Predictors of CD4 cell recovery following initiation of antiretroviral therapy among HIV‐1 positive patients with well‐estimated dates of seroconversion
Objectives To investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre‐treatment CD4 counts, because analyses...
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| Published in | HIV medicine Vol. 19; no. 3; pp. 184 - 194 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Wiley Subscription Services, Inc
01.03.2018
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Objectives
To investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre‐treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases.
Methods
We used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV‐1 seroconverters. We analysed pre‐ and post‐treatment data of patients with seroconversion dates estimated January 2003–March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation.
Results
‘True’ CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post‐treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude.
Conclusions
CD4 count at cART initiation is the most important factor in predicting post‐treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid. |
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| AbstractList | To investigate factors that predict speed of recovery and long-term CD4 cell count in HIV-1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre-treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases.OBJECTIVESTo investigate factors that predict speed of recovery and long-term CD4 cell count in HIV-1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre-treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases.We used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV-1 seroconverters. We analysed pre- and post-treatment data of patients with seroconversion dates estimated January 2003-March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation.METHODSWe used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV-1 seroconverters. We analysed pre- and post-treatment data of patients with seroconversion dates estimated January 2003-March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation.'True' CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post-treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude.RESULTS'True' CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post-treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude.CD4 count at cART initiation is the most important factor in predicting post-treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid.CONCLUSIONSCD4 count at cART initiation is the most important factor in predicting post-treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid. ObjectivesTo investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre‐treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases.MethodsWe used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV‐1 seroconverters. We analysed pre‐ and post‐treatment data of patients with seroconversion dates estimated January 2003–March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation.Results‘True’ CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post‐treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude.ConclusionsCD4 count at cART initiation is the most important factor in predicting post‐treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid. Objectives To investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre‐treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases. Methods We used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV‐1 seroconverters. We analysed pre‐ and post‐treatment data of patients with seroconversion dates estimated January 2003–March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation. Results ‘True’ CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post‐treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude. Conclusions CD4 count at cART initiation is the most important factor in predicting post‐treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid. To investigate factors that predict speed of recovery and long-term CD4 cell count in HIV-1 seroconverters initiating combination antiretroviral therapy (cART), and to quantify the influence of very early treatment initiation. We make use of all pre-treatment CD4 counts, because analyses using only a single observation at initiation may be subject to biases. We used data from the CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe) multinational cohort collaboration of HIV-1 seroconverters. We analysed pre- and post-treatment data of patients with seroconversion dates estimated January 2003-March 2014 (n = 7600 for primary analysis) using a statistical model in which the characteristics of recovery in CD4 counts are determined by multiple predictive factors. Secondary analyses were performed incorporating uncertainty in the exact timing of seroconversion to allow more precise estimation of the benefit of very early treatment initiation. 'True' CD4 count at cART initiation was the strongest predictor of CD4 count beyond 3 years on cART. Allowing for lack of complete certainty in the date of seroconversion, CD4 recovery was more rapid for patients in whom treatment was initiated within 4 months. For a given CD4 count, higher viral load (VL) at initiation was strongly associated with higher post-treatment CD4 recovery. For other patient and drug characteristics, associations with recovery were statistically significant but small in magnitude. CD4 count at cART initiation is the most important factor in predicting post-treatment recovery, but VL provides substantial additional information. If cART is initiated in the first 4 months following seroconversion, recovery of CD4 counts appears to be more rapid. |
| Author | Ramacciotti, Tim Sannes, Mette Morrison, Charles Pérez‐Hoyos, Santiago Copas, AJ Porter, Kholoud Luca, Andrea Chipato, Tsungai Babiker, AG Cayla, Joan Stirrup, OT Touloumi, G Romero, Jorge Boufassa, Faroudy Monforte, Antonella Chaix, Marie‐Laure Sönnerborg, Anders Costagliola, Dominique Pillay, Deenan Lutsar, Irja Pantazis, Nikos Kelleher, Tony Tor, Jordi Helm, Jannie Meixenberger, Karolin Kran, Anne‐Marte Gill, MJ Bannert, Norbert Muga, Roberto Amo, Julia Guiguet, Marguerite Salata, Robert Gill, John Dabis, Francois Geskus, Ronald Bucher, HC Moreno, Santiago Zangerle, Robert Gilmour, Jill Smith, Don Bucher, C Cooper, David Olalla, Patricia Phillips, AN Scherrer, Alexandra Finlayson, Robert Monge, Susana Geskus, RB Antoniadou, Anastasia Price, Matt A. Ghosn, Jade Chêne, Geneviève Babiker, Abdel Malyuta, Ruslan Chrysos, Georgios Schuitemaker, Hanneke Karita, Etienne Thiebaut, Rodolphe Murphy, Gary Katsarou, Olga Rezza, Giovanni Vanhems, Philippe Hamouda, Osamah Dorrucci, Maria Johnson, Anne Young, J Bartmeyer, Barbara Meyer, Laurence Daikos, Georgios L. |
