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Estimated dates of detectable infection (EDDIs) as an improvement upon Fiebig staging for HIV infection dating.

ABSTRACT: Accurate methods for determining the duration of HIV infection at the individual level are valuable in many settings, including many critical research studies and in clinical practice (especially for acute infection). Since first published in 2003, the 'Fiebig staging system' has been used as the primary way of classifying early HIV infection into five sequential stages based on HIV test result patterns in newly diagnosed individuals. However, Fiebig stages can only be assigned to individuals who produce both a negative and a positive test result on the same day, on specific pairs of tests of varying 'sensitivity'. Further, in the past 16 years HIV-testing technology has evolved substantially, and three of the five key assays used to define Fiebig stages are no longer widely used. To address these limitations, we developed an improved and more general framework for estimating the duration of HIV infection by interpreting any combination of diagnostic test results, whether obtained on single or multiple days, into an estimated date of detectable infection, or EDDI. A key advantage of the EDDI method over Fiebig staging is that it allows for the generation of a point estimate, as well as an associated credibility interval for the date of first detectable infection, for any person who has at least one positive and one negative HIV test of any kind. The tests do not have to be run on the same day; they do not have to be run during the acute phase of infection and the method does not rely on any special pairing of tests to define 'stages' of infection. The size of the interval surrounding the EDDI (and therefore the precision of the estimate itself) depends largely on the length of time between negative and positive tests. The EDDI approach is also flexible, seamlessly incorporating any assay for which there is a reasonable diagnostic delay estimate. An open-source, free online tool includes a user-updatable curated database of published diagnostic delays. HIV diagnostics have evolved tremendously since that original publication more than 15 years ago, and it is time to similarly evolve the methods used to estimate timing of infection. The EDDI method is a flexible and rigorous way to estimate the timing of HIV infection in a continuously evolving diagnostic landscape.

SUBMITTER: Facente SN

PROVIDER: S-EPMC7078584 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Estimated dates of detectable infection (EDDIs) as an improvement upon Fiebig staging for HIV infection dating.

Facente S N SN Grebe E E Pilcher C D CD Busch M P MP Murphy G G Welte A A

Epidemiology and infection 20200219

Accurate methods for determining the duration of HIV infection at the individual level are valuable in many settings, including many critical research studies and in clinical practice (especially for acute infection). Since first published in 2003, the 'Fiebig staging system' has been used as the primary way of classifying early HIV infection into five sequential stages based on HIV test result patterns in newly diagnosed individuals. However, Fiebig stages can only be assigned to individuals wh ...[more]

PMID: 32070438

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Project description:BackgroundMinimisation of the delay to diagnosis is critical to achieving optimal outcomes for HIV patients and to limiting the potential for further onward infections. However, investigation of diagnosis delay is hampered by the fact that in most newly diagnosed patients the exact timing of infection cannot be determined and so inferences must be drawn from biomarker data.MethodsWe develop a Bayesian statistical model to evaluate delay-to-diagnosis distributions in HIV patients without known infection date, based on viral sequence genetic diversity and longitudinal viral load and CD4 count data. The delay to diagnosis is treated as a random variable for each patient and their biomarker data are modelled relative to the true time elapsed since infection, with this dependence used to obtain a posterior distribution for the delay to diagnosis. Data from a national seroconverter cohort with infection date known to within ± 6 months, linked to a database of viral sequences, are used to calibrate the model parameters. An exponential survival model is implemented that allows general inferences regarding diagnosis delay and pooling of information across groups of patients. If diagnoses are only observed within a given window period, then it is necessary to also model incidence as a function of time; we suggest a pragmatic approach to this problem when dealing with data from an established epidemic. The model developed is used to investigate delay-to-diagnosis distributions in men who have sex with men diagnosed with HIV in London in the period 2009-2013 with unknown date of infection.ResultsCross-validation and simulation analyses indicate that the models developed provide more accurate information regarding the timing of infection than does CD4 count-based estimation. Delay-to-diagnosis distributions were estimated in the London cohort, and substantial differences were observed according to ethnicity.ConclusionThe combination of all available biomarker data with pooled estimation of the distribution of diagnosis-delays allows for more precise prediction of the true timing of infection in individual patients, and the models developed also provide useful population-level information.

Project description:BackgroundThe suppression of viremia among persons with HIV (PWH) using antiretroviral therapy has been hypothesized to reduce HIV incidence at the population level. We investigated the impact of state level viral suppression among PWH in the United States on estimated HIV incidence between 2010 and 2015.MethodsViral suppression data and HIV incidence estimates from the National HIV Surveillance System were available from 29 states and the District of Columbia. We assumed a one year delay for viral suppression to impact incidence. Poisson regression models were used to calculate the estimated annual percent change (EAPC) in incidence rate. We employed a multivariable mixed-effects Poisson regression model to assess the effects of state level race/ethnicity, socioeconomic status, percent men who have sex with men (MSM) and hepatitis C virus prevalence as a proxy for injection drug use on HIV incidence.FindingsFitted HIV incidence for 30 jurisdictions declined from 11.5 in 2010 to 10.0 per 100,000 population by 2015 corresponding with an EAPC of -2.67 (95% confidence interval [95%CI] -2.95, -2.38). Southern states experienced the highest estimated incidence by far throughout this period but upon adjustment for viral suppression and demographics there was a 36% lower incidence rate than Northeast states (adjusted rate ratio [aRR] 0.64; 95%CI 0.42, 0.99). For every 10 percentage point (pp) increase in viral suppression there was an adjusted 4% decline in HIV incidence rate in the subsequent year (aRR 0.96; 95%CI 0.93, 0.99). While controlling for viral suppression, HIV incidence rate increased by 42% (aRR 1.42 95%CI 1.31, 1.54) for every 5 pp increase in percent Black race and by 27% (aRR 1.27 95%CI 1.10, 1.48) for every 1 pp increase in percent MSM in states.InterpretationA decline in estimated HIV incidence from 2010 to 2015 was associated with increasing viral suppression in the United States. Race and sexual orientation were important HIV acquisition risk factors.

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Estimated dates of detectable infection (EDDIs) as an improvement upon Fiebig staging for HIV infection dating.

Publications

Estimated dates of detectable infection (EDDIs) as an improvement upon Fiebig staging for HIV infection dating.

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