Development of a mortality prediction model in hospitalised SARS-CoV-2 positive patients based on routine kidney biomarkers

Boss, A. N. et al. (2022) Development of a mortality prediction model in hospitalised SARS-CoV-2 positive patients based on routine kidney biomarkers. International Journal of Molecular Sciences, 23(13), 7260. (doi: 10.3390/ijms23137260) (PMID:35806273) (PMCID:PMC9266863)

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Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.

Item Type:Articles
Additional Information:Funding: The main body of research was supported by the Engineering and Physical Sciences Research Council [grant number EP/R511705/1]. Dr Andrew Swift is funded by a Wellcome Trust fellowship 205188/Z/16/Z. We also received funding from the University of Brighton COVID-19 Research Urgency Fund.
Glasgow Author(s) Enlighten ID:Wilkie, Dr Craig and Ray, Professor Surajit
Authors: Boss, A. N., Banerjee, A., Mamalakis, M., Ray, S., Swift, A., Wilkie, C., Fanstone, J. W., Vorselaars, B., Cole, J., Weeks, S., and Mackenzie, L. S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:International Journal of Molecular Sciences
ISSN (Online):1422-0067
Published Online:30 June 2022
Copyright Holders:Copyright © 2022 by the authors
First Published:First published in International Journal of Molecular Sciences 23(13): 7260
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
309324Optimisation of prediction models for red blood cell demandAlice MillerEngineering and Physical Sciences Research Council (EPSRC)EP/R511705/1Computing Science