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.
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-infectedpatients 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.
Authors: J Troy Grennan; Mona R Loutfy; DeSheng Su; P Richard Harrigan; Curtis Cooper; Marina Klein; Nima Machouf; Julio S G Montaner; Sean Rourke; Christos Tsoukas; Bob Hogg; Janet Raboud Journal: J Infect Dis Date: 2012-04-15 Impact factor: 5.226
Authors: Fred Stephen Sarfo; Maame Anima Sarfo; Betty Norman; Richard Phillips; George Bedu-Addo; David Chadwick Journal: PLoS One Date: 2014-10-23 Impact factor: 3.240