Cvd risk
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Summary
10 papers analyzed
These studies suggest that cardiovascular disease (CVD) risk can be predicted using various models and factors, including recalibrated risk charts, HbA1c, hypertension, machine learning models, prenatal to adulthood factors, and specific algorithms for different populations like HIV-positive individuals and hematopoietic cell transplantation survivors.
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Cardiovascular Disease (CVD) Risk: Insights from Recent Research
Global CVD Risk Prediction Models
Laboratory-Based and Office-Based Risk Scores
Recent research has developed two cardiovascular risk scores to predict the 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years across 182 countries. These models include a laboratory-based risk score, which factors in age, sex, smoking, blood pressure, diabetes, and total cholesterol, and an office-based risk score, which substitutes diabetes and total cholesterol with BMI. The study found that predicted risks for the same risk factor profile were generally lower in high-income countries (HICs) compared to low- and middle-income countries (LMICs), with the highest risks observed in central and southeast Asia and eastern Europe. Notably, the office-based model underestimated the risk among patients with diabetes.
SCORE2 Risk Prediction Algorithms
The SCORE2 model is a newly developed algorithm designed to estimate the 10-year risk of fatal and non-fatal CVD in European populations aged 40-69 years. This model incorporates age, smoking status, systolic blood pressure, and cholesterol levels, and is recalibrated for different European regions based on country-specific CVD mortality rates. The model demonstrated good predictive performance, with C-indices ranging from 0.67 to 0.81 across various European regions.
CVD Risk in Specific Populations
Type 1 Diabetes
A study focusing on adults with type 1 diabetes identified several key risk factors for CVD, including HbA1c levels, hypertension, dyslipidemia, and diabetic nephropathy. Over a median follow-up of 4.6 years, 3.7% of participants developed incident CVD, with ischemic heart disease being the most common event. The study underscores the importance of managing these risk factors to mitigate CVD risk in this population.
HIV-Positive Individuals
The D:A:D study developed a CVD risk prediction model tailored to HIV-positive individuals, incorporating traditional risk factors along with HIV-specific parameters such as CD4 lymphocyte count and exposure to antiretroviral therapies. This model outperformed the recalibrated Framingham model in predicting CVD risk, highlighting the need for specialized risk assessment tools in this population. Additionally, the study found that individuals at high predicted risk for both CVD and chronic kidney disease (CKD) had substantially greater risks for both conditions, suggesting that CVD and CKD risk should be assessed together in HIV-positive persons.
Hematopoietic Cell Transplantation Survivors
Hematopoietic cell transplantation (HCT) survivors are at increased risk of CVD. A study developed risk prediction models for this population, identifying age, anthracycline dose, chest radiation, hypertension, diabetes, and smoking as significant predictors. These models were validated and shown to effectively stratify patients into low, intermediate, and high-risk groups for developing CVD.
Advances in CVD Risk Prediction Techniques
Machine Learning Approaches
A study utilizing data from the UK Biobank tested the efficacy of machine learning (ML) techniques in predicting CVD risk. The ML-based model, developed using the AutoPrognosis framework, outperformed traditional risk prediction models such as the Framingham score. The ML model included non-traditional predictors like walking pace and self-reported health rating, demonstrating the potential of data-driven techniques to enhance CVD risk prediction accuracy.
Conclusion
Recent advancements in CVD risk prediction models have significantly improved our ability to identify individuals at high risk across various populations. From global models recalibrated for specific countries to specialized tools for populations with unique risk profiles, these models are crucial for guiding preventive care and reducing the burden of cardiovascular diseases worldwide. The integration of machine learning techniques further enhances the precision of these predictions, offering promising avenues for future research and clinical practice.
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