Skip to main content

Advertisement

Log in

Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations

  • Co-infections and Comorbidity (D Bhattacharya, Section Editor)
  • Published:
Current HIV/AIDS Reports Aims and scope Submit manuscript

Abstract

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.

Summary

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Lerner AM, Eisinger RW, Fauci AS. Comorbidities in persons with HIV: the lingering challenge. JAMA. 2019.

  2. Schouten J, Wit FW, Stolte IG, Kootstra NA, van der Valk M, Geerlings SE, et al. Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV Cohort Study. Clin Infect Dis. 2014;59:1787–97.

    Article  CAS  Google Scholar 

  3. Triant VA, Lee H, Hadigan C, Grinspoon SK. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. JCEM. 2007;92(7):2506–12.

    Article  CAS  Google Scholar 

  4. • Shah ASV, Stelzle D, Lee KK, Beck EJ, Alam S, Clifford S, et al. Global burden of atherosclerotic cardiovascular disease in people living with HIV. Circulation. 2018;138(11):1100–12. A large systematic review of global burden of CVD in PLWH highlighting the anticipated rise in CVD disease burden in sub-Saharan Africa in coming years.

    Article  Google Scholar 

  5. Achhra AC, Mocroft A, Reiss P, Sabin C, Ryom L, de Wit S, et al. Short-term weight gain after antiretroviral therapy initiation and subsequent risk of cardiovascular disease and diabetes: the D:A:D study. HIV Med. 2016;17(4):255–68.

    Article  CAS  Google Scholar 

  6. Ladapo JA, Richards AK, DeWitt CM, et al. Disparities in the quality of cardiovascular care between HIV-infected versus HIV-uninfected adults in the United States: a cross-sectional study. J Am Heart Assoc. 2017;6(11).

  7. So-Armah K, Benjamin LA, Bloomfield GS, Feinstein MJ, Hsue P, Njuguna B, et al. HIV and cardiovascular disease. The lancet HIV. 2020;7(4):e279–93.

    Article  Google Scholar 

  8. D'Agostino RB Sr. Cardiovascular risk estimation in 2012: lessons learned and applicability to the HIV population. J Infect Dis. 2012;205(Suppl 3):S362–7.

    Article  CAS  Google Scholar 

  9. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Group CHDRP. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. [see comment]. JAMA. 2001;286(2):180–7.

    Article  Google Scholar 

  10. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.

    Article  Google Scholar 

  11. Sullivan LM, Massaro JM, D'Agostino RB Sr. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med. 2004;23(10):1631–60.

    Article  Google Scholar 

  12. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.

    Article  Google Scholar 

  13. Grundy SM, Stone NJ, Bailey AL, et al. AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol. Circulation. 2018;2018:CIR0000000000000625.

    Google Scholar 

  14. D'Agostino RB Sr, Pencina MJ, Massaro JM, Coady S. Cardiovascular disease risk assessment: insights from Framingham. Glob Heart. 2013;8(1):11–23.

    Article  Google Scholar 

  15. Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–8.

    Article  Google Scholar 

  16. Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683–90.

    Article  Google Scholar 

  17. Grant SW, Collins GS, Nashef SAM. Statistical primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg. 2018;54(2):203–8.

    Article  Google Scholar 

  18. D'Agostino RB, Nam BH. Evaluation of the performance of survival analysis models: discrimination and calibration measures. Vol 23: Elsevier Science B.V.; 2004.

  19. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49–73.

    PubMed  Google Scholar 

  20. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.

    Article  Google Scholar 

  21. D'Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.

    Article  Google Scholar 

  22. Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the Framingham heart study. Circulation. 2009;119(24):3078–84.

    Article  Google Scholar 

  23. Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.

    Article  CAS  Google Scholar 

  24. • Friis-Moller N, Ryom L, Smith C, et al. An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons: the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Eur J Prev Cardiol. 2016;23(2):214–23. Updated D:A:D model, the main HIV-specific CVD risk prediction model recommended by multiple guidelines.

    Article  Google Scholar 

  25. Amin NP, Martin SS, Blaha MJ, Nasir K, Blumenthal RS, Michos ED. Headed in the right direction but at risk for miscalculation: a critical appraisal of the 2013 ACC/AHA risk assessment guidelines. J Am Coll Cardiol. 2014.

  26. Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S1–45.

    PubMed  Google Scholar 

  27. Thompson-Paul AM, Lichtenstein KA, Armon C, Palella FJ Jr, Skarbinski J, Chmiel JS, et al. Cardiovascular disease risk prediction in the HIV outpatient study. Clin Infect Dis. 2016;63(11):1508–16.

