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The Role of Risk Prediction Models in Prevention and Management of AKI
Luke E Hodgson1, Nicholas Selby2, Tao-Min Huang3
1Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, United Kingdom; Anaesthetics Department, Worthing Hospital, Western Sussex Hospitals National Health Service Foundation Trust, Worthing, United Kingdom.
Abstract:
Acute 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.
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