An Early Real-Time Electronic Health Record Risk Algorithm for the Prevention and Treatment of Acute Kidney Injury
NCT ID: NCT03590028
Last Updated: 2025-08-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
NA
180 participants
INTERVENTIONAL
2018-10-01
2026-01-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
NONE
Study Groups
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Early Nephrology Consult (ENC)
The ENC will be a structured consultative note that will provide detailed recommendations around issues such as Differential Diagnosis, Drug Dosing and Volume Status. The research ENC will have a daily follow-up with documented recommendations.
Early Nephrology Consult (ENC)
The electronic risk prediction algorithm (ESTOP-AKI) will interface with electronic medical data to determine the likelihood for the patient to develop AKI. Early Nephrology Consult (ENC) will be implemented. A nephrologist will assess the subject and consult with their care team to advise a treatment plan during the hospitalization.
Standard of Care (SOC)
Subjects will receive nephrology consultation at the typical timepoint after symptoms of AKI appear.
Standard of Care (SOC)
Subjects will receive nephrology consultation at the typical timepoint after symptoms of AKI appear.
Interventions
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Early Nephrology Consult (ENC)
The electronic risk prediction algorithm (ESTOP-AKI) will interface with electronic medical data to determine the likelihood for the patient to develop AKI. Early Nephrology Consult (ENC) will be implemented. A nephrologist will assess the subject and consult with their care team to advise a treatment plan during the hospitalization.
Standard of Care (SOC)
Subjects will receive nephrology consultation at the typical timepoint after symptoms of AKI appear.
Eligibility Criteria
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Inclusion Criteria
2. Initial ESTOP AKI score ≥0.01 within the last 8 hours.
Exclusion Criteria
2. Patients with a known history of end-stage renal disease on dialysis (including renal transplantation).
3. Patients without a measured serum creatinine value during their inpatient stay.
4. Patients with a creatinine \>4.0 mg/dl at the time of admission or available in the electronic health record (EHR) from the last 6 months.
5. Patients with prior episode of Kidney Disease Improving Global Outcomes (KDIGO) defined AKI during this same hospitalization- regardless of ESTOP AKI score.
6. Patients with prior renal consultation during their admission.
18 Years
ALL
No
Sponsors
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University of Chicago
OTHER
Responsible Party
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Principal Investigators
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Jay Koyner, MD
Role: PRINCIPAL_INVESTIGATOR
University of Chicago Medicine
Locations
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University of Chicago Medical Center
Chicago, Illinois, United States
Countries
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References
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Koyner JL, Martin J, Carey KA, Caskey J, Edelson DP, Mayampurath A, Dligach D, Afshar M, Churpek MM. Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI. Clin J Am Soc Nephrol. 2025 Apr 15;20(6):766-778. doi: 10.2215/CJN.0000000695.
Other Identifiers
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IRB17-1081
Identifier Type: -
Identifier Source: org_study_id
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