Article in Int. J. Mol. Sci. on a mortality prediction model in hospitalised SARS-CoV-2 positive patients based on routine kidney biomarkers

Our paper “Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers” part of the special issue New Insights into the Molecular Mechanisms of Kidney Injury and Repair has been published in the journal International Journal of Molecular Sciences (with impact factor 6.2) and is available here. The work results from a collaboration between the University of Brighton, University of Oxford, University of Sheffield, University of Glasgow and the University of Lincoln.

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

A.N. Boss, A. Banerjee, M. Mamalakis, S. Ray, A. J. Swift, C. Wilkie, J. W. Fanstone, B. Vorselaars, J. Cole, S. Weeks, L. S. Mackenzie, Int. J. Mol. Sci., 7260, 23 (2022)

Abstract: 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.

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