Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients

NCT ID: NCT05988658

Last Updated: 2025-09-12

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 800 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-01-05
• Study Completion Date: 2028-03-01

Brief Summary

The study's objective is to evaluate the additive value of renal biomarkers (from blood and urine) for identifying individuals at high risk for severe acute kidney injury (AKI) above that of a novel natural language processing (NLP)-based AKI risk algorithm. The risk algorithm is based on electronic health records (EHR) data (labs, vitals, clinical notes, and test reports). Patients will enroll at the University of Chicago Medical Center and the University of Wisconsin Hospital, where the risk score will run in real time. The risk score will identify those patients with the highest risk for the future development of Stage 2 AKI and collect blood and urine for biomarker measurement over the subsequent 3 days.

Detailed Description

The investigators hypothesize that combining the biomarkers with electronic health risk score will impact improvement in AKI risk stratification. Using a real time, externally validated electronic health record based AKI risk score, the investigators will enroll patients who are at high risk for the impending development of KDIGO Stage 2 AKI (top 10% of risk). Once identified and enrolled, patients will have blood and urine samples collected over the next 3 days. The investigators will recruit two cohorts of 400 patients across the two institutions. In the development cohort, the investigators will see if adding urinary or blood biomarkers of AKI can improve the ability of EHR-risk score to predict the development of Stage 2 AKI and other outcomes. The investigators will compare the area under the receiver operator characteristic curve (AUC) for the risk score alone versus the risk score plus biomarkers. The investigators will then seek to validate our findings in a separate cohort of 400 patients.

Conditions

Acute Kidney Injury; Biomarkers

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Study cohort

Patients will be identified as high risk based on their AKI risk score (ESTOP- AKI 2.0) being in the top 10% of all hospitalized patients

ESTOP - AKI 2.0

Intervention Type: DEVICE

Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject/clinical care.

Interventions

ESTOP - AKI 2.0

Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject/clinical care.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

1. Age ≥ 18 years

2. E-STOP AKI 2.0 score in the top 10% of risk (historically from all hospitalized patients) within the last 12 hours. (First time across this 10% risk threshold during this hospital stay).

3. Admitted to an inpatient ward, intermediate, or ICU care at the University of Chicago Medical Center (UCMC) or University of Wisconsin Health (UWHealth). (No Emergency Department patients)

4. Patient or their legally authorized representative must be able to read, speak, and understand English, for the purposes of consenting. Otherwise, inclusion in this protocol will be done without regard to race, ethnic origin or gender

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 EHR from the last 6 months

5. Patients with prior episode of KDIGO defined AKI during this same hospitalization- regardless of E-STOP AKI 2.0 score

6. Patients with prior renal consultation during their admission.

7. Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago during this same hospital stay.

8. Incarcerated patients

9. Pregnant patients

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

NIH

Sponsor Role: collaborator

University of Wisconsin, Madison

OTHER

Sponsor Role: collaborator

University of Chicago

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Jay Koyner, MD

Role: PRINCIPAL_INVESTIGATOR

University of Chicago

Matthew Churpek, MD,MPH,PhD

Role: PRINCIPAL_INVESTIGATOR

University of Wisconsin, Madison

Locations

University of Chicago Medical Center

Chicago, Illinois, United States

Site Status: RECRUITING

University of Wisconsin Hospital

Madison, Wisconsin, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Jay Koyner, MD

Role: CONTACT

jkoyner@uchicago.edu

773-702-4842

Facility Contacts

Aiman Fatima, MBBS

Role: primary

aimanfatima@uchicagomedicine.org

773-702-6201

Ola Anjorin, MBBS,DA,MPH

Role: backup

oanjorin@bsd.uchicago.edu

773-704-3168

Madeline Ogus, MS

Role: primary

mkoguss@medicine.wisc.edu

608-265-2878

Michael Weber

Role: backup

mjweber@medicine.wisc.edu

608-263-3369

References

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

R01DK126933

Identifier Type: NIH

Identifier Source: secondary_id

View Link

IRB23-0343

Identifier Type: -

Identifier Source: org_study_id