'Acute kidney injury predictive models : advanced yet far from application in resource-constrained settings.'
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Date
Authors
Mrara, Busisiwe
Paruk, Fathima
Oladimeji, Olanrewaju
Journal Title
Journal ISSN
Volume Title
Publisher
F1000 Research Ltd
Abstract
Acute kidney injury (AKI) remains a significant cause of morbidity and mortality in hospitalized patients, particularly critically ill patients. It poses a public health challenge in resource-constrained settings due to high administrative costs. AKI is commonly misdiagnosed due to its painless onset and late disruption of serum creatinine, which is the gold standard biomarker for AKI diagnosis. There is increasing research into the use of early biomarkers and the development of predictive models for early AKI diagnosis using clinical, laboratory, and imaging data. This field note provides insight into the challenges of using available AKI prediction models in resource-constrained environments, as well as perspectives that practitioners in these settings may find useful.
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DATA AVAILABILITY : No data are associated with this article.
Keywords
Predictive models, Resource-constrained settings, Acute kidney injury (AKI)
Sustainable Development Goals
Citation
Mrara, B., Paruk, F. & Oladimeji, O. "Acute Kidney Injury predictive models: advanced yet far from
application in resource-constrained settings." F1000Research 2022, 11:642
https://DOI.org/10.12688/f1000research.122344.2.