Robert Dubrow1, Li Qin, Haiqun Lin, Raúl U Hernández-Ramírez, Romain S Neugebauer, Wendy Leyden, Keri N Althoff, Chad J Achenbach, Nancy A Hessol, Sharada P Modur, Gypsyamber DʼSouza, Ronald J Bosch, Surbhi Grover, Michael A Horberg, Mari M Kitahata, Angel M Mayor, Richard M Novak, Charles S Rabkin, Timothy R Sterling, James J Goedert, Amy C Justice, Eric A Engels, Richard D Moore, Michael J Silverberg. 1. *Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale School of Medicine, New Haven, CT; †Department of Internal Medicine, Yale School of Medicine, New Haven, CT; ‡Department of Biostatistics, Yale School of Public Health, Yale School of Medicine, New Haven, CT; §Division of Research, Kaiser Permanente, Oakland, CA; ‖Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD; ¶Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL; #Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA; **Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; ††Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; ‡‡Department of Oncology, Princess Marina Hospital, Gaborone, Botswana; §§Mid-Atlantic Permanente Research Institute, Kaiser Permanente Mid-Atlantic States, Rockville, MD; ‖‖Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA; ¶¶Department of Internal Medicine, Universidad Central del Caribe School of Medicine, Bayamon, Puerto Rico; ##Division of Infectious Diseases, University of Illinois College of Medicine, Chicago, IL; ***Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD; †††Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; ‡‡‡Department of Health Policy and Management, Yale School of Public Health, New Haven, CT; §§§Research Service, Veterans Affairs Connecticut Healthcare System, West Haven, CT; and ‖‖‖Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
Abstract
BACKGROUND: Kaposi sarcoma (KS) remains common among HIV-infected persons. To better understand KS etiology and to help target prevention efforts, we comprehensively examined a variety of CD4 T-cell count and HIV-1 RNA viral load (VL) measures, as well as antiretroviral therapy (ART) use, to determine independent predictors of KS risk. SETTING: North American AIDS Cohort Collaboration on Research and Design. METHODS: We followed HIV-infected persons during 1996-2009 from 18 cohorts. We used time-updated Cox regression to model relationships between KS risk and recent, lagged, trajectory, and cumulative CD4 count or VL measures, as well as ART use. We used Akaike's information criterion and global P values to derive a final model. RESULTS: In separate models, the relationship between each measure and KS risk was highly significant (P < 0.0001). Our final mutually adjusted model included recent CD4 count [hazard ratio (HR) for <50 vs. ≥500 cells/μL = 12.4; 95% confidence interval (CI): 6.5 to 23.8], recent VL (HR for ≥100,000 vs. ≤500 copies/mL = 3.8; 95% CI: 2.0 to 7.3), and cumulative (time-weighted mean) VL (HR for ≥100,000 vs. ≤500 copies/mL = 2.5; 95% CI: 1.0 to 5.9). Each P-trend was <0.0001. After adjusting for these measures, we did not detect an independent association between ART use and KS risk. CONCLUSIONS: Our results suggested a multifactorial etiology for KS, with early and late phases of development. The cumulative VL effect suggested that controlling HIV replication promptly after HIV diagnosis is important for KS prevention. We observed no evidence for direct anti-KS activity of ART, independent of CD4 count and VL.
BACKGROUND:Kaposi sarcoma (KS) remains common among HIV-infectedpersons. To better understand KS etiology and to help target prevention efforts, we comprehensively examined a variety of CD4 T-cell count and HIV-1 RNA viral load (VL) measures, as well as antiretroviral therapy (ART) use, to determine independent predictors of KS risk. SETTING: North American AIDS Cohort Collaboration on Research and Design. METHODS: We followed HIV-infectedpersons during 1996-2009 from 18 cohorts. We used time-updated Cox regression to model relationships between KS risk and recent, lagged, trajectory, and cumulative CD4 count or VL measures, as well as ART use. We used Akaike's information criterion and global P values to derive a final model. RESULTS: In separate models, the relationship between each measure and KS risk was highly significant (P < 0.0001). Our final mutually adjusted model included recent CD4 count [hazard ratio (HR) for <50 vs. ≥500 cells/μL = 12.4; 95% confidence interval (CI): 6.5 to 23.8], recent VL (HR for ≥100,000 vs. ≤500 copies/mL = 3.8; 95% CI: 2.0 to 7.3), and cumulative (time-weighted mean) VL (HR for ≥100,000 vs. ≤500 copies/mL = 2.5; 95% CI: 1.0 to 5.9). Each P-trend was <0.0001. After adjusting for these measures, we did not detect an independent association between ART use and KS risk. CONCLUSIONS: Our results suggested a multifactorial etiology for KS, with early and late phases of development. The cumulative VL effect suggested that controlling HIV replication promptly after HIV diagnosis is important for KS prevention. We observed no evidence for direct anti-KS activity of ART, independent of CD4 count and VL.
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