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

Dual role of hepcidin in response to pathogens

Hepcidin is the primary regulator of vertebrate iron homeostasis. Its production is stimulated by systemic iron levels and inflammatory signals. Although the role of hepcidin in iron homeostasis is well characterized, its response to pathogenic agents is complex and diverse. In this review, we examine studies that investigate the role of hepcidin in response to infectious agents. Interleukin-6 (IL-6) is a key factor responsible for the induction of hepcidin expression. During infection, hepcidin-mediated depletion of extracellular iron serves as a protective mechanism against a variety of pathogens. However, accumulation of iron in macrophages through hepcidin-mediated pathways may increase susceptibility to intracellular pathogens such as Mycobacterium tuberculosis. Prolonged elevation of hepcidin production can lead to anemia due to reduced iron availability for erythropoiesis, a condition referred to as anemia of inflammation. In addition, we highlight the role of hepcidin upregulation in several infectious contexts, including HIV-associated anemia, iron deficiency anemia in Helicobacter pylori infection, and post-malarial anemia in pediatric patients. In addition, we show that certain infectious agents, such as hepatitis C virus (HCV), can suppress hepcidin production during both the acute and chronic phases of infection, while hepatitis B virus (HBV) exhibits similar suppression during the chronic phase.

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis

Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.

Individualized survival predictions using state space model with longitudinal and survival data

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Dynamic predictions from longitudinal CD4 count measures and time to death of HIV/AIDS patients using a Bayesian joint model

A Bayesian joint modeling approach to dynamic prediction of HIV progression and mortality allows individualized predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival. Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging. The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7-39 months. The estimates of the association structure parameters from two of the three models considered indicated that the HIV mortality hazard at any time point is associated with the rate of change in the underlying value of the CD4 count. Model averaging the dynamic predictions resulted in only one of the hypothesized association structures having non-zero weight in almost all time points for each individual, with the exception of twelve patients, for whom other association structures were preferred at a few time points. The predictions were found to be different when we averaged them over models than when we derived them from the highest posterior weight model alone. The model with highest posterior weight for almost all time points for each individual gave an estimate of the association parameter of -0.02 implying that for a unit increase in the CD4 count, the hazard of HIV mortality decreases by a factor (hazard ratio) of 0.98. Functional status and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements.

HIV Infection Progression Monitoring System Based on CD4 Counts and Wishart Distribution

The human immunodeficiency virus (HIV) is a viral infection that destroys the human immune system, resulting in the acquired immunodeficiency syndrome (AIDS). The management and care of patients on antiretroviral therapy (ART) consumes a large portion of the health budget of many countries. ART improves the lives of the HIV patients. However, benefiting from the treatment remains to be low due to the nonadherence, adverse events, and treatment failure associated with the transmitted drug resistance mutations (TDRMs). Extra care is therefore required in prescribing switch of ART regimens for HIV-naive patients. We propose a disease monitoring system, which depends on how the HIV-naive patients respond to the ART regimen. We model cluster of differentiation 4 (CD4) counts data measured at every 3 months in a period of 48 weeks on a cohort of 87 HIV-naive patients on ART, from Zambia. We demonstrate how to apply the Bayesian Wishart distribution to model CD4 counts, leading to an informative HIV progression monitoring system. We found a steady increase in the average of the CD4 counts (from 219 to 315) for HIV-naive patients on the ART regimen. The average was still below the expected 500 CD4 counts for a normal person. The derived precision matrix shows an increase in probability of potency of the ART regimen, which ranges from 0.1261 to 0.8678. An early detection is crucial as it allows for timely switch of regimen from first to second line or to the third line. The proposed HIV disease progression monitoring system for HIV-naive patients on ART regimen that is based on CD4 counts could enable physicians make informed decisions on the management and care of the patients.