| AuthorAffiliation | 3 Department of Medicine University of Calgary Calgary AB Canada 6 Oxford University Clinical Research Unit Wellcome Trust Major Overseas Programme Ho Chi Minh City Vietnam 4 Department of Clinical Epidemiology, Biostatistics and Bioinformatics Academic Medical Center (AMC) Amsterdam The Netherlands 8 Department of Hygiene, Epidemiology and Medical Statistics, Medical School National and Kapodistrian University of Athens Athens Greece 7 Nuffield Department of Clinical Medicine Centre for Tropical Medicine and Global Health University of Oxford Oxford UK 1 MRC Clinical Trials Unit University College London London UK 2 Research Department of Infection & Population Health University College London London UK 5 Department of Infectious Diseases Public Health Service of Amsterdam Amsterdam The Netherlands 9 Basel Institute for Clinical Epidemiology and Biostatistics University Hospital Basel and University of Basel Basel Switzerland |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29230953$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Contributor | Ramacciotti, Tim Sannes, Mette Morrison, Charles Porter, Kholoud Luca, Andrea Chipato, Tsungai Cayla, Joan Romero, Jorge Boufassa, Faroudy Monforte, Antonella Sönnerborg, Anders Costagliola, Dominique Pillay, Deenan Lutsar, Irja Pantazis, Nikos Kelleher, Tony Tor, Jordi Helm, Jannie Meixenberger, Karolin Bannert, Norbert Muga, Roberto Amo, Julia Guiguet, Marguerite Salata, Robert Gill, John Dabis, Francois Geskus, Ronald Moreno, Santiago Zangerle, Robert Gilmour, Jill Smith, Don Bucher, C Daikos, Georgios L Pérez-Hoyos, Santiago Cooper, David Olalla, Patricia Scherrer, Alexandra Finlayson, Robert Monge, Susana Antoniadou, Anastasia Ghosn, Jade Chêne, Geneviève Babiker, Abdel Malyuta, Ruslan Chrysos, Georgios Schuitemaker, Hanneke Karita, Etienne Chaix, Marie-Laure Thiebaut, Rodolphe Murphy, Gary Katsarou, Olga Rezza, Giovanni Vanhems, Philippe Hamouda, Osamah Dorrucci, Maria Johnson, Anne Price, Matt A Bartmeyer, Barbara Meyer, Laurence Gelgor, Linda Kran, Anne-Marte Brubakk, Oddbjorn Kamali, Anatoli Prins, Maria Günthard, Huldrych Grey, Pat Bloch, M |
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| Copyright | 2017 The Authors. published by John Wiley & Sons Ltd on behalf of British HIV Association 2017 The Authors. HIV Medicine published by John Wiley & Sons Ltd on behalf of British HIV Association. HIV Medicine © 2018 British HIV Association |
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| DOI | 10.1111/hiv.12567 |
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| Keywords | CD4 ART HIV antiretroviral therapy HAART longitudinal data mixed effects model |
| Language | English |
| License | Attribution 2017 The Authors. HIV Medicine published by John Wiley & Sons Ltd on behalf of British HIV Association. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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| References | 2015; 2 2000; 356 2013; 368 2013; 20 2013; 63 2003; 17 2011; 12 1999; 103 2016; 16 2009; 48 2016; 35 2015; 68 1998; 17 2010; 24 2015; 29 2016; 3 2013; 10 2004; 190 2015; 44 2015; 373 2003; 4 2005; 54 2011; 67 2014 2012; 27 2011; 25 2007; 44 2014; 383 2007; 45 2010; 4 2014; 384 2003; 163 2016; 22 |
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publication-title: Stat Med – volume: 67 start-page: 467 year: 2011 end-page: 475 article-title: Estimation of the distribution of infection times using longitudinal serological markers of HIV: implications for the estimation of HIV incidence publication-title: Biometrics – volume: 17 start-page: 1907 year: 2003 end-page: 1915 article-title: Continued CD4 cell count increases in HIV‐infected adults experiencing 4 years of viral suppression on antiretroviral therapy publication-title: AIDS – volume: 12 start-page: 583 year: 2011 end-page: 593 article-title: Long‐term trends in CD4 cell counts and impact of viral failure in individuals starting antiretroviral therapy: UK Collaborative HIV Cohort (CHIC) study publication-title: HIV Med – volume: 27 start-page: 233 year: 2012 end-page: 249 article-title: AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models publication-title: Optim Methods Softw – volume: 4 start-page: 1847 year: 2010 end-page: 1870 article-title: Modeling the dynamics of biomarkers during primary HIV infection taking into account the uncertainty of infection date publication-title: Ann Appl Stat – volume: 63 start-page: 77 year: 2013 end-page: 85 article-title: Durable efficacy and safety of raltegravir versus efavirenz when combined with tenofovir/emtricitabine in treatment‐naive HIV‐1–infected patients: final 5‐year results from STARTMRK publication-title: J Acquir Immune Defic Syndr – volume: 373 start-page: 808 year: 2015 end-page: 822 article-title: A trial of early antiretrovirals and isoniazid preventive therapy in Africa publication-title: N Engl J Med |
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To investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral... To investigate factors that predict speed of recovery and long-term CD4 cell count in HIV-1 seroconverters initiating combination antiretroviral therapy... ObjectivesTo investigate factors that predict speed of recovery and long‐term CD4 cell count in HIV‐1 seroconverters initiating combination antiretroviral... |
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| SubjectTerms | Acquired immune deficiency syndrome AIDS Anti-HIV Agents - therapeutic use Antiretroviral agents Antiretroviral drugs Antiretroviral therapy CD4 CD4 antigen CD4 Lymphocyte Count Drug therapy Female HAART HIV HIV Infections - drug therapy HIV Infections - immunology HIV-1 - immunology Human immunodeficiency virus Humans longitudinal data Male Mathematical models mixed effects model Models, Statistical Original Research Patients Seroconversion Statistical analysis Statistical models Therapy Treatment Outcome Viral Load |
| Title | Predictors of CD4 cell recovery following initiation of antiretroviral therapy among HIV‐1 positive patients with well‐estimated dates of seroconversion |
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