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

NCT05988658 | Recruiting

• 2 other identifiers: IRB23-0343R01DK126933
• Study Type: observational
• Target: 800
• Locations: 1 country
• Sites: 2

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.

Conditions

Acute Kidney Injury; Biomarkers

Keywords

Acute Kidney Injury; Biomarkers; Renal Replacement Therapy; Artificial Intelligence; Risk Assessment; Clinical Nephrology

Outcome Measures

Primary Outcomes (1)

• Developing KDIGO stage 2 AKI

  Number of patients developing KDIGO Stage 2 AKI. KDIGO Stage 2 AKI defined as: A double of baseline serum creatinine from baseline OR 12 hours of urine output of less than 0.5ml/kg/hr in those with bladder catheters. If no catheter in place than urine output based AKI cannot be diagnosed

  Within 7 days of enrollment

Secondary Outcomes (5)

• Development of KDIGO stage 3 AKI

  within 12 hour of each observation, within 7 days of enrollment and 90 day MAKE outcome

• Recipient of renal replacement therapy(RRT)

  within 7 days of enrollment and 90 day make outcome

• Clinical indication for the receipt of renal replacement therapy(RRT)

  within 12 hour of each observation, within 7 days of enrollment and 90 day make outcome

• Change in Mortality Status during hospitalization

  within 12 hour of each observation, within 7 days of enrollment and during current hospitalization

• Major Adverse Kidney Events (MAKE) Outcomes

  3 months (90 days)

Study Arms (1)

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

Device: ESTOP - AKI 2.0

Interventions

ESTOP - AKI 2.0 DEVICE

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

Study cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Adult patients admitted in inpatient ward, intermediate or ICU care at UCMC or UWHealth with 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)

You may qualify if:

• Age ≥ 18 years
• 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).
• 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)

You may not qualify if:

• Voluntary refusal or missing written consent of the patient / legal representative.
• Patients with a known history of end-stage renal disease on dialysis (including renal transplantation).
• Patients without a measured serum creatinine value during their inpatient stay.
• Patients with a creatinine \>4.0 mg/dl at the time of admission or available in the EHR from the last 6 months
• Patients with prior episode of KDIGO defined AKI during this same hospitalization- regardless of E-STOP AKI 2.0 score
• Patients with prior renal consultation during their admission.
• Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago during this same hospital stay.
• Incarcerated patients
• Pregnant patients

Sponsors & Collaborators

• University of Chicago: lead
• National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK): collaborator
• University of Wisconsin, Madison: collaborator

Study Sites (2)

University of Chicago Medical Center

Chicago, Illinois, 60637, United States

RECRUITING

University of Wisconsin Hospital

Madison, Wisconsin, 53792, United States

RECRUITING

Related Publications (1)

• 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.

  PMID: 40232856 DERIVED

Biospecimen

Retention: SAMPLES WITHOUT DNA

1. Blood 2. Urine

MeSH Terms

Conditions

Acute Kidney Injury

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Jay Koyner, MD

  University of Chicago

  PRINCIPAL INVESTIGATOR

• Matthew Churpek, MD,MPH,PhD

  University of Wisconsin, Madison

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jay Koyner, MD

CONTACT

773-702-4842; jkoyner@uchicago.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 4, 2023
• First Posted: August 14, 2023
• Study Start: January 5, 2024
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2028
• Last Updated: September 12, 2025

Record last verified: 2025-09

Locations

US (2)