Exhausting T Cells During HIV Infection May Improve the Prognosis of Patients with COVID-19

T-cell reduction is an important characteristic of coronavirus disease 2019 (COVID-19), and its immunopathology is a subject of debate. It may be due to the direct effect of the virus on T-cell exhaustion or indirectly due to T cells redistributing to the lungs. HIV/AIDS naturally served as a T-cell exhaustion disease model for recognizing how the immune system works in the course of COVID-19. In this study, we collected the clinical charts, T-lymphocyte analysis, and chest CT of HIV patients with laboratory-confirmed COVID-19 infection who were admitted to Jin Yin-tan Hospital (Wuhan, China). The median age of the 21 patients was 47 years [interquartile range (IQR) = 40-50 years] and the median CD4 T-cell count was 183 cells/μl (IQR = 96-289 cells/μl). Eleven HIV patients were in the non-AIDS stage and 10 were in the AIDS stage. Nine patients received antiretroviral treatment (ART) and 12 patients did not receive any treatment. Compared to the reported mortality rate (nearly 4%-10%) and severity rate (up to 20%-40%) among COVID-19 patients in hospital, a benign duration with 0% severity and mortality rates was shown by 21 HIV/AIDS patients. The severity rates of COVID-19 were comparable between non-AIDS (median CD4 = 287 cells/μl) and AIDS (median CD4 = 97 cells/μl) patients, despite some of the AIDS patients having baseline lung injury stimulated by HIV: 7 patients (33%) were mild (five in the non-AIDS group and two in the AIDS group) and 14 patients (67%) were moderate (six in the non-AIDS group and eight in the AIDS group). More importantly, we found that a reduction in T-cell number positively correlates with the serum levels of interleukin 6 (IL-6) and C-reactive protein (CRP), which is contrary to the reported findings on the immune response of COVID-19 patients (lower CD4 T-cell counts with higher levels of IL-6 and CRP). In HIV/AIDS, a compromised immune system with lower CD4 T-cell counts might waive the clinical symptoms and inflammatory responses, which suggests lymphocyte redistribution as an immunopathology leading to lymphopenia in COVID-19.

Predictors of Current CD4+ T-Cell Count Among Women of Reproductive Age on Antiretroviral Therapy in Public Hospitals, Southwest Ethiopia

Background

HIV/AIDS is one of the major global public health problems. CD4 is a glycoprotein found on the surface of different immune cells. CD4 cell counts determine the need for screening and prophylactic interventions against common opportunistic infections in those with advanced HIV disease. Thus, this study aimed to assess the predictors of current CD4⁺ T-cell count among women of reproductive age on antiretroviral therapy in public hospitals, southwest Ethiopia.

Methods

A cross-sectional study was conducted from February to April 2018. A total of 422 participants in the three public hospitals were selected using a systematic random sampling method. Linear regression analyses were used to determine the important predictors of current CD4⁺ T-cell count at p-values of <0.05.

Results

A total of 422 women with a median age of 37.00 years participated in this study. More than one in ten (12.8%) respondents experienced immunological failure. An increased current CD4⁺ T-cell count was observed among patients with a tertiary level of education [β = 56.45, 95% CI (3.5, 109.4)], baseline WHO clinical stage II [β = 44.06, 95% CI (5.3, 82.9)], initial regimen of AZT+3TC+EFV [β = 167.23, 95% CI (100.4, 234.1)], with increased baseline CD4⁺ T-cell count [β = 0.35, 95% CI (0.2, 0.5)], and with increased time duration on ART [β = 14.36, 95% CI (6.304, 22.4)]. On the other hand, the current CD4⁺ T-cell count was lowered among patients with poor baseline adherence, opportunistic infection, and viral load of ≥1000 by 181.06 cells/mm³, 101.62 cells/mm³, and 137.53 cells/mm³ compared to good baseline adherence, no opportunistic infection and undetectable viral load, respectively.

Conclusion

The immunological failure was relatively low. Maintaining adherence, early identification and treatment of opportunistic infections, and minimizing viral load to undetectable levels may further decrease immunological failure.

Joint Modeling in Detecting Predictors of CD4 Cell Count and Status of Tuberculosis Among People Living with HIV/AIDS Under HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia

Background

Globally, for individuals infected with HIV, the presence of other infections including TB tends to increase the rate of HIV replication. Of the 8.8 million TB cases worldwide, an estimated 1.1 million (13%) were found to be co-infected with HIV. This research was conducted with the objective to identify potential predictors for the status of TB and CD4 cell count under PLWHIV at Felege Hiwot Specialized Hospital, North-west Ethiopia.

Methods

A retrospective repeated measurement was taken from a sample of 226 HIV patients. Separate and joint models were conducted for data analysis of CD4 cell count and TB status of people living with HIV.

