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Comparison of Predicted Cardiovascular Risk Profiles by Different CVD Risk-Scoring Algorithms between HIV-1-Infected
Titus Msoka1, Josephine Rogath1, Gary Van Guilder2
1Kilimanjaro Christian Medical Centre, Moshi, Tanzania.
Insights
HIV-infected individuals face higher cardiovascular disease (CVD) risk. Standard CVD risk algorithms show reasonable agreement but an HIV-specific tool is needed for accurate risk prediction in sub-Saharan Africa.
Area of Science:
- Cardiology
- Infectious Diseases
- Public Health
Background:
- Cardiovascular disease (CVD) risk assessment is crucial for identifying individuals needing intervention.
- Concerns exist regarding the applicability of current CVD risk prediction algorithms for HIV-infected populations in sub-Saharan Africa.
Purpose of the Study:
- To compare cardiovascular risk profiles between HIV-infected (ART-naive and on ART) and HIV-uninfected adults.
- To assess the concordance of the American College of Cardiology/American Heart Association (ASCVD) and Framingham cardiovascular risk score (FRS) algorithms in predicting 10-year CVD risk in Tanzania.
- To evaluate the suitability of existing algorithms for HIV-infected individuals in sub-Saharan Africa.
Main Methods:
- A cross-sectional hospital-based study in Tanzania included 40 HIV-infected ART-naive, 64 HIV-infected on ART, and 50 HIV-uninfected adults.
- Cardiovascular risk factors were determined using standard investigations.
- The primary outcome was the absolute 10-year CVD risk score calculated by ASCVD and FRS algorithms.
Main Results:
- HIV-infected adults exhibited a higher 10-year CVD risk compared to HIV-uninfected individuals.
- The ASCVD algorithm identified a greater proportion of high-risk individuals than the FRS in both HIV-infected and uninfected groups.
- Concordance between ASCVD and FRS-lipid algorithms was reasonable for both groups, though higher in the HIV-uninfected population.
Conclusions:
- HIV infection is associated with increased 10-year cardiovascular risk.
- Existing CVD risk algorithms (ASCVD and FRS-lipid) demonstrate reasonable concordance in HIV-infected and uninfected individuals in Tanzania.
- There is a need for an HIV-specific algorithm to accurately predict CVD risk in this high-risk population.
Purpose:
Cardiovascular disease (CVD) risk assessment is a suitable way to differentiate between high-risk individuals requiring intervention and risk modification, and those at low risk. However, concerns have been raised when adopting a CVD-risk prediction algorithm for HIV-infected patients in sub-Saharan Africa.
Patients And Methods:
We compared cardiovascular risk profiles between HIV-infected (with and without antiretroviral therapy (ART)) and HIV-uninfected adults as predicted by the American College of Cardiology/American Heart Association (ASCVD) and the Framingham cardiovascular risk score (FRS) algorithms and assessed the concordance of the algorithms in predicting 10-year CVD risk separately in HIV-infected and uninfected groups in a hospital-based cross-sectional study in Tanzania. A cross-sectional hospital-based study including 40 HIV-infected ART-naive, 64 HIV-infected on ART, and 50 HIV-uninfected adults was conducted. Traditional cardiovascular risk factors were determined by standard investigations. The primary outcome was the absolute 10-year CVD risk score based on the two algorithms.
Results:
Compared to HIV-uninfected, HIV-infected adults were classified at a higher 10-year CVD risk. ASCVD algorithms predicted a higher proportion of high-risk individuals compared to FRS in both HIV-infected and uninfected groups. The concordance between ASCVD and FRS-lipid algorithms was reasonable for both HIV-infected and uninfected groups though relatively higher in the HIV-uninfected group.
Conclusion:
HIV-infected individuals have a higher 10-year cardiovascular risk compared to HIV-uninfected persons. The concordance between ASCVD and FRS-lipid algorithms is reasonable in both HIV-uninfected and infected persons in Tanzania. Development of an HIV-specific algorithm is needed to accurately predict CVD risk in this population at high-risk.
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