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

Results pending

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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Recruitment Status

ACTIVE_NOT_RECRUITING

Clinical Phase

NA

Total Enrollment

180 participants

Study Classification

INTERVENTIONAL

Study Start Date

2018-10-01

Study Completion Date

2026-01-31

Brief Summary

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This is a single center randomized trial that seeks to determine if the use of an automated real-time electronic medical record Acute Kidney Injury (AKI) risk score can improve patient outcomes through the use of an early standardized nephrology focused intervention.

Detailed Description

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Conditions

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Acute Kidney Injury

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

TREATMENT

Blinding Strategy

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.

Group Type EXPERIMENTAL

Early Nephrology Consult (ENC)

Intervention Type OTHER

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.

Group Type ACTIVE_COMPARATOR

Standard of Care (SOC)

Intervention Type OTHER

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.

Intervention Type OTHER

Standard of Care (SOC)

Subjects will receive nephrology consultation at the typical timepoint after symptoms of AKI appear.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

1. Age \>18 years old
2. Initial ESTOP AKI score ≥0.01 within the last 8 hours.

Exclusion Criteria

1. Voluntary refusal or missing written consent of the patient / legal representative.
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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Chicago

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

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

Site Status

Countries

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United States

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.

Reference Type DERIVED
PMID: 40232856 (View on PubMed)

Other Identifiers

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IRB17-1081

Identifier Type: -

Identifier Source: org_study_id

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