    Article  Google Scholar 

  28. • Feinstein MJ, Nance RM, Drozd DR, Ning H, Delaney JA, Heckbert SR, et al. Assessing and refining myocardial infarction risk estimation among patients with human immunodeficiency virus: a study by the centers for AIDS Research network of integrated clinical systems. JAMA Cardiol. 2017;2(2):155–62. Multisite US based study in PLWH illustrating poor to modest performace of general population CVD risk prediction models and challenges in developing HIV-specific models.

    Article  Google Scholar 

  29. •• Triant VA, Perez J, Regan S, et al. Cardiovascular risk prediction functions underestimate risk in HIV infection. Circulation. 2018 US based study showing underestimation of CVD risk in PLWH using general population risk prediction models.

  30. •• van Zoest RA, Law M, Sabin CA, Vaartjes I, van der Valk M, Arends JE, et al. Predictive performance of cardiovascular disease risk prediction algorithms in people living with HIV. J Acquir Immune Defic Syndr. 2019;81(5):562–71. Important validation study of CVD prediction models in Dutch PLWH with a large sample size. It showed that the D:A:D model performed well in a population similar to the D:A:D cohort.

    Article  Google Scholar 

  31. Giles ML, Gartner C, Boyd MA. Smoking and HIV: what are the risks and what harm reduction strategies do we have at our disposal? AIDS Res Ther. 2018;15(1):26.

    Article  Google Scholar 

  32. Titanji B, Gavegnano C, Hsue P, Schinazi R, Marconi VC. Targeting inflammation to reduce atherosclerotic cardiovascular risk in people with HIV infection. J Am Heart Assoc. 2020;9(3):e014873.

    Article  Google Scholar 

  33. Libby P, Ridker PM, Hansson GK. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129–38.

    Article  CAS  Google Scholar 

  34. Hsue PY, Deeks SG, Hunt PW. Immunologic basis of cardiovascular disease in HIV-infected adults. J Infect Dis. 2012;205(Suppl 3):S375–82.

    Article  CAS  Google Scholar 

  35. Friis-Moller N, Thiebaut R, Reiss P, et al. Predicting the risk of cardiovascular disease in HIV-infected patients: the data collection on adverse effects of anti-HIV drugs study. Eur J Cardiovasc Prev Rehabil. 2010;17(5):491–501.

    Article  Google Scholar 

  36. Risk assessment for cardiovascular disease with nontraditional risk factors: recommendation statement. Am Fam Physician 2019;99(2):Online.

  37. Baker JV, Duprez D. Biomarkers and HIV-associated cardiovascular disease. Curr Opin HIV AIDS. 2010;5(6):511–6.

    Article  Google Scholar 

  38. Triant VA, Meigs JB, Grinspoon SK. Association of C-reactive protein and HIV infection with acute myocardial infarction. J Acquir Immune Defic Syndr. 2009;51(3):268–73.

    Article  CAS  Google Scholar 

  39. Pereira B, Mazzitelli M, Milinkovic A, Moyle G, Ranasinghe S, Mandalia S, et al. Use of coronary artery calcium scoring to improve cardiovascular risk stratification and guide decisions to start statin therapy in people living with HIV. J Acquir Immune Defic Syndr. 2020;85(1):98–105.

    Article  CAS  Google Scholar 

  40. Sherer R, Solomon S, Schechter M, Nachega JB, Rockstroh J, Zuniga JM. HIV provider-patient communication regarding cardiovascular risk: results from the AIDS Treatment for Life International Survey. J Int Assoc Provid AIDS Care. 2014;13(4):342–5.

    Article  Google Scholar 

  41. •• Feinstein MJ, Hsue PY, Benjamin LA, Bloomfield GS, Currier JS, Freiberg MS, et al. Characteristics, prevention, and management of cardiovascular disease in people living with HIV: a scientific statement from the American Heart Association. Circulation. 2019;140(2):e98–e124. A scientific statement document from AHA on CVD prevention in PLWH - an excellent resource for clinicians and researchers.

    Article  CAS  Google Scholar 

  42. •• Grinspoon SK, Douglas PS, Hoffmann U, Ribaudo HJ. Leveraging a landmark trial of primary cardiovascular disease prevention in human immunodeficiency virus: introduction from the REPRIEVE coprincipal investigators. J Infect Dis. 2020;222(Suppl 1):S1–7. First and largest trial in PLWH on CVD prevention in low-intermediate risk individuals.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Virginia A. Triant.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Co-infections and Comorbidity

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Achhra, A.C., Lyass, A., Borowsky, L. et al. Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations. Curr HIV/AIDS Rep 18, 271–279 (2021). https://doi.org/10.1007/s11904-021-00567-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11904-021-00567-w

Keywords

Navigation