Abstract
The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.
We independently externally validated the performance of candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at the time of admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictors in univariable analyses.
We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78, 95% CI 0.73–0.83), and a novel model for prediction of deterioration <14 days from admission (0.78, 95% CI 0.74–0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76, 95% CI 0.71–0.81), and age for in-hospital mortality (AUROC 0.76, 95% CI 0.71–0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.
Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.
Abstract
Oxygen saturation on room air and patient age are strong predictors of deterioration and mortality, respectively, among hospitalised adults with COVID-19. None of the 22 prognostic models evaluated in this study adds incremental value to these univariable predictors. https://bit.ly/2Hg24TO
Footnotes
The UCLH COVID-19 Reporting Group comprised the following individuals, who were involved in data curation as non-author contributors: Asia Ahmed, Ronan Astin, Malcolm Avari, Elkie Benhur, Anisha Bhagwanani, Timothy Bonnici, Sean Carlson, Jessica Carter, Sonya Crowe, Mark Duncan, Ferran Espuny-Pujol, James Fullerton, Marc George, Georgina Harridge, Ali Hosin, Rachel Hubbard, Adnan Hubraq, Prem Jareonsettasin, Zella King, Avi Korman, Sophie Kristina, Lawrence Langley, Jacques-Henri Meurgey, Henrietta Mills, Alfio Missaglia, Ankita Mondal, Samuel Moulding, Christina Pagel, Liyang Pan, Shivani Patel, Valeria Pintar, Jordan Poulos, Ruth Prendecki, Alexander Procter, Magali Taylor, David Thompson, Lucy Tiffen, Hannah Wright, Luke Wynne, Jason Yeung, Claudia Zeicu, Leilei Zhu.
Author contributions: R.K. Gupta and M. Noursadeghi conceived the study. R.K. Gupta conducted the analysis and wrote the first draft of the manuscript. All other authors contributed towards data collection, study design and/or interpretation. All authors have critically appraised and approved the final manuscript prior to submission. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Members of The UCLH COVID-19 Reporting Group contributed towards data curation and are non-author contributors/collaborators for this study.
This article has an editorial commentary: https://doi.org/10.1183/13993003.03728-2020
This article has supplementary material available from erj.ersjournals.com
The conditions of regulatory approvals for the present study preclude open access data sharing to minimise risk of patient identification through granular individual health record data. The authors will consider specific requests for data sharing as part of academic collaborations subject to ethical approval and data transfer agreements in accordance with GDPR regulations.
Support statement: The study was funded by National Institute for Health Research (DRF-2018-11-ST2-004 to R.K. Gupta; NF-SI-0616-10037 to I. Abubakar), the Wellcome Trust (207511/Z/17/Z to M. Noursadeghi) and has been supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), in particular by the NIHR UCLH/University College London (UCL) BRC Clinical and Research Informatics Unit. This paper presents independent research supported by the NIHR. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funder had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. Funding information for this article has been deposited with the Crossref Funder Registry.
Conflict of interest: M. Marks has nothing to disclose.
Conflict of interest: T.H.A. Samuels has nothing to disclose.
Conflict of interest: A. Luintel has nothing to disclose.
Conflict of interest: T. Rampling has nothing to disclose.
Conflict of interest: H. Chowdhury has nothing to disclose.
Conflict of interest: M. Quartagno has nothing to disclose.
Conflict of interest: A. Nair reports non-financial support from AIDENCE BV and grants from NIHR UCL Biomedical Research Centre, outside the submitted work.
Conflict of interest: M. Lipman has nothing to disclose.
Conflict of interest: I. Abubakar has nothing to disclose.
Conflict of interest: M. van Smeden has nothing to disclose.
Conflict of interest: W.K. Wong has nothing to disclose.
Conflict of interest: B. Williams has nothing to disclose.
Conflict of interest: M. Noursadeghi reports grants from Wellcome Trust and National Institute for Health Research Biomedical Research Centre at University College London NHS Foundation Trust, during the conduct of the study.
Conflict of interest: R.K. Gupta has nothing to disclose.
- Received September 14, 2020.
- Accepted September 17, 2020.
- Copyright ©ERS 2020
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