Study of Blood and Urine Biomarkers with Electronic Risk Scores to Predict Severe Acute Kidney Injury in Hospitalized Adults

What this Trial Is About

Researchers are evaluating how combining kidney-related biomarkers from blood and urine with a new computer-based risk score can better identify hospitalized patients at high risk for severe acute kidney injury AKI. This observational study focuses on patients who have a high risk of developing Stage 2 AKI, using a natural language processing NLP based AKI risk algorithm that analyzes electronic health record data from labs, vital signs, clinical notes, and test reports. The study is conducted at the University of Chicago Medical Center and the University of Wisconsin Hospital. Patients identified as high risk top 10% risk score will be enrolled and have blood and urine samples collected for biomarker testing over three days. The study plans to recruit two groups of 400 patients each across the two hospitals. The first group will help researchers determine if adding biomarkers to the electronic risk score improves prediction of Stage 2 AKI and other outcomes. The findings will then be validated in the second group. The risk score software is used as a noninvasive device but will not directly impact clinical care during the study. Participants will be admitted patients at inpatient wards, intermediate care, or ICU, and will be monitored for the development of KDIGO Stage 2 AKI within seven days of enrollment. Researchers will collect clinical data continuously and measure outcomes including Stage 3 AKI, need for kidney dialysis, mortality during hospitalization, and major adverse kidney events over 90 days. The study involves consent procedures and excludes patients with certain kidney conditions, prior AKI episodes during the admission, or those unable to consent in English. The total study participation period includes the initial 3-day biomarker collection and follow-up for up to 90 days.

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