OBJECTIVES: Total lymphocyte counts (TLC) may be used as an alternative for CD4 cell counts to monitor HIV infection in resource-limited settings, where CD4 cell counts are too expensive or not available. METHODS: We used prospectively collected patient data from an urban HIV clinic in Indonesia. Predictors of mortality were identified via Cox regression, and the relation between TLC and CD4 cell counts was calculated by linear regression. Receiver operating characteristics (ROC) curves were used to choose the cut-off values of TLC corresponding with CD4 cell counts <200 and ≤350 cells/μl. Based on these analyses, we designed TLC-based treatment algorithms. RESULTS: Of 889 antiretroviral treatment (ART)-naïve subjects included, 66% had CD4 cell counts <200 and 81% had 350 ≤ cells/μl at baseline. TLC and CD4 cell count were equally strong predictors of mortality in our population, where ART was started based on CD4 cell count criteria. The correlation coefficient (R) between TLC and √CD4 was 0.70. Optimal cut-off values for TLC to identify patients with CD4 cell counts <200 and ≤350 cells/μl were 1500 and 1700 cells/μl, respectively. Treatment algorithms based on a combination of TLC, gender, oral thrush, anaemia and body mass index performed better in terms of predictive value than WHO staging or TLC alone. In our cohort, such an algorithm would on average have saved $14.05 per patient. CONCLUSION: Total lymphocyte counts is a good marker for HIV-associated mortality. Simple algorithms including TLC can prioritize patients for HIV treatment in a resource-limited setting, until affordable CD4 cell counts will be universally available.
OBJECTIVES: Total lymphocyte counts (TLC) may be used as an alternative for CD4 cell counts to monitor HIV infection in resource-limited settings, where CD4 cell counts are too expensive or not available. METHODS: We used prospectively collected patient data from an urban HIV clinic in Indonesia. Predictors of mortality were identified via Cox regression, and the relation between TLC and CD4 cell counts was calculated by linear regression. Receiver operating characteristics (ROC) curves were used to choose the cut-off values of TLC corresponding with CD4 cell counts <200 and ≤350 cells/μl. Based on these analyses, we designed TLC-based treatment algorithms. RESULTS: Of 889 antiretroviral treatment (ART)-naïve subjects included, 66% had CD4 cell counts <200 and 81% had 350 ≤ cells/μl at baseline. TLC and CD4 cell count were equally strong predictors of mortality in our population, where ART was started based on CD4 cell count criteria. The correlation coefficient (R) between TLC and √CD4 was 0.70. Optimal cut-off values for TLC to identify patients with CD4 cell counts <200 and ≤350 cells/μl were 1500 and 1700 cells/μl, respectively. Treatment algorithms based on a combination of TLC, gender, oral thrush, anaemia and body mass index performed better in terms of predictive value than WHO staging or TLC alone. In our cohort, such an algorithm would on average have saved $14.05 per patient. CONCLUSION: Total lymphocyte counts is a good marker for HIV-associated mortality. Simple algorithms including TLC can prioritize patients for HIV treatment in a resource-limited setting, until affordable CD4 cell counts will be universally available.
Authors: Adiatma Y M Siregar; Noor Tromp; Dindin Komarudin; Rudi Wisaksana; Reinout van Crevel; Andre van der Ven; Rob Baltussen Journal: BMC Health Serv Res Date: 2015-09-30 Impact factor: 2.655