Predictors of mortality in a cohort of HIV-1-infected adults in rural Africa

J Acquir Immune Defic Syndr. 2007 Apr 1;44(4):478-83. doi: 10.1097/QAI.0b013e318032bbcd.

Abstract

Background: CD4 cell count and plasma HIV RNA level are used to monitor HIV-infected patients in high-income countries, but the applicability in an African context with frequent concomitant infections has only been studied sparsely. Moreover, alternative inexpensive markers are needed in the attempts to roll out antiretroviral treatment in the region. We explored the prognostic strengths of classic and alternative progression markers in this study set in rural Zimbabwe.

Methods: We followed 196 treatment-naive HIV-1-infected patients from the Mupfure Schistosomiasis and HIV Cohort, Zimbabwe. CD4 cell count, HIV RNA level, hemoglobin (HB), total lymphocyte count (TLC), body mass index, clinical staging (Centers for Disease Control and Prevention [CDC] classification), and self-reported level of function (Karnofsky Performance Scale score) were assessed at baseline; participants were followed until death or last follow-up (3-4.3 years).

Results: All parameters except TLC predicted survival in univariate Cox models. HIV RNA level (P = 0.001), HB (P = 0.018), CD4 cell count (P = 0.047), and CDC category C (P = 0.007) remained significant in multivariate analysis.

Conclusions: We found HIV RNA level and CD4 cell count to predict mortality with prognostic capabilities similar to findings from high-income countries. HB and clinical staging were strong independent predictors and might be considered candidates for alternative HIV progression markers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • CD4 Lymphocyte Count
  • Cohort Studies
  • Female
  • Follow-Up Studies
  • HIV Infections / complications
  • HIV Infections / immunology
  • HIV Infections / mortality*
  • HIV-1 / genetics
  • Hemoglobins / analysis
  • Humans
  • Kaplan-Meier Estimate
  • Karnofsky Performance Status
  • Lymphocyte Count
  • Male
  • Multivariate Analysis
  • Predictive Value of Tests
  • Prognosis
  • Proportional Hazards Models
  • RNA, Viral / blood
  • Rural Health / statistics & numerical data*
  • Schistosomiasis / complications*
  • Survival Rate
  • Zimbabwe

Substances

  • Hemoglobins
  • RNA, Viral