Predictive factors for immunological and virological endpoints in Thai patients receiving combination antiretroviral treatment

Authors

  • P Srasuebkul,

    1. National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, NSW, Australia,
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  • C Ungsedhapand,

    1. HIV Netherlands Australia Thailand Research Collaboration and the Thai Red Cross AIDS Research Centre (HIV-NAT), Bangkok, Thailand,
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  • K Ruxrungtham,

    1. HIV Netherlands Australia Thailand Research Collaboration and the Thai Red Cross AIDS Research Centre (HIV-NAT), Bangkok, Thailand,
    2. Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, and
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  • MA Boyd,

    1. National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, NSW, Australia,
    2. HIV Netherlands Australia Thailand Research Collaboration and the Thai Red Cross AIDS Research Centre (HIV-NAT), Bangkok, Thailand,
    3. Department of Microbiology & Infectious Disease, Flinders Medical Centre, Bedford Park, SA, Australia
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  • P Phanuphak,

    1. HIV Netherlands Australia Thailand Research Collaboration and the Thai Red Cross AIDS Research Centre (HIV-NAT), Bangkok, Thailand,
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  • DA Cooper,

    1. National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, NSW, Australia,
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  • MG Law,

    1. National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, NSW, Australia,
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  • the HIV-NAT study team

    1. National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, NSW, Australia,
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Preeyaporn Srasuebkul, National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, 376 Victoria St, Sydney, NSW 2010, Australia. Tel: +61 (0)29385 0900; fax: +61 (0)29385 0920; email: psrasuebkul@nchecr.unsw.edu.au

Abstract

Background

Routine CD4 count and HIV viral load monitoring is a financial barrier in developing countries.

Methods

We assessed factors associated with CD4 counts ≤200 cells/μL and detectable viral load in Thai HIV-infected patients receiving antiretroviral therapy (ART) at the HIV Netherlands Australia Thailand Research Collaboration and the Thai Red Cross AIDS Research Centre (HIV-NAT). Univariate and multivariate Cox proportional hazards models for multiple treatment failures were used to determine factors related to CD4 counts ≤200 cells/μL and detectable viral load. Multivariate Cox proportional hazards models for CD4 counts ≤200 cells/μL were developed with and without viral load in order to build models applicable to contexts in which viral load is not available.

Results

Four hundred and seventeen patients were included in the study. Fifty-four per cent were male, and the median CD4 count and log10 viral load at baseline were 283 cells/μL and 4.3 log10 HIV-1 RNA copies/mL, respectively. Independent factors related to CD4 count ≤200 cells/μL were CD4 count at baseline [hazards ratio (HR) 0.20/100 cells/μL; 95% confidence interval (CI) 0.17–0.23] and changes in CD4 count (HR 0.22/100 cells/μL; 95% CI 0.17–0.28). Factors in multivariate models (in which viral load was considered for inclusion) were CD4 count at baseline (HR 0.21/100 cells/μL; 95% CI 0.18–0.24), changes in CD4 count (HR 0.25/100 cells/μL; 95% CI 0.19–0.32) and detectable viral load (HR 1.94; 95% CI 1.20–3.13). Predictive factors (independent of viral load) were triple ART or highly active antiretroviral therapy (HAART) (HR 0.28; 95% CI 0.22–0.36) and detectable viral load at baseline (HR 2.96; 95% CI 2.24–3.91).

Conclusions

CD4 count at baseline and changes in CD4 count were important in predicting CD4 counts ≤200 cells/μL. Triple ART and detectable viral load at baseline were important in predicting detectable viral load.

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