Assessment of the impact of adherence and other predictors during HAART on various CD4 cell responses in resource-limited settings
Authors Abrogoua DP, Kablan, Thierry Kamenan BA, Aulagner, N’Guessan K, Zohoré C
Received 24 September 2011
Accepted for publication 29 October 2011
Published 23 March 2012 Volume 2012:6 Pages 227—237
Review by Single anonymous peer review
Peer reviewer comments 2
Danho Pascal Abrogoua1,2, Brou Jerome Kablan1, Boua Alexis Thierry Kamenan1,3, Gilles Aulagner4, Konan N'Guessan1, Christian Zohoré1
1Laboratoire de Pharmacie Clinique, Pharmacologie et Therapeutique – UFR Sciences Pharmaceutiques et Biologiques, 2Laboratoire de Pharmacologie Clinique, CHU de Cocody, 3Service de Pharmacie, CHU de Cocody, Abidjan, Cote d'Ivoire, 4Service Pharmaceutique Hopital Louis Pradel, Lyon, France
Objective: The aim of this study was to quantify, by modeling, the impact of significant predictors on CD4 cell response during antiretroviral therapy in a resource-limited setting.
Methods: Modeling was used to determine which antiretroviral therapy response predictors (baseline CD4 cell count, clinical state, age, and adherence) significantly influence immunological response in terms of CD4 cell gain compared to a reference value at different periods of monitoring.
Results: At 6 months, CD4 cell response was significantly influenced by baseline CD4 count alone. The probability of no increase in CD4 cells was 2.6 higher in patients with a baseline CD4 cell count of ≥200/mm3. At 12 months, CD4 cell response was significantly influenced by both baseline CD4 cell count and adherence. The probability of no increase in CD4 cells was three times higher in patients with a baseline CD4 cell count of ≥200/mm3 and 0.15 times lower with adherent patients. At 18 months, CD4 cell response was also significantly influenced by both baseline CD4 cell count and adherence. The probability of no increase in CD4 cells was 5.1 times higher in patients with a baseline CD4 cell count of ≥200/mm3 and 0.28 times lower with adherent patients. At 24 months, optimal CD4 cell response was significantly influenced by adherence alone. Adherence increased the probability (by 5.8) of an optimal increase in CD4 cells. Age and baseline clinical state had no significant influence on immunological response.
Conclusion: The relationship between adherence and CD4 cell response was the most significant compared to that of baseline CD4 cell count. Counseling before initiation of treatment and educational therapy during follow-up must always help to strengthen adherence and optimize the efficiency of antiretroviral therapy in a resource-limited setting.
Keywords: antiretroviral therapy, CD4 cells response, adherence, predictors, modeling, Abidjan
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