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Article

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

1
School of Applied Sciences, University of Brighton, Brighton BN2 4GJ, UK
2
Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
3
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
4
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
5
Brighton and Sussex Medical School, Falmer Campus, University of Brighton, Brighton BN1 9PX, UK
6
School of Mathematics and Physics, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Peter Hamar and Tzong-Shyuan Lee
Int. J. Mol. Sci. 2022, 23(13), 7260; https://doi.org/10.3390/ijms23137260
Received: 25 April 2022 / Revised: 21 June 2022 / Accepted: 28 June 2022 / Published: 30 June 2022
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. View Full-Text
Keywords: SARS-CoV-2; prediction model; kidney function; COVID-19 SARS-CoV-2; prediction model; kidney function; COVID-19
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MDPI and ACS Style

Boss, A.N.; Banerjee, A.; Mamalakis, M.; Ray, S.; Swift, A.J.; Wilkie, C.; Fanstone, J.W.; Vorselaars, B.; Cole, J.; Weeks, S.; Mackenzie, L.S. Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers. Int. J. Mol. Sci. 2022, 23, 7260. https://doi.org/10.3390/ijms23137260

AMA Style

Boss AN, Banerjee A, Mamalakis M, Ray S, Swift AJ, Wilkie C, Fanstone JW, Vorselaars B, Cole J, Weeks S, Mackenzie LS. Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers. International Journal of Molecular Sciences. 2022; 23(13):7260. https://doi.org/10.3390/ijms23137260

Chicago/Turabian Style

Boss, Anna N., Abhirup Banerjee, Michail Mamalakis, Surajit Ray, Andrew J. Swift, Craig Wilkie, Joseph W. Fanstone, Bart Vorselaars, Joby Cole, Simonne Weeks, and Louise S. Mackenzie. 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, no. 13: 7260. https://doi.org/10.3390/ijms23137260

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