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. 2019 Oct 26;19(1):894.
doi: 10.1186/s12879-019-4543-9.

Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool

Eduard Grebe  1   2   3 , Shelley N Facente  4   5   6 , Jeremy Bingham  7 , Christopher D Pilcher  5 , Andrew Powrie  8 , Jarryd Gerber  8 , Gareth Priede  8 , Trust Chibawara  7 , Michael P Busch  4   5 , Gary Murphy  9 , Reshma Kassanjee  7   10 , Alex Welte  7 ; Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA)
Collaborators, Affiliations

Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool

Eduard Grebe et al. BMC Infect Dis. .

Abstract

Background: It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the 'Fiebig staging' system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests - as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection.

Methods: The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time 'point estimates' and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice).

Results: In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic.

Conclusions: This tool, available at https://tools.incidence-estimation.org/idt/ , is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

Keywords: Diagnostic assays; Diagnostics; HIV; Infection dating; Infection duration; Infection timing.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Diagnostic test sensitivity as a function of time since infection. The green curves show individual subject-level test sensitivities, and the blue curve shows the population-level average
Fig. 2
Fig. 2
Example of infection time point estimate and interval obtained for a hypothetical subject who tested negative on the Aptima qualitative NAT assay and Determine rapid test at t1, and positive on the Geenius supplemental assay and Determine rapid test at t2
Fig. 3
Fig. 3
The joint likelihood of obtaining a negative test result at t1 and a positive test at t2, given a hypothetical time of infection. With a uniform prior on time of infection, this can be interpreted as a Bayesian posterior, with the interval [a,b] representing the 95% credibility interval
See this image and copyright information in PMC

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References

    1. Pilcher CD, Porco TC, Facente SN, Grebe E, Delaney KP, Masciotra S, et al. A generalizable method for estimating duration of HIV infections using clinical testing history and HIV test results. AIDS. 2019;33(7):1231–1240. doi: 10.1097/QAD.0000000000002190. - DOI - PMC - PubMed
    1. Fiebig EW, Wright DJ, Rawal BD, Garrett PE, Schumacher RT, Peddada L, et al. Dynamics of HIV viremia and antibody seroconversion in plasma donors: implications for diagnosis and staging of primary HIV infection. AIDS. 2003;17(13):1871–1879. doi: 10.1097/00002030-200309050-00005. - DOI - PubMed
    1. Lee HY, Giorgi EE, Keele BF, Gaschen B, Athreya GS, Salazar-Gonzalez JF, et al. Modeling sequence evolution in acute HIV-1 infection. J Theor Biol. 2009;261(2):341–360. doi: 10.1016/j.jtbi.2009.07.038. - DOI - PMC - PubMed
    1. Ananworanich J, Fletcher JL, Pinyakorn S, van Griensven F, Vandergeeten C, Schuetz A, et al. A novel acute HIV infection staging system based on 4th generation immunoassay. Retrovirology. 2013;10:56. doi: 10.1186/1742-4690-10-56. - DOI - PMC - PubMed
    1. Kassanjee R. Characterisation and application of tests for recent infection for HIV incidence surveillance. Johannesburg: University of the Witwatersrand; 2014.