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Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study

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Kristin E Wickstrøm, Valeria Vitelli, Ewan Carr, Aleksander R Holten, Rebecca Bendayan, Andrew H Reiner, Daniel Bean, Tom Searle, Anthony Shek, Zeljko Kraljevic, James T. Teo, Richard Dobson, Kristian Tonby, Alvaro Köhn-Luque, Erik K Amundsen

Original languageEnglish
Article numbere0255748
Pages (from-to)e0255748
JournalPloS one
Issue number8 August
Accepted/In press16 Aug 2021
Published25 Aug 2021

Bibliographical note

Funding Information: One of the authors (JTHT) have previously received research support and funding from InnovateUL, Bristol-Myers-Squibb, iRhytm Technologies, and hold shares under ?5000 in Glaxo SmithKline and Biogen. No external commercial financial support was received for this work. Institutional support and grants are stated under funding information. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 Wickstrøm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


BACKGROUND: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors.

METHODS: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration.

RESULTS: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79-0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76-0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74-0.88] and KCH AUROC 0.72 [0.68-0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration.

CONCLUSIONS: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

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