Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model

Dessie, Zelalem G.; Zewotir, Temesgen; Mwambi, Henry; North, Delia

doi:10.1186/s12889-020-08530-x

“…The prognostic factors include (1) cART initiation, (2) demographics: age, marital status, educational level, and sex under the influence of alcohol; (3) OI: hypertension and tuberculosis; (4) clinical parameters: WBC components (neutrophils, lymphocyte count, monocytes, eosinophils count, and leucocyte count), Blood chemistry (sodium, chloride, calcium, ALT, AST, total protein and LDH), RBC parameters (Hb, RDW, MCH, MCV, MCHC, and hematocrit) (5) QoL domain scores. Detailed information about the above-mentioned covariates was given in [21,22,24].…”

Section: Variables and Measurementsmentioning

confidence: 99%

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

Dessie

¹

,

Zewotir

²

,

Mwambi

³

et al. 2020

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Background: Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements. Methods: The data is from an ongoing prospective cohort study conducted amongst adult women who are HIVinfected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied. Results: Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time. Conclusion: It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

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“…The fourth domain is devoted to the level of independence and measures areas such as work capacity, dependence on treatments, activities of daily living, and mobility. Further information about the above-mentioned factors was reported in [25,26]. (See Fig.…”

Section: Variables and Measurementsmentioning

confidence: 99%

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

Dessie

¹

,

Zewotir

²

,

Mwambi

³

et al. 2020

Theor Biol Med Model

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Background: HIV infected patients may experience many intermediate events including between-event transition throughout their follow up. Through modelling these transitions, we can gain a deeper understanding of HIV disease process and progression and of factors that influence the disease process and progression pathway. In this work, we present transition-specific parametric multi-state models to describe HIV disease process and progression. Methods: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIVinfected in KwaZulu-Natal, South Africa. Participants were enrolled during the acute HIV infection phase and then followed up during chronic infection, up to ART initiation. Results: Transition specific distributions for multi-state models, including a variety of accelerated failure time (AFT) models and proportional hazards (PH) models, were presented and compared in this study. The analysis revealed that women enrolling with a CD4 count less than 350 cells/mm 3 (severe and advanced disease stages) had a far lower chance of immune recovery, and a considerably higher chance of immune deterioration, compared to women enrolling with a CD4 count of 350 cells/mm 3 or more (normal and mild disease stages). Our analyses also showed that older age, higher educational levels, higher scores for red blood cell counts, higher mononuclear scores, higher granulocytes scores, and higher physical health scores, all had a significant effect on a shortened time to immunological recovery, while women with many sex partners, higher viral load and larger family size had a significant effect on accelerating time to immune deterioration. Conclusion: Multi-state modelling of transition-specific distributions offers a flexible tool for the study of demographic and clinical characteristics' effects on the entire disease progression pathway. It is hoped that the article will help applied researchers to familiarize themselves with the models, including interpretation of results.

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“…Although the World Health Organization (WHO) has noticed viral load suppression as one of the ultimate goals of 90-90-90, determined by UNAIDS, CD4 count has been widely used as the main marker of HIV progression in developing and low-income countries due to the high cost and nonavailability of viral load assays [4]. Modeling the CD4 cell count progression and the likelihood of HIV/AIDS transmission and progression is crucial for understanding the pathogenesis of HIV, improving treatment strategies, managing patients who have not undergone treatment, and determining the stage at which ART initiation is more clinically effective and less costly [5]. The immunological state of HIV/ AIDS patients carries dynamic properties, which can be changed over time.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Antiretroviral Treatment on the Transition Probability of Immunological State of HIV/AIDS Patients: A Markov Chain Model on the Iranian “National Registry of HIV/AIDS Care” Database

Mirahmadizadeh

¹

,

Sharafi

²

,

Hassanzadeh

³

et al. 2023

BioMed Research International

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Antiretroviral therapy (ART) has improved survival and clinical course amongst HIV/AIDS patients. CD4 cell count is one of the most critical indicators of the disease progression. With respect to the dynamic nature of CD4 cell count during the clinical history of HIV/AIDS, modeling the CD4 cell count changes, which represents the likelihood of disease progression, is of interest to establish or revise treatment strategies and, specifically, to determine the stage at which giving ART is more clinically effective. In this historical cohort study on 917 HIV/AIDS patients in the Iranian “National Registry of HIV/AIDS Care” database, we used the Markov chain model to predict the effectiveness of the ART based on the transition probability of CD4 cell count, measured before and after initiating ART. We found that when the ART was initiated in the earlier stages of HIV infection, good prognosis might be more accessible; that is, after initiating ART at state CD 4 ≥ 500 , the probability of staying at this state was statistically increased than before the treatment ( P < 0.001 ). Also, it was found that initiating ART significantly decreased the probability of CD4 cell count transition to the lower counts ( P CD 4 ≥ 500 ‐ to ‐ 350 ≤ CD 4 < 500 < 0.001 , P 350 ≤ CD 4 < 500 ‐ to ‐ 200 ≤ CD 4 < 350 < 0.001 , and P 200 ≤ CD 4 < 350 ‐ to ‐ CD 4 < 200 < 0.001 ). In addition, initiating ART showed a statistically significant increase in the probability of CD4 cell count transition from a lower state ( 350 ≤ CD 4 < 500 ) to a higher state ( CD 4 ≥ 500 ) ( P = 0.009 ). Furthermore, When CD4 count reaches under 200, even after the initiation of therapy, the probability of CD4 cell count transition to higher levels was not significant ( P > 0.05 ). In summary, these results have a message for primary healthcare organizations to extensively identify HIV patients and initiating ART as early as possible.

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Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model

Cited by 5 publications

References 62 publications

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

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

Effectiveness of Antiretroviral Treatment on the Transition Probability of Immunological State of HIV/AIDS Patients: A Markov Chain Model on the Iranian “National Registry of HIV/AIDS Care” Database

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