Identifying COVID-19 cases in outpatient settings

  1. Yinan Mao
  2. Yi-Roe Tan
  3. Tun Linn Thein
  4. Yi Ann Louis Chai
  5. Alex R. Cook
  6. Borame L. Dickens
  7. Yii Jen Lew
  8. Fong Seng Lim
  9. Jue Tao Lim
  10. Yinxiaohe Sun
  11. Meena Sundaram
  12. Alexius Soh
  13. Glorijoy Shi En Tan
  14. Franco Pey Gein Wong
  15. Barnaby Young
  16. Kangwei Zeng
  17. Mark Chen
  18. Desmond Luan Seng Ong

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Abstract

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.26.20182204: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Demographic data and detailed information on symptoms, signs and laboratory investigations were collected using structured questionnaires with waiver of consent granted by the Ministry of Health, Singapore under the Infectious Diseases Act.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    This study has several limitations. Firstly, the distinction between dry and productive cough, and anosmia as a symptom were not captured in this study, particularly because the latter was reported only after our study was started. These have been reported as clinically relevant characteristics for SARS-CoV-2 positive individuals (2,32,33), and their inclusion may have improved the performance of the algorithm further. Secondly, our controls were not tested for COVID-19. However, there was no widespread transmission of COVID-19 at the time of data collection for our controls. For instance, testing of 774 residual sera samples collected in early April 2020, around the time we ceased collecting data controls, identified no seropositive individuals (unpublished data). Thirdly, we recognize that the “controls” against which our COVID-19 patients must be distinguished may differ due to variations in the epidemiology of background illnesses by place and time. This limitation can potentially be overcome by collecting, then repeating the analyses using, updated data from locally relevant “control patients”, collected through the simple data collection format we used. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

    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.

    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.

  2. Version published to 10.1017/s0950268821000704
  3. Version published to 10.1101/2020.08.26.20182204v1 on medRxiv