Abstract
Original language | English |
---|---|
Pages (from-to) | 214-223 |
Number of pages | 10 |
Journal | European Journal of Preventive Cardiology |
Volume | 23 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jan 2016 |
Keywords
- AIDS
- CVD risk prediction
- epidemiology
- HIV
- abacavir
- cholesterol
- high density lipoprotein
- indinavir
- lopinavir
- proteinase inhibitor
- RNA directed DNA polymerase inhibitor
- 2',3' dideoxynucleoside derivative
- Human immunodeficiency virus proteinase inhibitor
- adult
- age
- Article
- cardiovascular disease
- cardiovascular risk
- CD4 lymphocyte count
- cholesterol blood level
- clinical trial
- diabetes mellitus
- family history
- female
- Framingham risk score
- gender
- human
- Human immunodeficiency virus infected patient
- Human immunodeficiency virus infection
- male
- prediction
- priority journal
- proportional hazards model
- smoking
- systolic blood pressure
- blood
- blood pressure
- Cardiovascular Diseases
- genetic predisposition
- HIV Infections
- middle aged
- multicenter study
- prospective study
- risk assessment
- sex difference
- statistical model
- Adult
- Age Factors
- Blood Pressure
- CD4 Lymphocyte Count
- Cholesterol
- Diabetes Mellitus
- Dideoxynucleosides
- Female
- Genetic Predisposition to Disease
- HIV Protease Inhibitors
- Humans
- Lipoproteins, HDL
- Male
- Middle Aged
- Models, Statistical
- Prospective Studies
- Reverse Transcriptase Inhibitors
- Risk Assessment
- Sex Factors
- Smoking
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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 : European Journal of Preventive Cardiology. / Friis-Møller, N.; Ryom, L.; Smith, C. et al.
In: European Journal of Preventive Cardiology, Vol. 23, No. 2, 01.2016, p. 214-223.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - 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
T2 - European Journal of Preventive Cardiology
AU - Friis-Møller, N.
AU - Ryom, L.
AU - Smith, C.
AU - Weber, R.
AU - Reiss, P.
AU - Dabis, F.
AU - De Wit, S.
AU - Monforte, A.D.
AU - Kirk, O.
AU - Fontas, E.
AU - Sabin, C.
AU - Phillips, A.
AU - Lundgren, J.
AU - Law, M.
AU - Torres, Ferran
N1 - Cited By :129 Export Date: 17 February 2022 Correspondence Address: Friis-Møller, N.; Odense University Hospital, Denmark; email: nina.friis-moeller@rsyd.dk Chemicals/CAS: abacavir, 136470-78-5, 188062-50-2; cholesterol, 57-88-5; indinavir, 150378-17-9, 157810-81-6, 180683-37-8; lopinavir, 192725-17-0; proteinase inhibitor, 37205-61-1; abacavir; Cholesterol; Dideoxynucleosides; HIV Protease Inhibitors; Lipoproteins, HDL; Reverse Transcriptase Inhibitors Funding details: CT94-1637, CT97-2713 Funding details: CURE/97-46486 Funding details: National Institutes of Health, NIH Funding details: U.S. Food and Drug Administration, FDA Funding details: National Institute of Allergy and Infectious Diseases, NIAID, U01AI042170, U01AI046362, U01AI069907 Funding details: Bristol-Myers Squibb, BMS Funding details: Pfizer Funding details: GlaxoSmithKline, GSK Funding details: Merck Funding details: Roche Funding details: Gilead Sciences Funding details: AbbVie Funding details: Boehringer Ingelheim Funding details: Janssen Pharmaceuticals Funding details: Merck Sharp and Dohme, MSD Funding details: ViiV Healthcare Funding details: European Commission, EC Funding details: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF Funding details: University of New South Wales, UNSW Funding details: Agence Nationale de Recherches sur le Sida et les Hépatites Virales, ANRS Funding details: Fifth Framework Programme, FP5, QLK2-2000-00773 Funding details: Fundación para la Investigación y la Prevención del Sida en España, FIPSE, 5U01AI042170-10, 5U01AI046362-03, FIPSE 3171/00 Funding details: Instituto de Investigación Sanitaria Fundación Jiménez DÃaz, IIS-FJD, FIS 99/0887 Funding text 1: This work was supported by the Highly Active Antiretroviral Therapy Oversight Committee (HAART-OC), a collaborative committee with representation from academic institutions, the European Agency for the Evaluation of Medicinal Products, the United States Food and Drug Administration, the patient community, and all pharmaceutical companies with licensed anti-HIV