Survival and predictors of death in tuberculosis/HIV coinfection cases in Porto Alegre, Brazil: A historical cohort from 2009 to 2013

Brand, Évelin Maria; Rossetto, Maíra; Hentges, Bruna; Winkler, Gerson Barreto; Duarte, Êrica Rosalba Mallmann; Silva, Lucas Cardoso da; Leal, Andréa Fachel; Knauth, Daniela Riva; Silva, Danielle Lodi; Mantese, George; Volpato, Tiane Farias; Bobek, Paulo Ricardo; Dellanhese, Amanda Pereira Ferreira; Teixeira, Luciana Barcellos

doi:10.1371/journal.pgph.0000051

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…But, in the present study, the lognormal AFT model was the best fit for the data. The followup period of this study also revealed that there were 29.5% deaths among HIV/TB co-infected patients, which might be comparable with a study of Sanzana [17] and higher and lower than studies of [18][19][20][21][22][23] and [1,16,[24][25][26], respectively. In this study, female patients presented slightly higher survival time than male patients as indicated in the K-M plot of patients, which might show a comparable result with studies of Sanzana [17] and Kosgei et al [27].…”

Section: Discussionsupporting

confidence: 86%

A survival analysis of prognostic determinant factors of time-to-death of HIV/TB co-infected patients under HAART followed-up in a public hospital in Ethiopia

Gebreyes¹

2023

hivar

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Introduction: Human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) and its' damage are prevailing at a shocking level in the world, and tuberculosis (TB) also adds to this damage, which make things "Mumps on the Goiter". In this case, highly active antiretroviral treatment (HAART) plays a great role in reducing the damage, and it is a lifetime treatment to reduce HIV-related mortality and morbidity, and prolong patients' survival time. Material and methods:A retrospective survival study was conducted among 407 HIV-positive TB co-infected patients under HAART to observe the effects of HAART treatment and other covariates for the improvement of patient's life expectancy. Appropriate survival model was selected using AIC, BIC, and log-likelihood values.Results: Out of the total 407 patients, 120 (29.48%) experienced the event of interest. A majority (n = 74, 61.67%) of those who died of HIV/TB co-infection were males, 108 (90%) had pulmonary TB, and 12 (10%) patients suffered from extra-pulmonary TB. For the log-normal AFT model, mari tal status, WHO clinical stages, functional status, antiretroviral treatment (ART) regimen, religion, sqrt CD4+, and baseline CD4+ were among significant predictors at a 5% level of significance for the change in patient's lifetime.Conclusions: From this study, AFT models presented a better fit compared with Cox regression model. Among AFT models, the log-normal AFT model was selected, and hence, the study showed that prognostic factors, such as WHO clinical stages, functional status, sqrt CD4+ counts, ART regimen, marital status, baseline CD4+ counts, and their interactions with time, were among the significant predictors for the selected model at 5% significance level.

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Section: Discussionsupporting

confidence: 86%

A survival analysis of prognostic determinant factors of time-to-death of HIV/TB co-infected patients under HAART followed-up in a public hospital in Ethiopia

Gebreyes¹

2023

hivar

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“…Of these, approximately 3 million people with TB did not have access to adequate treatment, moreover, only one in three persons with drug‐resistant TB receive treatment, making the situation much worse (WHO) 3 . Tuberculosis is also the major cause of death among HIV‐positive patients 4 . One key element to TB cure is adherence to treatment and treatment abandonment is related to a low probability of cure 5 …”

Section: Introductionmentioning

confidence: 99%

Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data

Schneider,

dos Reis,

Gottselig

et al. 2023

Statistics in Medicine

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Ignoring the presence of dependent censoring in data analysis can lead to biased estimates, for example, not considering the effect of abandonment of the tuberculosis treatment may influence inferences about the cure probability. In order to assess the relationship between cure and abandonment outcomes, we propose a copula Bayesian approach. Therefore, the main objective of this work is to introduce a Bayesian survival regression model, capable of taking into account the dependent censoring in the adjustment. So, this proposed approach is based on Clayton's copula, to provide the relation between survival and dependent censoring times. In addition, the Weibull and the piecewise exponential marginal distributions are considered in order to fit the times. A simulation study is carried out to perform comparisons between different scenarios of dependence, different specifications of prior distributions, and comparisons with the maximum likelihood inference. Finally, we apply the proposed approach to a tuberculosis treatment adherence dataset of an HIV cohort from Alvorada‐RS, Brazil. Results show that cure and abandonment outcomes are negatively correlated, that is, as long as the chance of abandoning the treatment increases, the chance of tuberculosis cure decreases.

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A longitudinally measured disease progression-marker data analysis of HIV/TB co-infected patients taking HAART drugs in a public Hospital, Ethiopia

Gebreyes

2024

Preprint

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Background The applications of HAART drugs play a great role in decreasing the incidence of TB and TB related death in persons living with HIV/AIDS. However, after HAART initiation, a considerable number of the TB infection and TB-HIV damages are still at a shocking level especially we could understand that the addition of TB to this damage made things “Mumps on the Goiter”. Methods A 407 co-infection patients data under HAART follow up and diagnosed for TB were analyzed using linear mixed model to explain the effects of HAART treatment for the improvement of patient’s CD4 count. The appropriate fixed and random effect model was selected using AIC, BIC, and log-likelihood values. Results Out of a total of 407 HIV/TB co-infected patients, 196 (48.16%) were females. When we look at the baseline functional status of patients, 24.57% were able to do their day to day activities like farming, harvesting, office work and others, 62.16% were ambulatory and 13.27% were bedridden for all visit times. Based on linear mixed model, the study results reveal that, duration of treatment, age, marital status, functional status, ART_Regimen, education and base line CD4 + count were among the significant predictors of linear mixed effect model at 5% significance level for the change of CD4 + counts over time. Conclusions Linear mixed effect model with random intercept and random slope model, and an autoregressive of order 1 (AR (1)) covariance structure have been used to model this correlated data type and a random variables effect. Age, marital status, functional status, ART_Regimen, education and base line CD4 + count were identified as a potential risk factor for the progression of CD4 + count of the patients. Patients who have a good baseline CD4 count had a positive significant effect for the improvement of CD4 counts of the patients over time.

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Survival and predictors of death in tuberculosis/HIV coinfection cases in Porto Alegre, Brazil: A historical cohort from 2009 to 2013

Cited by 3 publications

References 26 publications

A survival analysis of prognostic determinant factors of time-to-death of HIV/TB co-infected patients under HAART followed-up in a public hospital in Ethiopia

A survival analysis of prognostic determinant factors of time-to-death of HIV/TB co-infected patients under HAART followed-up in a public hospital in Ethiopia

Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data

A longitudinally measured disease progression-marker data analysis of HIV/TB co-infected patients taking HAART drugs in a public Hospital, Ethiopia

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