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Published on: October 6, 2016
Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations
Amit C Achhra1, Asya Lyass2, Leila Borowsky3
1Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, 02114, USA.
Insights
Current cardiovascular disease (CVD) prediction tools often underestimate risk in people living with HIV (PLWH). Developing tailored models for diverse PLWH populations is crucial for effective CVD prevention.
Area of Science:
- Cardiology
- Infectious Diseases
- Public Health
Background:
- Cardiovascular disease (CVD) is a significant concern for people living with HIV (PLWH).
- Existing CVD risk prediction models (e.g., Pooled Cohort Equations, Framingham, SCORE) often underestimate risk in PLWH.
- An HIV-specific model (D:A:D) shows modest performance, particularly in US cohorts.
Purpose of the Study:
- To review the current development and application of CVD prediction tools for PLWH.
- To highlight the limitations of existing models in this population.
- To identify needs for improved CVD risk assessment in PLWH.
Main Methods:
- Review of existing literature on CVD risk prediction in PLWH.
- Analysis of validation studies for general population and HIV-specific models.
- Discussion of novel biomarkers and future research directions.
Main Results:
- General population models underestimate CVD risk in younger, female, Black, and low/intermediate risk PLWH.
- The D:A:D model's performance is modest, especially in US-based cohorts.
- Novel biomarkers and diverse global PLWH cohorts require further evaluation.
Conclusions:
- Available CVD risk models for PLWH are suboptimal.
- Clinicians must maintain vigilance for elevated CVD risk in PLWH.
- Tailored risk prediction tools for diverse PLWH populations are needed to optimize preventive care.
Purpose Of Review:
To provide the current state of the development and application of cardiovascular disease (CVD) prediction tools in people living with HIV (PLWH).
Recent Findings:
Several risk prediction models developed on the general population are available to predict CVD risk, the most notable being the US-based pooled cohort equations (PCE), the Framingham risk functions, and the Europe-based SCORE (Systematic COronary Risk Evaluation). In validation studies in cohorts of PLWH, these models generally underestimate CVD risk, especially in individuals who are younger, women, Black race, or predicted to be at low/intermediate risk. An HIV-specific CVD prediction model, the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, is available, but its performance is modest, especially in US-based cohorts. Enhancing CVD prediction with novel biomarkers of inflammation or coronary artery calcification is of interest but has not yet been evaluated in PLWH. Finally, studies on CVD risk prediction are lacking in diverse PLWH globally. While available risk models for CVD prediction in PLWH remain suboptimal, clinicians should remain vigilant of higher CVD risk in this population and should use any of these risk scores for risk stratification to guide preventive interventions. Focus on established traditional risk factors such as smoking remains critical in PLWH. Risk prediction functions tailored to PLWH in diverse settings will enhance clinicians' ability to deliver optimal preventive care.
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