drugs in the European Union: AbbVie, Boehringer Ingelheim, Bristol-Myers Squibb, Gilead Sciences, Viiv Healthcare, Merck, Pfizer, F Hoffman-LaRoche and Janssen Pharmaceuticals. Supported by the Health Insurance Fund Council, Amstelveen, the Netherlands (grant number CURE/97-46486 to the AIDS Therapy Evaluation Project Netherlands (ATHENA)); by the Agence Nationale de Recherches sur le SIDA (grant number Action Coordonnée no. 7, Cohortes to the Aquitaine Cohort); The Australian HIV Observational Database (AHOD) is funded as part of the Asia Pacific HIV Observational Database, a programme of The Foundation for AIDS Research, amfAR, and is supported in part by a grant from the US National Institutes of Health’s National Institute of Allergy and Infectious Diseases (NIAID) (grant number U01-AI069907) and by unconditional grants from Merck Sharp & Dohme; Gilead Sciences; Bristol-Myers Squibb; Boehringer Ingelheim Roche; Pfizer; GlaxoSmithKline; Janssen Pharmaceuticals. The Kirby Institute is funded by The Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, The University of New South Wales. Supported by the Fondo de Investigación Sanitaria (grant number FIS 99/0887) and Fundación para la Investigación y la Prevención del SIDA en Espanã (grant number FIPSE 3171/00 to the Barcelona Antiretroviral Surveillance Study (BASS)); by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (grants number 5U01AI042170-10, 5U01AI046362-03, to the Terry Beirn Community Programs for Clinical Research on AIDS (CPCRA)); by the BIOMED 1 (grant number CT94-1637) and BIOMED 2 (grant number CT97-2713) programmes and the fifth framework programme (grant number QLK2-2000-00773) of the European Commission; grants from Bristol-Myers Squibb, GlaxoSmithKline, Boehringer Ingelheim and Roche, to the EuroSIDA study; by unrestricted educational grants of AbbVie, Bristol-Myers Squibb, Gilead Sciences, GlaxoSmithKline, Pfizer, Janssen Pharmaceuticals to the Italian Cohort Naive to Antiretrovirals (The ICONA Foundation); and by a grant from the Swiss National Science Foundation, to the Swiss HIV Cohort Study (SHCS). References: McManus, H., O'Connor, C.C., Boyd, M., Long-term survival in HIV positive patients with up to 15 years of antiretroviral therapy (2012) PLoS One, 7, pp. e48839-e48839; Hasse, B., Ledergerber, B., Furrer, H., Morbidity and aging in HIV-infected persons: The Swiss HIV cohort study (2011) Clin Infect Dis, 53, pp. 1130-1139; Ryom, L., Mocroft, A., Kirk, O., Association between antiretroviral exposure and renal impairment among HIV-positive persons with normal baseline renal function: The D:A:D study (2013) J Infect Dis, 207, pp. 1359-1369; Rotger, M., Glass, T.R., Junier, T., Contribution of genetic background, traditional risk factors, and HIV-related factors to coronary artery disease events in HIV-positive persons (2013) Clin Infect Dis, 57, pp. 112-121; Freiberg, M.S., Chang, C.C., Kuller, L.H., HIV infection and the risk of acute myocardial infarction (2013) JAMA Intern Med, 173, pp. 614-622; http://www.eacsociety.org/guidelines/eacs-guidelines/eacs-guidelines.html, European AIDS Clinical Society (EACS) Guidelines, Version 7.1. 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Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents (1994) Circulation, 90, pp. 583-612; Kowalska, J.D., Friis-Moller, N., Kirk, O., The Coding Causes of Death in HIV (CoDe) Project: Initial results and evaluation of methodology (2011) Epidemiology, 22, pp. 516-523; Friis-Moller, N., Reiss, P., Sabin, C.A., Class of antiretroviral drugs and the risk of myocardial infarction (2007) N Engl J Med, 356, pp. 1723-1735; Worm, S.W., Sabin, C., Weber, R., Risk of myocardial infarction in patients with HIV infection exposed to specific individual antiretroviral drugs from the 3 major drug classes: The data collection on adverse events of anti-HIV drugs (D:A:D) study (2010) J Infect Dis, 201, pp. 318-330; Lang, S., Mary-Krause, M., Cotte, L., Impact of individual antiretroviral drugs on the risk of myocardial infarction in human immunodeficiency virus-infected patients: A case-control study nested within the French Hospital Database on HIV ANRS cohort CO4 (2010) Arch Intern Med, 170, pp. 