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dc.contributor.authorSelby, Nicholas
dc.date.accessioned2020-07-15T14:32:07Z
dc.date.available2020-07-15T14:32:07Z
dc.date.issued2019-09
dc.identifier.citationSemin Nephrol. 2019 Sep;39(5):421-430. doi: 10.1016/j.semnephrol.2019.06.002.en
dc.identifier.urihttp://hdl.handle.net/20.500.12904/801
dc.descriptionAuthor(s) pre or post print version onlyen
dc.description.abstractAcute kidney injury is a major health care problem. Improving recognition of those at risk and highlighting those who have developed AKI at an earlier stage remains a priority for research and clinical practice. Prediction models to risk-stratify patients and electronic alerts for AKI are two approaches that could address previously highlighted shortcomings in management and facilitate timely intervention. We describe and critique available prediction models and the effects of the use of AKI alerts on patient outcomes are reviewed. Finally, the potential for prediction models to enrich population subsets for other diagnostic approaches and potential research, including biomarkers of AKI, are discussed.en
dc.language.isoenen
dc.subjectAKIen
dc.subjectAcute Kidney Injuryen
dc.subjectPrediction Modelsen
dc.subjectElectronic Alertsen
dc.titleThe Role of Risk Prediction Models in Prevention and Management of AKI.en
dc.typeArticleen


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