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).

ANALYTICAL APPROACH

Descriptive statistics and univariable associations.

RESULTS

At enrollment, mean predicted probability of AKI was 19.1%; 18.9% of patients went on to develop AKI. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI vs 18% without), heart rate>100 beats/min (32% of patients with AKI vs 24% without), and oxygen saturation<92% (15% of patients with AKI vs 6% without) were all more common among those who developed AKI. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI.

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.