Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study

The UCLH COVID-19 Reporting Group

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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.

Original languageEnglish
Article number2003498
JournalEuropean Respiratory Journal
Volume56
Issue number6
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

Bibliographical note

Funding Information:
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.

Funding Information:
Conflict of interest: M. Marks has nothing to disclose. T.H.A. Samuels has nothing to disclose. A. Luintel has nothing to disclose. T. Rampling has nothing to disclose. H. Chowdhury has nothing to disclose. M. Quartagno has nothing to disclose. A. Nair reports non-financial support from AIDENCE BV and grants from NIHR UCL Biomedical Research Centre, outside the submitted work. M. Lipman has nothing to disclose. I. Abubakar has nothing to disclose. M. van Smeden has nothing to disclose. W.K. Wong has nothing to disclose. B. Williams has nothing to disclose. 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. R.K. Gupta has nothing to disclose.

Funding Information:
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.

Publisher Copyright:
Copyright © ERS 2020. This version is distributed under the terms of the Creative Commons Attribution Licence 4.0.

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