Awoke Ayele, Tadesse and Worku, Alemayehu and Kebede, Yigzaw and Zuma, Khangelani and Kasim, Adetayo and Shkedy, Ziv (2019) 'Model-based prediction of CD4 cells counts in HIV-infected adults on antiretroviral therapy in Northwest Ethiopia : a flexible mixed effects approach.', PLOS ONE, 14 (7). e0218514.
Background CD4 cell counts is widely used as a biomarker for treatment progression when studying the efficacy of drugs to treat HIV-infected patients. In the past, it had been also used in determining eligibility to initiate antiretroviral therapy. The main aim of this was to model the evolution of CD4 counts over time and use this model for an early prediction of subject-specific time to cross a pre-specified CD4 threshold. Methods Hospital based retrospective cohort study of HIV-infected patients was conducted from January 2009 to December 2014 at University of Gondar hospital, Northwest Ethiopia. Fractional polynomial random effect model is used to model the evolution of CD4 counts over time in response to treatment and to estimate the individual probability to be above a pre-selected CD4 threshold. Human subject research approval for this study was received from University of Gondar Research Ethics Committee and the medical director of the hospital. Results A total of 1347 patients were included in the analysis presented in this paper. The cohort contributed a total of 236.58 per 100 person-years of follow-up. Later the data were divided into two periods: the first is the estimation period in which the parameters of the model are estimated and the second is the prediction period. Based on the parameters from the estimation period, model based prediction for the time to cross a threshold was estimated. The correlations between observed and predicted values of CD4 levels in the estimation period were 0.977 and 0.982 for Neverapine and Efavirenz containing regimens, respectively; while the correlation between the observed and predicted CD4 counts in the prediction period are 0.742 and 0.805 for NVP and EFV, respectively. Conclusions The model enables us to estimate a subject-specific expected time to cross a CD4 threshold and to estimate a subject-specific probability to have CD4 count above a pre-specified threshold at each time point. By predicting long-term outcomes of CD4 count of the patients one can advise patient about the potential ART benefits that accrue in the long-term.
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|Publisher Web site:||https://doi.org/10.1371/journal.pone.0218514|
|Publisher statement:||©2019 Awoke Ayele et al. This is an open access article distributedunder the terms of the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproductionin any medium,provided the original author and source are credited.|
|Date accepted:||04 June 2019|
|Date deposited:||26 July 2019|
|Date of first online publication:||10 July 2019|
|Date first made open access:||26 July 2019|
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