RecruitingNCT05988658

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


Sponsor

University of Chicago

Enrollment

800 participants

Start Date

Jan 5, 2024

Study Type

OBSERVATIONAL

Conditions

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.


Eligibility

Min Age: 18 Years

Inclusion Criteria4

  • 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)
  • 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 Criteria9

  • 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

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Interventions

DEVICEESTOP - 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

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NCT05988658


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