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.
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.
Study Type
OBSERVATIONAL
Enrollment
800
Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject/clinical care.
University of Chicago Medical Center
Chicago, Illinois, United States
RECRUITINGUniversity of Wisconsin Hospital
Madison, Wisconsin, United States
RECRUITINGDeveloping 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
Time frame: Within 7 days of enrollment
Development of KDIGO stage 3 AKI
Number of patients developing KDIGO Stage 3 AKI KDIGO Stage 3 AKI defined as: Increase in Serum creatinine by 3.0 times baseline OR Increase serum creatinine to \> 4.0 mg/dL OR Need for Renal Replacement Therapy (RRT)
Time frame: within 12 hour of each observation, within 7 days of enrollment and 90 day MAKE outcome
Recipient of renal replacement therapy(RRT)
The number of patients who receive RRT
Time frame: within 7 days of enrollment and 90 day make outcome
Clinical indication for the receipt of renal replacement therapy(RRT)
The number of patients who have a clinical indication to receive RRT (even if they do not receive it) due to following indications (in the setting of Stage 2/3 AKI): 1. Hyperkalemia (≥ 6 mmol/L) 2. Diuretic-resistant hypervolemia (difficult to define) 3. BUN urea serum levels greater than or equal to 150mg/ dL 4. Severe metabolic acidosis (pH ≤ 7.15) 5. Oliguria (urinary output \< 200mL/12hr), or anuria.
Time frame: within 12 hour of each observation, within 7 days of enrollment and 90 day make outcome
Change in Mortality Status during hospitalization
Patients' mortality status during current hospitalization
Time frame: within 12 hour of each observation, within 7 days of enrollment and during current hospitalization
Major Adverse Kidney Events (MAKE) Outcomes
Number of Participants developing Major Adverse Kidney Events (MAKE): 1. Recurrent Hospitalization 2. Kidney Function Status: * Recurrent AKI * New chronic kidney disease (CKD) * Need or continued need for RRT 3. Mortality
Time frame: 3 months (90 days)
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