| Literature DB >> 31514906 |
Luke E Hodgson1, Nicholas Selby2, Tao-Min Huang3, Lui G Forni4.
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.Entities:
Keywords: Acute kidney injury; electronic alerts; prediction models
Mesh:
Year: 2019 PMID: 31514906 DOI: 10.1016/j.semnephrol.2019.06.002
Source DB: PubMed Journal: Semin Nephrol ISSN: 0270-9295 Impact factor: 5.299