Progress realized: trends in HIV-1 viral load and CD4 cell count in a tertiary-care center from 1999 through 2011

PLoS One. 2013;8(2):e56845. doi: 10.1371/journal.pone.0056845. Epub 2013 Feb 20.

Abstract

Background: HIV-1 RNA and CD4 cell counts are important parameters for HIV care. The objective of this study was to assess the overall trends in HIV-1 viral load and CD4 cell counts within our clinic.

Methods: Patients with at least one of each test performed by the Infectious Diseases Laboratory from 1999 through 2011 were included in this analysis. By adapting a novel statistical model, log(10) HIV-1 RNA means were estimated by month, and log(10)-transformed HIV-1 RNA means were estimated by calendar year. Geometric means were calculated for CD4 cell counts by month and calendar year. Log(10) HIV-1 RNA and CD4 cell count monthly means were also examined with polynomial regression.

Results: There were 1,814 individuals with approximately 25,000 paired tests over the 13-year observation period. Based on each patient's final value of the year, the percentage of patients with viral loads below the lower limit of quantitation rose from 29% in 1999 to 72% in 2011, while the percentage with CD4 counts <200 cells/µL fell from 31% to 11%. On average annually, the mean HIV-1 RNA decreased by 86 copies/mL and the mean CD4 counts increased by 16 cells/µL. For the monthly means, the correlations (R(2)) from second-order polynomial regressions were 0.944 for log(10) HIV-1 RNA and 0.840 for CD4 cell counts.

Conclusions: Marked improvements in HIV-1 RNA suppression and CD4 cell counts were achieved in a large inner-city population from 1999 through 2011. This success demonstrates that sustained viral control with improved immunologic status can be a realistic goal for most individuals in clinical care.

MeSH terms

  • CD4 Lymphocyte Count*
  • Disease Progression
  • Follow-Up Studies
  • HIV Infections / immunology*
  • HIV Infections / virology*
  • HIV-1*
  • Humans
  • Retrospective Studies
  • Tertiary Care Centers*
  • Time Factors
  • Viral Load*