Results

The descriptive statistics indicated that among the HIV patients receiving HAART, 26.6% had additional TB. AIDS clinical stage, weight, and hemoglobin level had a significant positive association with CD4 cell count, but a negative association with TB status. Weight and CD4 cell count have a negative relationship with the event of HIV/TB co-infection. Hence, the expected number of CD4 cell count of HIV patients who were co-infected with TB was decreased by 2.34 as compared to people living with HIV without TB. As visiting times of patients to hospitals for treatment increased by one unit, the odds of being co-infected with TB was decreased by 0.05, and the expected number of CD4 cell count was increased by 0.2. As patients’ age increased by one year, the expected number of CD4 cell count was decreased by 0.025 cells per/mm³.

Conclusion

Having lower CD4 cell count, lower weight, late WHO clinical stage, being non-adherent, having opportunistic infection, having lower hemoglobin, being ambulatory and bedridden were associated with a higher risk of co-infection of HIV/TB and were indicators of progression of the disease.

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

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 HIV-infected 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/mm3 (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/mm3 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.

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

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 HIV-infected 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.

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

INTRODUCTION

Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and state-specific duration of HIV-infected patients.

METHODS

Full-parametric and semi-parametric Markov multistate models were applied to assess the effects of covariates namely TB co-infection, educational status, marital status, age, quality of life (QoL) scores, white and red blood cell parameters, and liver enzyme abnormality on long-term viral suppression, viral rebound and state-specific duration for HIV-infected individuals before and after treatment. Furthermore, two models, one including and another excluding the effect of the frailty, were presented and compared in this study.

RESULTS

Results from the diagnostic plots, Akaike information criterion (AIC) and likelihood ratio test showed that the Weibull multistate frailty model fitted significantly better than the exponential and semi-parametric multistate models. Viral rebound was found to be significantly associated with many sex partners, higher eosinophils count, younger age, lower educational level, higher monocyte counts, having abnormal neutrophils count, and higher liver enzyme abnormality. Furthermore, viral suppression was also found to be significantly associated with higher QoL scores, and having a stable sex partner. The analysis result also showed that patients with a stable sex partner, higher educational levels, higher QoL scores, lower eosinophils count, lower monocyte counts, and higher RBC indices were more likely to spend more time in undetectable viral load state.

CONCLUSIONS

To achieve and maintain the UNAIDS 90% suppression targets, additional interventions are required to optimize antiretroviral therapy outcomes, specifically targeting those with poor clinical characteristics, lower education, younger age, and those with many sex partners. From a methodological perspective, the parametric multistate approach with frailty is a flexible approach for modeling time-varying variables, allowing for dealing with heterogeneity between the sequence of transitions, as well as allowing for a reasonable degree of flexibility with a few additional parameters, which then aids in gaining a better insight into how factors change over time.

Development of algorithms to estimate EQ-5D and derive health utilities from WHOQOL-HIV Bref: a mapping study

PURPOSE

This study aimed to develop and evaluate different families of applicable models available for utility mapping between World Health Organization Quality of Life for HIV-abbreviated version (WHOQOL-HIV Bref) and EQ-5D-3L and to propose an optimised algorithm to estimate health utilities of people living with HIV.

METHODS

Estimation dataset was collected between July 2014 and September 2016 in a cross-sectional study including 1526 people living with HIV/Aids (PLWH) under care at the Instituto Nacional de Infectologia Evandro Chagas-FIOCRUZ, in Brazil. Data of WHOQOL-HIV Bref and EQ-5D-3L questionnaires were collected. Fisher's exact tests were used for testing WHOQOL-HIV Bref response frequencies among groups of responses to each of the five EQ-5D-3L domains. Multiple correspondence analyses (MCA) were used to inspect the relationships between both instrument responses. Different families of applicable models available for utility mapping between WHOQOL-HIV Bref and EQ-5D-3L were adjusted for the prediction of disutility.

RESULTS

Candidate models' performances using mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were similarly good, which was evidenced by the overlapping of its 95% confidence intervals of the mean tenfold cross-validation or estimated generalisation errors. However, the Hurdle Logistic-Log-Normal model was better on average according to generalisation errors both in the prediction of Brazilian utility values (MAE = 0.1037, MSE = 0.0178, and RMSE = 0.1332) and for those of the UK (MAE = 0.1476, MSE = 0.0443, and RMSE = 0.2099).

CONCLUSIONS

Mapping EQ-5D-3L responses or deriving health utilities directly from WHOQOL-HIV Bref responses can be a valid alternative for settings with no preference-based health utility data.