Business Transformation and Innovation Management
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Introduction
There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often
associated with kidney failure.
Methods
We assessed whether proteinuria can be predictive of kidney failure, the development of
chronic kidney disease, and mortality in 37 critically ill COVID-19 patients. We used machine
learning (ML) methods as decision trees and cut-off points created by the OneR package to
add new aspects, even in smaller cohorts.
Results
Among a total of 37 patients, 24 suffered higher-grade renal failure, 20 of whom required kidney
replacement therapy. More than 40% of patients remained on hemodialysis after intensive
care unit discharge or died (27%). Due to frequent anuria proteinuria measured in two-thirds of
the patients, it was not predictive for the investigated endpoints; albuminuria was higher in
patients with AKI 3, but the difference was not significant. ML found cut-off points of >31.4 kg/
m2 for BMI and >69 years for age, constructed decision trees with great accuracy, and identified
highly predictive variables for outcome and remaining chronic kidney disease.
Conclusions
Different ML methods and their clinical application, especially decision trees, can provide
valuable support for clinical decisions. Presence of proteinuria was not predictive of CKD or
AKI and should be confirmed in a larger cohort.