A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital.

Background: Acute kidney injury (AKI) is a common and serious problem in hospitalized patients. Early detection is critical for optimal management but in practice is currently inadequate. To improve outcomes in AKI, development of early detection tools is essential.

Methods: We developed an automated real-time electronic alert system employing algorithms which combined internationally recognized criteria for AKI [Risk, Injury, Failure, Loss, End-stage kidney disease (RIFLE) and Acute Kidney Injury Network (AKIN)]. All adult patients admitted to Nottingham University Hospitals were included. Where a patient's serum creatinine increased sufficiently to define AKI, an electronic alert was issued, with referral to an intranet-based AKI guideline. Incidence of AKI Stages 1-3, in-hospital mortality, length of stay and distribution between specialties is reported.

Results: Between May 2011 and April 2013, 59,921 alerts resulted from 22,754 admission episodes, associated with 15,550 different patients. Overall incidence of AKI for inpatients was 10.7%. Highest AKI stage reached was: Stage 1 in 7.2%, Stage 2 in 2.2% and Stage 3 in 1.3%. In-hospital mortality for all AKI stages was 18.5% and increased with AKI stage (12.5, 28.4, 35.7% for Stages 1, 2 and 3 AKI, respectively). Median length of stay was 9 days for all AKI.

Conclusions: This is the first fully automated real time AKI e-alert system, using AKIN and RIFLE criteria, to be introduced to a large National Health Service hospital. It has provided one of the biggest single-centre AKI datasets in the UK revealing mortality rates which increase with AKI stage. It is likely to have improved detection and management of AKI. The methodology is transferable to other acute hospitals.