Literature DB >> 19134234

Markov modelling of HIV infection evolution in the HAART era.

C Binquet1, G Le Teuff, M Abrahamovicz, A Mahboubi, Y Yazdanpanah, D Rey, C Rabaud, C Chirouze, J L Berger, J P Faller, P Chavanet, C Quantin, L Piroth.   

Abstract

The aim was to investigate the impact of the main prognostic factors on HIV evolution. A multi-state Markov model was applied in a cohort of 2126 patients to estimate impact of these factors on patients' clinical and immunological evolutions. Clinical progression and immunological deterioration shared most of their prognostic factors: male gender, intravenous drug use, weight loss, low haemoglobin level (<110 g/l), CD8 cell count (<500/mm(3)) and HIV viral load (>5 log(10) copies/ml). Highly active retroviral therapy reduced the risks of clinical progression and immune deterioration whatever patients' CD4 cell count. Risk reductions were 41-60% for protease inhibitor-based and 27-68% for non-nucleoside reverse transcriptase inhibitor-based regimens. Three-year transition probabilities showed that only patients with a CD4 cell count >or=350 CD4/mm(3) could in most cases maintain their immunity. This model provides 'real life' transition probabilities from one immunological stage to another, allowing decision analyses that could help determine the beneficial therapeutic strategies for HIV-infected patients.

Entities:  

Mesh:

Year:  2009        PMID: 19134234     DOI: 10.1017/S0950268808001775

Source DB:  PubMed          Journal:  Epidemiol Infect        ISSN: 0950-2688            Impact factor:   2.451


  3 in total

1.  HIV-1 disease progression during highly active antiretroviral therapy: an application using population-level data in British Columbia: 1996-2011.

Authors:  Bohdan Nosyk; Jeong Min; Viviane D Lima; Benita Yip; Robert S Hogg; Julio S G Montaner
Journal:  J Acquir Immune Defic Syndr       Date:  2013-08-15       Impact factor: 3.731

2.  Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.

Authors:  Zelalem G Dessie; Temesgen Zewotir; Henry Mwambi; Delia North
Journal:  BMC Infect Dis       Date:  2020-03-26       Impact factor: 3.090

3.  Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa.

Authors:  Claris Shoko; Delson Chikobvu
Journal:  Theor Biol Med Model       Date:  2018-01-18       Impact factor: 2.432

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.