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

Abstract 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 of AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record (EHR) 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 of AKI in the next 24 hours exceeding 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 (AKI24) Analytical Approach Descriptive statistics and univariable associations Results At enrollment, the mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (p 100 bpm (32% of patients with AKI24 versus 24% without) and oxygen saturation Limitations Single-center study, reliance on serum creatinine for AKI diagnosis, small number of patients undergoing biomarker evaluation. Conclusions A real-time AKI risk model was successfully integrated into the EHR.