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

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.

© 2018 S. Karger AG, Basel.

Figures

Figure:. Relationship of an AKI Risk Score to the Time Course of AKI

A clinical model using time-updated covariates is used to generate a dynamic AKI Risk Score, which reflects the probability of the development of AKI within a given time period(eg 24 or 48 hours). Beyond a pre-determined threshold of risk, alerts are triggered to allow targeting of diagnostic and therapeutic strategies prior to the patient meeting the definition of AKI by creatinine criteria.