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. (29th August 2020)

• Record Type: Journal Article
• Authors: Friis-Møller, Nina; Ryom, Lene; Smith, Colette; Weber, Rainer; Reiss, Peter; Dabis, F; De Wit, Stephane; Monforte, Antonella D'Arminio; Kirk, Ole; Fontas, Eric; Sabin, Caroline; Phillips, Andrew; Lundgren, Jens; Law, Matthew
• Is Part Of: European journal of preventive cardiology. Volume 23:Number 2(2016)
• Page Start: 214
• Page End: 223
• Publication Date: 2020-08-29
• Subjects: HIV -- AIDS -- CVD risk prediction -- epidemiology; Cardiovascular system -- Diseases -- Prevention -- Periodicals; Cardiac patients -- Rehabilitation -- Periodicals; 616.12
• DOI: 10.1177/2047487315579291
• Languages: English
• ISSNs: 2047-4873

Abstract

Abstract: Background: With the aging of the population living with HIV, the absolute risk of cardiovascular disease (CVD) is increasing. There is a need to further facilitate the identification of persons at elevated risk in routine practice. Methods and results: Prospective information was collected on 32, 663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance of the D:A:D models were compared with a recent CVD prediction model from the Framingham study, which was assessed recalibrated to the D:A:D dataset. A total of 1010 CVD events occurred during 186, 364.5 person-years. The full D:A:D CVD prediction model included age, gender, systolic blood pressure, smoking status, family history of CVD, diabetes, total cholesterol, high-density lipoprotein, CD4 lymphocyte count, cumulative exposure to protease- and nucleoside reverse transcriptase-inhibitors, and current use of abacavir. A reduced model omitted antiretroviral therapies. The D:A:D models statistically significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p < 0.001). The D:A:D models also more accurately predicted five-year CVD-risk for key prognostic subgroups. Conclusions: An updated, easily recalibrated, global CVD-risk equation tailored to HIV-positive persons was developed using routinely collected CVD risk parameters and incorporating markers on immune function (CD4 lymphocyte count), and exposure to antiretroviral therapies. The estimated CVD risk can be used to quantify risk and to guide preventive care.