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

Abstract

BackgroundIt 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.MethodsThe 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).ResultsIn 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.ConclusionsThis 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.

Single-cell immune profiling reveals the impact of antiretroviral therapy on HIV-1-induced immune dysfunction, T cell clonal expansion, and HIV-1 persistencein vivo

“…We used longitudinally archived blood samples from the Sabes Study in which HIV-1 infection was prospectively tested monthly for early detection and treatment stratification 44,45 . Using detailed histories of HIV-1 testing, including date, type and result of test, we calculated the estimated date of detectable infection (EDDI) using an established algorithm 46 . We examined the single-cell transcriptional landscape of six people with acute HIV-1 infection treated either with immediate ART (three individuals receiving ART 22 – 47 days after EDDI) versus delayed ART (three individuals receiving ART 187 – 207 days after EDDI) (Fig.…”

Seminal Plasma-Derived Extracellular-Vesicle Fractions from HIV-Infected Men Exhibit Unique MicroRNA Signatures and Induce a Proinflammatory Response in Cells Isolated from the Female Reproductive Tract

“…The seminal plasma samples were selected based on the highest available volume in the semen bank. A total of 40 SP samples were collected from 20 antiretroviral therapy-naive men acutely infected with HIV (as defined by sampling Ͻ3 months from the estimated date of infection, based on previously described criteria [53]) and 20 clinically matched uninfected individuals. Additionally, for FACS characterization, we studied 10 chronically infected individuals with paired samples available from before and after the initiation of ART.…”

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“…This generalisation provides for a flexible family of test-discordant states whose temporal meaning can be made precise with widely accessible data, which has also been curated for the benefit of potential users [7]. The notion of categorical infection 'stages' implies estimates of infection dates which are almost uniform probability/confidence distributions within boundaries set by test dates, with offsets implied by typical 'diagnostic delays' of the relevant tests [8]. In other words, while the 'edges' are not sharp, complete knowledge of last negative and first positive tests (ideally, but not necessarily, from different tests performed on the same date) is equivalent to the estimation of an interval in which infection is almost certainly located.…”