Dataset Information

Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort.

ABSTRACT: Recent studies have evaluated cumulative human immunodeficiency virus type 1 (HIV-1) viral load (cVL) for predicting disease outcomes, with discrepant results. We reviewed the disparate methodological approaches taken and evaluated the prognostic utility of cVL in a resource-limited setting. Using data on the Infectious Diseases Institute (Makerere University, Kampala, Uganda) cohort, who initiated antiretroviral therapy in 2004-2005 and were followed up for 9 years, we calculated patients' time-updated cVL by summing the area under their viral load curves on either a linear scale (cVL1) or a logarithmic scale (cVL2). Using Cox proportional hazards models, we evaluated both metrics as predictors of incident opportunistic infections and mortality. Among 489 patients analyzed, neither cVL measure was a statistically significant predictor of opportunistic infection risk. In contrast, cVL2 (but not cVL1) was a statistically significant predictor of mortality, with each log10 increase corresponding to a 1.63-fold (95% confidence interval: 1.02, 2.60) elevation in mortality risk when cVL2 was accumulated from baseline. However, whether cVL is predictive or not hinges on difficult choices surrounding the cVL metric and statistical model employed. Previous studies may have suffered from confounding bias due to their focus on cVL1, which strongly correlates with other variables. Further methodological development is needed to illuminate whether the inconsistent predictive utility of cVL arises from causal relationships or from statistical artifacts.

SUBMITTER: Sempa JB

PROVIDER: S-EPMC4929240 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort.

Sempa Joseph B JB Dushoff Jonathan J Daniels Michael J MJ Castelnuovo Barbara B Kiragga Agnes N AN Nieuwoudt Martin M Bellan Steven E SE

American journal of epidemiology 20160504 1

Recent studies have evaluated cumulative human immunodeficiency virus type 1 (HIV-1) viral load (cVL) for predicting disease outcomes, with discrepant results. We reviewed the disparate methodological approaches taken and evaluated the prognostic utility of cVL in a resource-limited setting. Using data on the Infectious Diseases Institute (Makerere University, Kampala, Uganda) cohort, who initiated antiretroviral therapy in 2004-2005 and were followed up for 9 years, we calculated patients' time ...[more]

PMID: 27188943

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Project description:INTRODUCTION:There are Challenges in statistically modelling immune responses to longitudinal HIV viral load exposure as a function of covariates. We define Bayesian Markov Chain Monte Carlo mixed effects models to incorporate priors and examine the effect of different distributional assumptions. We prospectively fit these models to an as-yet-unpublished data from the Tshwane District Hospital HIV treatment clinic in South Africa, to determine if cumulative log viral load, an indicator of long-term viral exposure, is a valid predictor of immune response. METHODS:Models are defined, to express 'slope', i.e. mean annual increase in CD4 counts, and 'asymptote', i.e. the odds of having a CD4 count ?500 cells/?L during antiretroviral treatment, as a function of covariates and random-effects. We compare the effect of using informative versus non-informative prior distributions on model parameters. Models with cubic splines or Skew-normal distributions are also compared using the conditional Deviance Information Criterion. RESULTS:The data of 750 patients are analyzed. Overall, models adjusting for cumulative log viral load provide a significantly better fit than those that do not. An increase in cumulative log viral load is associated with a decrease in CD4 count slope (19.6 cells/?L (95% credible interval: 28.26, 10.93)) and a reduction in the odds of achieving a CD4 counts ?500 cells/?L (0.42 (95% CI: 0.236, 0.730)) during 5 years of therapy. Using informative priors improves the cumulative log viral load estimate, and a skew-normal distribution for the random-intercept and measurement error results is a better fit compared to using classical Gaussian distributions. DISCUSSION:We demonstrate in an unpublished South African cohort that cumulative log viral load is a strong and significant predictor of both CD4 count slope and asymptote. We argue that Bayesian methods should be used more frequently for such data, given their flexibility to incorporate prior information and non-Gaussian distributions.

Project description:The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.

Project description:BackgroundMany HIV-infected African children gained access to antiretroviral treatment (ART) through expansion of PEPFAR programs since 2004 and introduction of "Test and Treat" WHO guidelines in 2015. As ART access increases and children transition from adolescence to adulthood, treatment failure is inevitable. Viral load (VL) monitoring in Uganda was introduced in 2016 replacing clinical monitoring. However, there's limited data on the comparative effectiveness of these two strategies among HIV-infected children in resource-limited settings (RLS).MethodsHIV-infected Ugandan children aged 1-12 years from HIV-care programs with > 1 year of first-line ART using only immunologic and clinical criteria to monitor response to treatment were screened in 2010. Eligible children were stratified by VL ≤ 400 and > 400 copies/ml randomized to clinical and immunological (control) versus clinical, immunological and VL monitoring to determine treatment failure with follow-up at 12, 24, 36, and 48 weeks. Plasma VL was analyzed retrospectively for controls. Mixed-effects logistic regression models were used to compare the prevalence of viral suppression between study arms and identify factors associated with viral suppression.ResultsAt baseline all children (n = 142) were on NNRTI based ART (75% Nevirapine, 25% efavirenz). One third of ART-experienced children had detectable VL at baseline despite high CD4%. Median age was 6 years (interquartile range [IQR]: 5-9) and 43% were female. Overall, the odds of viral suppression were not different between study arms: (arm by week interaction, p = 0.63), adjusted odds ratio [aOR]: 1.07; 95%CI: 0.53, 2.17, p = 0.57) and did not change over time (aOR: 0 vs 24 week: 1.15; 95% CI: 0.91, 1.46, p = 0.24 and 0 vs 48 weeks: 1.26; 95%CI: 0.92, 1.74, p = 0.15). Longer duration of a child's ART exposure was associated with lower odds of viral suppression (aOR: 0.61; 95% CI: 0.42, 0.87, p < .01). Only 13% (9/71) of children with virologic failure were switched to second-line ART, in spite of access to real-time VL.ConclusionWith increasing ART exposure, viral load monitoring is critical for early detection of treatment failure in RLS. Clinicians need to make timely informed decisions to switch failing children to second-line ART.Trial registrationClinicalTrials.gov NCT04489953 , 28 Jul 2020. Retrospectively registered. ( https://register.clinicaltrials.gov ).

Dataset Information

Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort.

Publications

Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort.

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