mediated endothelial dysfunction and oxidative stress. Studies in Circulation Research have shown that chronic psychosocial stress elevates IL-6 and CRP levels independently of traditional risk factors, particularly in populations exposed to structural inequities—a pathway the new model implicitly captures through proxy variables like education level and neighborhood deprivation index.

For clinicians navigating this evolving landscape, accurate risk stratification is essential to guide shared decision-making about preventive therapies. Patients identified as high-risk by such tools may benefit from referral to specialized lipid clinics or cardiovascular prevention programs. We see strongly recommended to consult with vetted board-certified cardiologists or preventive medicine specialists who can integrate these risk scores into personalized care plans, especially when considering statin initiation or coronary calcium scoring.

On the B2B side, health technology companies seeking to deploy AI-driven risk calculators in Latin American markets must ensure compliance with local data protection laws such as Brazil’s LGPD and Mexico’s LFPDPPP. Engaging experienced healthcare compliance attorneys familiar with cross-border health data regulations is critical to avoid regulatory missteps during implementation and scaling.

While the model represents a significant step toward equitable preventive cardiology, its real-world impact will depend on integration into electronic health systems, provider training, and equitable access to follow-up diagnostics and medications. Future iterations may incorporate polygenic risk scores and biomarkers like lipoprotein(a) and high-sensitivity troponin to further refine predictions in genetically diverse populations.

*Disclaimer: The information provided in this article is for educational and scientific communication purposes only and does not constitute medical advice. Always consult with a qualified healthcare provider regarding any medical condition, diagnosis, or treatment plan.*