Browsing by Subject "Diagnostics"

Now showing 1 - 3 of 3
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    Open Access
    Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs
    (2019-06-24) Phelan, Jody E.; O’Sullivan, Denise M; Machado, Diana; Ramos, Jorge; Oppong, Yaa E A; Campino, Susana; O’Grady, Justin; McNerney, Ruth; Hibberd, Martin L.; Viveiros, Miguel; Huggett, Jim F; Clark, Taane G
    Abstract Background Mycobacterium tuberculosis resistance to anti-tuberculosis drugs is a major threat to global public health. Whole genome sequencing (WGS) is rapidly gaining traction as a diagnostic tool for clinical tuberculosis settings. To support this informatically, previous work led to the development of the widely used TBProfiler webtool, which predicts resistance to 14 drugs from WGS data. However, for accurate and rapid high throughput of samples in clinical or epidemiological settings, there is a need for a stand-alone tool and the ability to analyse data across multiple WGS platforms, including Oxford Nanopore MinION. Results We present a new command line version of the TBProfiler webserver, which includes hetero-resistance calling and will facilitate the batch processing of samples. The TBProfiler database has been expanded to incorporate 178 new markers across 16 anti-tuberculosis drugs. The predictive performance of the mutation library has been assessed using > 17,000 clinical isolates with WGS and laboratory-based drug susceptibility testing (DST) data. An integrated MinION analysis pipeline was assessed by performing WGS on 34 replicates across 3 multi-drug resistant isolates with known resistance mutations. TBProfiler accuracy varied by individual drug. Assuming DST as the gold standard, sensitivities for detecting multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) were 94% (95%CI 93–95%) and 83% (95%CI 79–87%) with specificities of 98% (95%CI 98–99%) and 96% (95%CI 95–97%) respectively. Using MinION data, only one resistance mutation was missed by TBProfiler, involving an insertion in the tlyA gene coding for capreomycin resistance. When compared to alternative platforms (e.g. Mykrobe predictor TB, the CRyPTIC library), TBProfiler demonstrated superior predictive performance across first- and second-line drugs. Conclusions The new version of TBProfiler can rapidly and accurately predict anti-TB drug resistance profiles across large numbers of samples with WGS data. The computing architecture allows for the ability to modify the core bioinformatic pipelines and outputs, including the analysis of WGS data sourced from portable technologies. TBProfiler has the potential to be integrated into the point of care and WGS diagnostic environments, including in resource-poor settings.
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    Open Access
    Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool
    (2019-10-26) Grebe, Eduard; Facente, Shelley N; Bingham, Jeremy; Pilcher, Christopher D; Powrie, Andrew; Gerber, Jarryd; Priede, Gareth; Chibawara, Trust; Busch, Michael P; Murphy, Gary; Kassanjee, Reshma; Welte, Alex
    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.
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    Open Access
    QuantiFERON conversion following tuberculin administration is common in HIV infection and relates to baseline response
    (BioMed Central, 2016-10-07) Esmail, Hanif; Thienemann, Friedrich; Oni, Tolu; Goliath, Rene; Wilkinson, Katalin A; Wilkinson, Robert J
    Background: HIV-1 infection impairs tuberculosis (TB) specific immune responses affecting the diagnosis of latent TB. We aimed to (1) determine the proportion of HIV-1-infected adults with a negative QuantiFERON®-TB Gold in-tube (QFT-GIT) and Tuberculin skin testing (TST) that convert to QFT-GIT positive following TST, and (2) evaluate the relationship between conversion and baseline QFT-GIT results. Methods: HIV-1 infected adults being screened for a TB vaccine study in South Africa underwent QFT-GIT followed by TST. As per protocol, QFT-GIT was repeated if randomization was delayed allowing for evaluation of TST boosting in a proportion of participants. Results: Of the 22 HIV-1 infected, TST and QFT-GIT negative adults (median CD4 477/mm3 IQR 439–621) who had QFT-GIT repeated after median 62 days (IQR 49–70), 40.9 % (95 % CI 18.6–63.2 %) converted. Converters had a significantly greater increase in the background subtracted TB antigen response (TBAg-Nil – all units IU/mL) following TST, 0.82 (IQR 0.39–1.28) vs 0.03 (IQR −0.05–0.06), p = 0.0001. Those who converted also had a significantly higher baseline TBAg-Nil 0.21(IQR 0.17–0.26) vs 0.02(IQR 0.01–0.07), p = 0.002. Converters did not differ with regard to CD4 count or ART status. ROC analysis showed a baseline cut off of 0.15 correctly classified 86.4 % of converters with 88.9 % sensitivity. Conclusions: Our findings support the possibility that there are 2 distinct groups in an HIV-1 infected population with negative QFT-GIT and TST; a true negative group and a group showing evidence of a weak Mtb specific immune response that boosts significantly following TST resulting in conversion of the test result that may represent false negatives. Further evaluation of whether a lower cut off may improve sensitivity of QFT-GIT in this population is warranted.