1228-1238; Harrell, F.E., Jr., Lee, K.L., Mark, D.B., Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors (1996) Stat Med, 15, pp. 361-387; Royston, P., Parmar, M.K., Sylvester, R., Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer (2004) Stat Med, 23, pp. 907-926; Achhra, A.C., Amin, J., Sabin, C., Reclassification of risk of death with the knowledge of D-dimer in a cohort of treated HIV-infected individuals (2012) AIDS, 26, pp. 1707-1717; Cook, N.R., Ridker, P.M., Advances in measuring the effect of individual predictors of cardiovascular risk: The role of reclassification measures (2009) Ann Intern Med, 150, pp. 795-802; Sabin, C.A., Worm, S.W., Weber, R., Use of nucleoside reverse transcriptase inhibitors and risk of myocardial infarction in HIV-infected patients enrolled in the D:A:D study: A multi-cohort collaboration (2008) Lancet, 371, pp. 1417-1426; Lichtenstein, K.A., Armon, C., Buchacz, K., Low CD4+ T cell count is a risk factor for cardiovascular disease events in the HIV outpatient study (2010) Clin Infect Dis, 51, pp. 435-447; Triant, V.A., Regan, S., Lee, H., Association of immunologic and virologic factors with myocardial infarction rates in a US healthcare system (2010) J Acquir Immune Defic Syndr, 55, pp. 615-619; Sabin, C.A., Ryom, L., De Wit, S., Associations between immune depression and cardiovascular events in HIV infection (2013) AIDS, 27, pp. 2735-2748; Kaplan, R.C., Kingsley, L.A., Gange, S.J., Low CD4+ T-cell count as a major atherosclerosis risk factor in HIV-infected women and men (2008) AIDS, 22, pp. 1615-1624; Lang, S., Mary-Krause, M., Simon, A., HIV replication and immune status are independent predictors of the risk of myocardial infarction in HIV-infected individuals (2012) Clin Infect Dis, 55, pp. 600-607; Phillips, A.N., Neaton, J., Lundgren, J.D., The role of HIV in serious diseases other than AIDS (2008) AIDS, 22, pp. 2409-2418; De Backer, G., Ambrosioni, E., Borch-Johnsen, K., European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force of European and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (2003) Eur Heart J, 24, pp. 1601-1610; Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report (2002) Circulation, 106, pp. 3143-3421. , National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III); Duprez, D.A., Neuhaus, J., Kuller, L.H., Inflammation, coagulation and cardiovascular disease in HIV-infected individuals (2012) PLoS One, 7, pp. e44454-e44454
PY - 2016/1
Y1 - 2016/1
N2 - 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. © 2014 European Society of Cardiology.
AB - 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. © 2014 European Society of Cardiology.
KW - AIDS
KW - CVD risk prediction
KW - epidemiology
KW - HIV
KW - abacavir
KW - cholesterol
KW - high density lipoprotein
KW - indinavir
KW - lopinavir
KW - proteinase inhibitor
KW - RNA directed DNA polymerase inhibitor
KW - 2',3' dideoxynucleoside derivative
KW - Human immunodeficiency virus proteinase inhibitor
KW - adult
KW - age
KW - Article
KW - cardiovascular disease
KW - cardiovascular risk
KW - CD4 lymphocyte count
KW - cholesterol blood level
KW - clinical trial
KW - diabetes mellitus
KW - family history
KW - female
KW - Framingham risk score
KW - gender
KW - human
KW - Human immunodeficiency virus infected patient
KW - Human immunodeficiency virus infection
KW - male
KW - prediction
KW - priority journal
KW - proportional hazards model
KW - smoking
KW - systolic blood pressure
KW - blood
KW - blood pressure
KW - Cardiovascular Diseases
KW - genetic predisposition
KW - HIV Infections
KW - middle aged
KW - multicenter study
KW - prospective study
KW - risk assessment
KW - sex difference
KW - statistical model
KW - Adult
KW - Age Factors
KW - Blood Pressure
KW - CD4 Lymphocyte Count
KW - Cholesterol
KW - Diabetes Mellitus
KW - Dideoxynucleosides
KW - Female
KW - Genetic Predisposition to Disease
KW - HIV Protease Inhibitors
KW - Humans
KW - Lipoproteins, HDL
KW - Male
KW - Middle Aged
KW - Models, Statistical
KW - Prospective Studies
KW - Reverse Transcriptase Inhibitors
KW - Risk Assessment
KW - Sex Factors
KW - Smoking
U2 - 10.1177/2047487315579291
DO - 10.1177/2047487315579291
M3 - Article
C2 - 25882821
VL - 23
SP - 214
EP - 223
IS - 2
ER -