Related Experiment Video
Updated: Mar 25, 2026
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
Estimating the degree of cardiovascular disease risk under-prediction in people with HIV by existing risk models: an
Michael Kwok1, Hari Dandapani2, Nouran Ibrahim2
1Department of Medicine, Washington University School of Medicine, St. Louis, MO.
Insights
Cardiovascular disease (CVD) risk models underestimate risk in people living with HIV (PLHIV), particularly those with lower expected risk. HIV-specific calibration factors are needed for accurate CVD risk assessment in PLHIV.
Area of Science:
- Public Health
- Epidemiology
- Cardiology
Background:
- Cardiovascular disease (CVD) risk models may underestimate risk in people living with HIV (PLHIV).
- Existing models may not accurately reflect CVD risk in the PLHIV population.
- An updated meta-analysis was conducted to evaluate CVD risk underprediction in PLHIV.
Purpose of the Study:
- To assess the degree of CVD risk underprediction by existing risk models in PLHIV.
- To evaluate the performance of CVD risk models in the PLHIV population.
- To identify the need for HIV-specific calibration in CVD risk assessment.
Main Methods:
- Systematic search of electronic databases and reference lists.
- Meta-analysis of calibration (observed:expected ratios) and discrimination (c-statistics) data.
- Inclusion of 16 publications with data on 97,945 PLHIV.
Main Results:
- CVD risk scores significantly underestimated risk in PLHIV, especially at lower expected risk levels (e.g., 71% underestimation at 3% expected risk).
- Risk scores showed moderate discrimination performance (pooled c-statistics 0.71-0.82).
- The Pooled Cohort Equations (PCE-10) and Framingham Risk Score (FRS-10) demonstrated better calibration.
Conclusions:
- Existing CVD risk scores underpredict risk in PLHIV, necessitating HIV-specific calibration factors.
- PCE-10 and FRS-10 models showed improved calibration compared to others.
- Accurate CVD risk assessment in PLHIV requires adjustments to current risk prediction tools.
Background:
The application of cardiovascular disease (CVD) risk models in people with HIV (PWH) may be limited due to concerns that the models tend to underestimate risk in this population. We conducted an updated meta-analysis to assess the degree of CVD risk under prediction in PWH by existing CVD risk models.
Methods:
We searched electronic databases and the reference lists of relevant articles. Data were abstracted using standardized forms. Calibration data included observed: expected ratios, which were graphically summarized. Weighted-regression models were fitted to estimate the relationship between observed and expected event rates across studies. Discrimination data included C -statistics which were pooled using random-effects model meta-analysis.
Results:
Sixteen publications reporting data on 97 945 PWH (mean age, 41 years; 75% male) were included. The risk scores tended to underpredict CVD risk in PWH, especially for individuals with lower expected risk. For PWH with expected 10-year CVD risk of 3%, the risk scores can underestimate risk by 71% on average. By contrast, for PWH with 6% and 9% expected 10-year CVD risk, the underestimation is by ~23% and ~7%, respectively. The risk scores had moderate performance in discrimination (pooled C -statistic values ranged from 0.71 to 0.82). There was substantial heterogeneity across studies and risk models. The 10-year models of the American Joint Societies Pooled Cohort Equation (PCE-10) and the Framingham Risk Score (FRS-10) had better calibration.
Conclusions:
Extant risk scores underpredict CVD risk in PWH, especially for individuals with lower expected risk, suggesting a need for implementation of HIV-specific calibration factors in this population. PCE-10 and FRS-10 showed better calibration.
Related Concept Videos
01:29Bias in Epidemiological Studies
01:12Relative Risk
01:30Coronary Artery Disease I: Introduction
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:12Hazard Ratio
For example, in a clinical trial...
01:10Retrovirus Life Cycles