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Nephron. 2018;140(2):116-119. doi: 10.1159/000492064. Epub 2018 Aug 2.

Acute Kidney Injury in Real Time: Prediction, Alerts, and Clinical Decision Support.

Author information

1
Program of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
2
Department of Internal Medicine, Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut, USA.
3
Department of Pediatrics, Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut, USA.

Abstract

Broad adoption of electronic health record (EHR) systems has facilitated acute kidney injury (AKI) research in 2 ways. First, the detection of AKI based on changes in serum creatinine has largely replaced the sensitive but nonspecific administrative coding of AKI that predominated in earlier studies. Second, the ability to implement real-time AKI interventions such as alerts and best-practice advisories has emerged as a promising tool to fight against the harmful sequela of AKI, which include short-term adverse outcomes as well as progression to chronic kidney disease, dialysis, and death. In this review, we discuss the current state-of-the-art in EHR-based tools to predict imminent AKI, alert to the presence of AKI, and modify provider behaviors in the presence of AKI.

KEYWORDS:

Acute renal injury; Alert; Clinical decision support; Real time modeling

PMID:
30071528
PMCID:
PMC6165685
[Available on 2019-08-02]
DOI:
10.1159/000492064
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