Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19

  1. Rishi K Gupta
  2. Ewen M Harrison
  3. Antonia Ho
  4. Annemarie B Docherty
  5. Stephen R Knight
  6. Maarten van Smeden
  7. Ibrahim Abubakar
  8. Marc Lipman
  9. Matteo Quartagno
  10. Riinu Pius
  11. Iain Buchan
  12. Gail Carson
  13. Thomas M Drake
  14. Jake Dunning
  15. Cameron J Fairfield
  16. Carrol Gamble
  17. Christopher A Green
  18. Sophie Halpin
  19. Hayley E Hardwick
  20. Karl A Holden
  21. Peter W Horby
  22. Clare Jackson
  23. Kenneth A Mclean
  24. Laura Merson
  25. Jonathan S Nguyen-Van-Tam
  26. Lisa Norman
  27. Piero L Olliaro
  28. Mark G Pritchard
  29. Clark D Russell
  30. James Scott-Brown
  31. Catherine A Shaw
  32. Aziz Sheikh
  33. Tom Solomon
  34. Cathie Sudlow
  35. Olivia V Swann
  36. Lance Turtle
  37. Peter JM Openshaw
  38. J Kenneth Baillie
  39. Malcolm G Semple
  40. Mahdad Noursadeghi

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Abstract

Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions. Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables. We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions. We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (−0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)). Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making. Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.

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    SciScore for 10.1101/2020.10.09.20209957: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval: Ethical approval was given by the South Central-Oxford C Research Ethics Committee in England (reference 13/SC/0149), and by the Scotland A Research Ethics Committee (reference 20/SS/0028).
    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:
    Another limitation is that we only included predictors that were routinely measured as part of clinical care during the study period, and specified that they had to be available among >60% of the population for inclusion in the analysis. Thus, we were unable to assess candidate models that include predictors such as lactate dehydrogenase or D-dimer, since these variables were only available in a small minority of participants. Future studies could consider standardised capture of laboratory measurements considered to have prognostic value to enable inclusion of these variables in model development and validation at scale. Moreover, we note that novel molecular biomarkers currently under investigation may also offer prognostic value36. Blood transcript, protein and metabolite measurements will be available from a subset of the ISARIC4C participants and could be integrated into risk-stratification tools in future studies. In summary, we present a prognostic model for clinical deterioration among hospitalised adults with community or hospital acquired COVID-19, validated in nine NHS regions in England, Scotland and Wales. The model uses readily available clinical predictors and will be made freely available online alongside our previously reported mortality risk score (https://isaric4c.net/outputs/4c_score/)9 at the point of peer-reviewed publication, to inform clinical decision-making and patient stratification for therapeutic interventions. The underlying model coefficients ar...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN66726260NANA

    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.

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  2. Version published to 10.1101/2020.10.09.20209957 on medRxiv