Literature DB >> 23568309

Multi-state models for defining degrees of chronicity related to HIV-infected patient therapy adherence.

Raquel de Vasconcellos Carvalhaes de Oliveira1, Silvia Emiko Shimakura, Dayse Pereira Campos, Flaviana Pavan Victoriano, Sayonara Rocha Ribeiro, Valdiléa G Veloso, Beatriz Grinsztejn, Marilia Sá Carvalho.   

Abstract

Few studies on AIDS that evaluate factors associated with treatment failure have considered the slow evolution of the disease and multiple health state transitions following the use of antiretrovirals. In this article we study factors associated with the progression between different stages of the disease, focusing on therapy adherence using a sample of 722 HIV+ patients followed up for 3 years. States were defined using the following classifications of the CD4 cell count: s₁ (CD4 ≥ 500); s₂ (350 ≤ CD4 < 500); and s₃ (CD4 < 350). The transitions between states were modeled using multi-state models. Antiretroviral therapy adherence and disease duration were associated with transitions between immune states during follow-up. Low adherence increased the hazard ratio of a transition between s₁ to s₂ and intermediate adherence increased the hazard ratio of a transition between s₂ to s₃. On the other hand, older age and disease duration between two and four years are protective factors for AIDS progression. Multi-state modeling is a powerful approach for studying chronic diseases and estimating factors associated with transitions between each stage of progression, thus enabling the use of more individualized and effective interventions.

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Year:  2013        PMID: 23568309     DOI: 10.1590/s0102-311x2013000800017

Source DB:  PubMed          Journal:  Cad Saude Publica        ISSN: 0102-311X            Impact factor:   1.632


  4 in total

1.  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

2.  Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach.

Authors:  Zelalem G Dessie; Temesgen Zewotir; Henry Mwambi; Delia North
Journal:  Infect Dis Ther       Date:  2020-04-21

3.  Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model.

Authors:  Zelalem G Dessie; Temesgen Zewotir; Henry Mwambi; Delia North
Journal:  Theor Biol Med Model       Date:  2020-06-23       Impact factor: 2.432

4.  Survival Analysis of Breast Cancer Patients after Surgery with an Intermediate Event: Application of Illness-Death Model.

Authors:  Morteza Hajihosseini; Javad Faradmal; Abdolazim Sadighi-Pashaki
Journal:  Iran J Public Health       Date:  2015-12       Impact factor: 1.429

  4 in total

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