Acute Kidney Injury, Biomarkers
Conditions
Keywords
Acute Kidney Injury, Biomarkers, Renal Replacement Therapy, Artificial Intelligence, Risk Assessment, Clinical Nephrology
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.
Detailed description
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.
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.
Sponsors
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
University of Wisconsin, Madison
University of Chicago
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥ 18 years 2. 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). 3. 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) 4. 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 criteria
1. Voluntary refusal or missing written consent of the patient / legal representative. 2. Patients with a known history of end-stage renal disease on dialysis (including renal transplantation). 3. Patients without a measured serum creatinine value during their inpatient stay. 4. Patients with a creatinine \>4.0 mg/dl at the time of admission or available in the EHR from the last 6 months 5. Patients with prior episode of KDIGO defined AKI during this same hospitalization- regardless of E-STOP AKI 2.0 score 6. Patients with prior renal consultation during their admission. 7. Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago during this same hospital stay. 8. Incarcerated patients 9. Pregnant patients
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Developing KDIGO stage 2 AKI | Within 7 days of enrollment | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Development of KDIGO stage 3 AKI | within 12 hour of each observation, within 7 days of enrollment and 90 day MAKE outcome | 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) |
| Recipient of renal replacement therapy(RRT) | within 7 days of enrollment and 90 day make outcome | The number of patients who receive RRT |
| 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 | 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. |
| Change in Mortality Status during hospitalization | within 12 hour of each observation, within 7 days of enrollment and during current hospitalization | Patients' mortality status during current hospitalization |
| Major Adverse Kidney Events (MAKE) Outcomes | 3 months (90 days) | 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 |
Countries
United States
Contacts
Primary ContactJay Koyner, MD
Outcome results
None listed