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Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients
Sponsor: University of Chicago
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.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
800
Start Date
2024-01-05
Completion Date
2028-03-01
Last Updated
2025-09-12
Healthy Volunteers
No
Conditions
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.
Locations (2)
University of Chicago Medical Center
Chicago, Illinois, United States
University of Wisconsin Hospital
Madison, Wisconsin, United States