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Distribution and Performance of Cardiovascular Risk Scores in a Mixed Population of HIV-Infected and Community-Based HIV-Uninfected Individuals in Uganda.
Journal of Acquired Immune Deficiency Syndromes : JAIDS 2018 August 2
BACKGROUND: The utility and validity of cardiovascular diseases (CVD) risk scores are not well studied in sub-Saharan Africa. We compared and correlated CVD risk scores with carotid intima media thickness (c-IMT) among HIV-infected and uninfected people in Uganda.
METHODS: We first calculated CVD risk using the (1) Framingham laboratory-based score; (2) Framingham nonlaboratory score (FRS-BMI); (3) Reynolds risk score; (4) American College of Cardiology and American Heart Association score; and (5) the Data collection on Adverse Effects of Anti-HIV Drugs score. We then compared absolute risk scores and risk categories across each score using Pearson correlation and kappa statistics, respectively. Finally, we fit linear regression models to estimate the strength of association between each risk score and c-IMT.
RESULTS: Of 205 participants, half were females and median age was 49 years [interquartile range (IQR) 46-53]. Median CD4 count was 430 cells/mm (IQR 334-546), with median 7 years of antiretroviral therapy exposure (IQR 6.4-7.5). HIV-uninfected participants had a higher median systolic blood pressure (121 vs. 110 mm Hg), prevalent current smokers (18% vs. 4%, P = 0.001), higher median CVD risk scores (P < 0.003), and greater c-IMT (0.68 vs. 0.63, P = 0.003). Overall, FRS-BMI was highly correlated with other risk scores (all rho >0.80). In linear regression models, we found significant correlations between increasing CVD risk and higher c-IMT (P < 0.01 in all models).
CONCLUSIONS: In this cross-sectional study from Uganda, the FRS-BMI correlated well with standard risk scores and c-IMT. HIV-uninfected individuals had higher risk scores than HIV-infected individuals, and the difference seemed to be driven by modifiable factors.
METHODS: We first calculated CVD risk using the (1) Framingham laboratory-based score; (2) Framingham nonlaboratory score (FRS-BMI); (3) Reynolds risk score; (4) American College of Cardiology and American Heart Association score; and (5) the Data collection on Adverse Effects of Anti-HIV Drugs score. We then compared absolute risk scores and risk categories across each score using Pearson correlation and kappa statistics, respectively. Finally, we fit linear regression models to estimate the strength of association between each risk score and c-IMT.
RESULTS: Of 205 participants, half were females and median age was 49 years [interquartile range (IQR) 46-53]. Median CD4 count was 430 cells/mm (IQR 334-546), with median 7 years of antiretroviral therapy exposure (IQR 6.4-7.5). HIV-uninfected participants had a higher median systolic blood pressure (121 vs. 110 mm Hg), prevalent current smokers (18% vs. 4%, P = 0.001), higher median CVD risk scores (P < 0.003), and greater c-IMT (0.68 vs. 0.63, P = 0.003). Overall, FRS-BMI was highly correlated with other risk scores (all rho >0.80). In linear regression models, we found significant correlations between increasing CVD risk and higher c-IMT (P < 0.01 in all models).
CONCLUSIONS: In this cross-sectional study from Uganda, the FRS-BMI correlated well with standard risk scores and c-IMT. HIV-uninfected individuals had higher risk scores than HIV-infected individuals, and the difference seemed to be driven by modifiable factors.
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