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Cross-sectional comparison of cardiovascular risk scores in people with HIV
Maria Hernandez-Pereira1, Juan Du1,2, Jade Soldado-Folgado2,3
1Medicine and Life Sciences Department (MELIS), Universitat Pompeu Fabra, Barcelona, Spain.
Insights
Cardiovascular disease (CVD) risk scores show poor agreement in people with HIV, potentially underestimating risk. This highlights the need for HIV-specific risk prediction tools for accurate CVD assessment.
Area of Science:
- Cardiology
- Infectious Diseases
- Epidemiology
Background:
- People with HIV face elevated cardiovascular disease (CVD) risk.
- General population CVD risk scores may underestimate risk in this cohort.
- Accurate risk assessment is crucial for preventative strategies.
Purpose of the Study:
- To evaluate the concordance of five CVD risk prediction models in people with HIV.
- To compare risk category distribution across different models.
- To identify the most suitable risk assessment tools for this population.
Main Methods:
- Cross-sectional study of 246 participants (aged 18-75) in Barcelona, Spain.
- Calculation of 10-year CVD risk using SCORE2, REGICOR, REGICOR 3.0, D:A:D reduced, and D:A:D full models.
- Statistical analysis included chi-square tests, Bonferroni correction, Lin's concordance correlation coefficient (CCC), and Bland-Altman analysis.
Main Results:
- Significant differences in risk category distribution across models (p < 0.001).
- High-risk classification varied widely: REGICOR 3.0 (44.4%) and D:A:D full (41.9%) identified more very high-risk individuals than REGICOR (66.2% low-risk).
- Concordance was highest between D:A:D models (CCC=0.82) and poor (<0.5) between most general population and HIV-specific models, with systematic bias noted.
Conclusions:
- Existing cardiovascular risk scores demonstrate limited concordance and interchangeability in people with HIV.
- Discrepancies underscore the inadequacy of general population models for this cohort.
- Validated, HIV-adapted tools are essential for precise CVD risk assessment in people with HIV.
Introduction:
People with HIV have an increased risk of cardiovascular disease (CVD). Despite this, commonly used CVD risk scores have been developed for the general population, which may underestimate risk in people with HIV. This study aims to evaluate the concordance of five risk prediction models (SCORE2, REGICOR, REGICOR 3.0, D:A:D reduced and D:A:D full) in a cohort of people with HIV.
Methods:
This cross-sectional study enrolled 246 participants aged 18-75 at a single centre in Barcelona, Spain. Demographic, clinical and laboratory data were collected to calculate 10-year CVD risk using each model. Risk categories were compared using a chi-square test followed by a post hoc analysis with Bonferroni's correction for multiple comparisons. Concordance was assessed using Lin's concordance correlation coefficient (CCC), and agreement was evaluated with Bland-Altman analysis.
Results:
The risk category distribution differed significantly across models (χ2 = 53.6, df = 12; p < 0.001). REGICOR classified most individuals as low-risk (66.2%), whereas REGICOR 3.0 and D:A:D full identified more as very high-risk (44.4% and 41.9%, respectively). Concordance was highest between D:A:D full and reduced (CCC = 0.82), good between SCORE2 and REGICOR (CCC = 0.78) and poor between most HIV-specific and general population models (CCC < 0.5). Bland-Altman analyses revealed systematic bias, particularly between REGICOR and D:A:D full.
Conclusions:
Cardiovascular risk scores showed limited concordance and poor interchangeability in people with HIV. The discrepancies between the general population and HIV-specific models highlight the need for validated and HIV-adapted tools to accurately assess CVD risk in this population.
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