Predictive factors for immunological and virological endpoints in Thai patients receiving combination antiretroviral treatment.
BACKGROUND
Routine CD4 count and HIV viral load monitoring is a financial barrier in developing countries.


METHODS
We assessed factors associated with CD4 counts < or =200 cells/microL 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 < or =200 cells/microL and detectable viral load. Multivariate Cox proportional hazards models for CD4 counts < or =200 cells/microL 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 log(10) viral load at baseline were 283 cells/microL and 4.3 log(10) HIV-1 RNA copies/mL, respectively. Independent factors related to CD4 count < or =200 cells/microL were CD4 count at baseline [hazards ratio (HR) 0.20/100 cells/microL; 95% confidence interval (CI) 0.17-0.23] and changes in CD4 count (HR 0.22/100 cells/microL; 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/microL; 95% CI 0.18-0.24), changes in CD4 count (HR 0.25/100 cells/microL; 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 < or =200 cells/microL. Triple ART and detectable viral load at baseline were important in predicting detectable viral load.
Srasuebkul
P
P
National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, 376 Victoria Street, Sydney, NSW 2010, Australia. psrasuebkul@nchecr.unsw.edu.au
Ungsedhapand
C
C
Ruxrungtham
K
K
Boyd
M A
MA
Phanuphak
P
P
Cooper
D A
DA
Law
M G
MG
D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use; D023241:Antiretroviral Therapy, Highly Active; D018791:CD4 Lymphocyte Count / Q000706:statistics & numerical data*; D004359:Drug Therapy, Combination; D005260:Female; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology* / Q000821:virology*; D015497:HIV-1 / Q000276:immunology*; D006801:Humans; D008297:Male; D011379:Prognosis; D016016:Proportional Hazards Models; D013785:Thailand; D017211:Treatment Failure; D019562:Viral Load / Q000706:statistics & numerical data*
D019380:Anti-HIV Agents