Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.

Rationale & Objective: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm.
Study Design: Prospective observational cohort study.
Setting & Participants: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers.
Exposure: Clinical characteristics at the time of pre-AKI alert.
Outcome: AKI within 24 hours of pre-AKI alert (AKI ₂₄ ).
Analytical Approach: Descriptive statistics and univariable associations.
Results: At enrollment, mean predicted probability of AKI ₂₄ was 19.1%; 18.9% of patients went on to develop AKI ₂₄ . Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI ₂₄ and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI ₂₄ vs 18% without), heart rate>100 beats/min (32% of patients with AKI ₂₄ vs 24% without), and oxygen saturation<92% (15% of patients with AKI ₂₄ vs 6% without) were all more common among those who developed AKI ₂₄ . Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI ₂₄ vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI ₂₄ vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI ₂₄ .
Limitations: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation.
Conclusions: A real-time AKI risk model was successfully integrated into the EHR.
(Copyright © 2020 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)