Viremia copy-years predicts mortality among treatment-naive HIV-infected patients initiating antiretroviral therapy. 2011

Michael J Mugavero, and Sonia Napravnik, and Stephen R Cole, and Joseph J Eron, and Bryan Lau, and Heidi M Crane, and Mari M Kitahata, and James H Willig, and Richard D Moore, and Steven G Deeks, and Michael S Saag, and
Department of Medicine, Division of Infectious Diseases, University of Alabama, Birmingham, AL, USA. mmugavero@uab.edu

BACKGROUND Cross-sectional plasma human immunodeficiency virus (HIV) viral load (VL) measures have proven invaluable for clinical and research purposes. However, cross-sectional VL measures fail to capture cumulative plasma HIV burden longitudinally. We evaluated the cumulative effect of exposure to HIV replication on mortality following initiation of combination antiretroviral therapy (ART). METHODS We included treatment-naive HIV-infected patients starting ART from 2000 to 2008 at 8 Center for AIDS Research Network of Integrated Clinical Systems sites. Viremia copy-years, a time-varying measure of cumulative plasma HIV exposure, were determined for each patient using the area under the VL curve. Multivariable Cox models were used to evaluate the independent association of viremia copy-years for all-cause mortality. RESULTS Among 2027 patients contributing 6579 person-years of follow-up, the median viremia copy-years was 5.3 log₁₀ copy × y/mL (interquartile range: 4.9-6.3 log₁₀ copy × y/mL), and 85 patients (4.2%) died. When evaluated separately, viremia copy-years (hazard ratio [HR] = 1.81 per log₁₀ copy × y/mL; 95% confidence interval [CI], 1.51-2.18 per log(10) copy × y/mL), 24-week VL (1.74 per log₁₀ copies/mL; 95% CI, 1.48-2.04 per log₁₀ copies/mL), and most recent VL (HR = 1.89 per log₁₀ copies/mL; 95% CI: 1.63-2.20 per log₁₀ copies/mL) were associated with increased mortality. When simultaneously evaluating VL measures and controlling for other covariates, viremia copy-years increased mortality risk (HR = 1.44 per log₁₀ copy × y/mL; 95% CI, 1.07-1.94 per log₁₀ copy × y/mL), whereas no cross-sectional VL measure was independently associated with mortality. CONCLUSIONS Viremia copy-years predicted all-cause mortality independent of traditional, cross-sectional VL measures and time-updated CD4+ T-lymphocyte count in ART-treated patients, suggesting cumulative HIV replication causes harm independent of its effect on the degree of immunodeficiency.

UI MeSH Term Description Entries
D008137 Longitudinal Studies Studies in which variables relating to an individual or group of individuals are assessed over a period of time. Bogalusa Heart Study,California Teachers Study,Framingham Heart Study,Jackson Heart Study,Longitudinal Survey,Tuskegee Syphilis Study,Bogalusa Heart Studies,California Teachers Studies,Framingham Heart Studies,Heart Studies, Bogalusa,Heart Studies, Framingham,Heart Studies, Jackson,Heart Study, Bogalusa,Heart Study, Framingham,Heart Study, Jackson,Jackson Heart Studies,Longitudinal Study,Longitudinal Surveys,Studies, Bogalusa Heart,Studies, California Teachers,Studies, Jackson Heart,Studies, Longitudinal,Study, Bogalusa Heart,Study, California Teachers,Study, Longitudinal,Survey, Longitudinal,Surveys, Longitudinal,Syphilis Studies, Tuskegee,Syphilis Study, Tuskegee,Teachers Studies, California,Teachers Study, California,Tuskegee Syphilis Studies
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D014766 Viremia The presence of viruses in the blood. Viremias
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015658 HIV Infections Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS). HTLV-III Infections,HTLV-III-LAV Infections,T-Lymphotropic Virus Type III Infections, Human,HIV Coinfection,Coinfection, HIV,Coinfections, HIV,HIV Coinfections,HIV Infection,HTLV III Infections,HTLV III LAV Infections,HTLV-III Infection,HTLV-III-LAV Infection,Infection, HIV,Infection, HTLV-III,Infection, HTLV-III-LAV,Infections, HIV,Infections, HTLV-III,Infections, HTLV-III-LAV,T Lymphotropic Virus Type III Infections, Human
D016019 Survival Analysis A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function. Analysis, Survival,Analyses, Survival,Survival Analyses

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