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Adderley NJ, Taverner T, Price MJ, et al. Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care. BMJ Open. 2022;12(1):e049506doi: 10.1136/bmjopen-2021-049506.
Adderley, N. J., Taverner, T., Price, M. J., Sainsbury, C., Greenwood, D., Chandan, J. S., Takwoingi, Y., Haniffa, R., Hosier, I., Welch, C., Parekh, D., Gallier, S., Gokhale, K., Denniston, A. K., Sapey, E., & Nirantharakumar, K. (2022). Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care. BMJ open, 12(1), e049506. https://doi.org/10.1136/bmjopen-2021-049506
Adderley, Nicola J, et al. "Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care." BMJ open vol. 12,1 (2022): e049506. doi: https://doi.org/10.1136/bmjopen-2021-049506
Adderley NJ, Taverner T, Price MJ, Sainsbury C, Greenwood D, Chandan JS, Takwoingi Y, Haniffa R, Hosier I, Welch C, Parekh D, Gallier S, Gokhale K, Denniston AK, Sapey E, Nirantharakumar K. Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care. BMJ Open. 2022 Jan 17;12(1):e049506. doi: 10.1136/bmjopen-2021-049506. PMID: 35039282.
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BMJ Open. 2022 Jan 17;12(1):e049506. doi: 10.1136/bmjopen-2021-049506.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.

BMJ open

Nicola J Adderley, Thomas Taverner, Malcolm James Price, Christopher Sainsbury, David Greenwood, Joht Singh Chandan, Yemisi Takwoingi, Rashan Haniffa, Isaac Hosier, Carly Welch, Dhruv Parekh, Suzy Gallier, Krishna Gokhale, Alastair K Denniston, Elizabeth Sapey, Krishnarajah Nirantharakumar

Affiliations

  1. Institute of Applied Health Research, University of Birmingham, Birmingham, UK [email protected].
  2. Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
  3. NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  4. Department of Diabetes, Gartnavel General Hospital, Glasgow, UK.
  5. Mahidol Oxford Tropical Medicine Research Unit, University of Oxford, Oxford, UK.
  6. Centre for Anaesthesia Critical Care & Pain Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
  7. Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
  8. University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  9. National Institute for Health Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
  10. Health Data Research UK, London, UK.

PMID: 35039282 DOI: 10.1136/bmjopen-2021-049506

Abstract

OBJECTIVES: Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score.

DESIGN: Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection.

SETTING: Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset.

PARTICIPANTS: Patients with COVID-19 admitted to UHB January-August 2020 were included.

MAIN OUTCOME MEASURES: Death and ITU admission within 28 days of admission.

RESULTS: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785).

CONCLUSIONS: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Keywords: COVID-19; public health

Conflict of interest statement

Competing interests: NA, ES, KN, MJP, AKD, CS, TT and YT report a grant from UKRI MRC during the conduct of the study. ES reports grants from National Institute for Health Research (NIHR), Wellcome Tr

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