Abstract
BackgroundPrediction 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.MethodsPrediction 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.ResultsWe 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.ConclusionsThe 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.
Highlights
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in Wuhan, China in December 2019
The outcome was either mortality or a composite outcome of mortality and intensive care unit (ICU) admission. 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
Since one of the models was developed at KCH and validated at OUH in a previous publication [14], only four models are presented here
Summary
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in Wuhan, China in December 2019. Practical tools for prediction of prognosis in COVID-19 patients are still lacking in clinical practice [5, 6]. We observed that many laboratory tests are ordered for patients with COVID-19 due to their predictive value. There is redundancy in the information from the different tests and it could be possible to improve the prediction by using a multivariable model and reducing the number of redundantly ordered tests. Prediction models can be crucial to prioritize patients needing hospitalization, intensive care treatment, or future individualized therapy. 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
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