Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening

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Abstract

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  1. SciScore for 10.1101/2021.08.24.21262376: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsField Sample Permit: The study protocol for model development, external and prospective validation was approved by the National Health Service (NHS) Health Research Authority (IRAS ID 281832) and sponsored by the University of Oxford.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Routine COVID-19 testing was performed in line with trust policies, with LFDs (Innova SARS-CoV-2 Antigen Rapid Qualitative Test) performed in the department and paired multiplex PCR on-premises in a dedicated laboratory (ThermoFisher TaqPath).
    ThermoFisher TaqPath
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, despite significant innovation leading to new near-patient testing options, alongside reduced PCR result-times to typically within 12-24 h, there remain significant performance and logistical limitations that contribute to nosocomial transmission and operational strain. While many hospitals have adopted LFDs within acute admissions pathways10, our study confirms a limited sensitivity (56.9%) indicating a clinically-meaningful false negative rate (Figure 3, Supplementary Table S6)14–16,41. In this study we demonstrate generalisability, efficacy, and real-world operational benefits of AI-driven COVID-19 screening in the acute care setting. Whereas rapid molecular testing options are frequently rationed11,12, we show that a high-throughput AI-solution, CURIAL-Lab, rapidly excludes COVID-19 using routine data and generalises across three independent hospital groups (Figure 2). Moreover, we improve upon the speed of existing rapid testing solutions, demonstrating a median result-time of 45 minutes (32-64 min; CURIAL-Rapide) from patients’ first arrival in an emergency department using near-patient haematology analysis. This decentralised approach may support time-critical decision making and assist triage in remote and primary care settings where laboratory facilities are less readily available. In our external and prospective validation of CURIAL-Lab & CURIAL-Rapide, model performance was consistently high across four UK hospital groups (CURIAL-Lab: AUROC range 0.858-0.8...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.