Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya

• Published in: BMC infectious diseases Vol. 22; no. 1; p. 29
• Main Authors: Mugo, Caroline W, Shkedy, Ziv, Mwalili, Samuel, Awoke, Tadesse, Braekers, Roel, Wandede, Dolphine, Mwachari, Christina
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.01.2022; BioMed Central; BMC
• Subjects: Anti-HIV Agents - therapeutic use; Antiretroviral agents; Antiretroviral drugs; Antiretroviral Therapy, Highly Active; CD4; CD4 antigen; CD4 Lymphocyte Count; CD4 lymphocytes; Comprehensive Health Care; Drug therapy; Evaluation; Female; Forecasts and trends; Hazard assessment; Health care; Highly active antiretroviral therapy; Highly active antiretroviral therapy(HAART); HIV; HIV Infections - drug therapy; HIV patients; HIV/AIDS; Human immunodeficiency virus; Humans; Immune system; Infectious diseases; Kenya; Medical research; Patient outcomes; Patients; Retrospective Studies; Statistical models; Switches; Teenagers; Trends; Viral Load; Zidovudine; Kenya; Nairobi Kenya; HIV/AIDS; CD4; Highly active antiretroviral therapy(HAART)
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-021-06977-w

In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly active antiretroviral therapy (HAART) in a comprehensive health care clinic in Kenya between 2011 and 2017. We evaluated the rate of change in CD4 cell count in response to antiretroviral treatment. We further assessed factors that influenced time to treatment change focusing on baseline characteristics of the patients and different initial drug regimens used. This was a retrospective study involving 432 naïve HIV patients that had at least two CD4 count measurements for the period. The relationship between CD4 cell count and time was modeled using a semi parametric mixed effects model while the Cox proportional hazards model was used to assess factors associated with the first regimen change. Majority of the patients were females and the average CD4 count at start of treatment was 362.1 [Formula: see text]. The CD4 count measurements increased nonlinearly over time and these trends were similar regardless of the treatment regimen administered to the patients. The change of logarithm CD4 cell count rises fast for in the first 450 days of antiretroviral initiation. The average time to first regimen change was 2142 days. Tenoforvir (TDF) based regimens had a lower drug substitution(aHR 0.2682, 95% CI:0.08263- 0.8706) compared to Zidovudine(AZT). The backbone used was found to be associated with regimen changes among the patients with fewer switches being observed, with the use of TDF when compared to AZT. There was however no significant difference between TDF and AZT in terms of the rate of change in logarithm CD4 count over time.