Clinical trial NCT06840210: Effectiveness-Implementation Evaluation of Acute Kidney Injury Decision Support
Official title: Transforming Kidney Care in the Emergency Department: A Prospective Effectiveness-Implementation Evaluation
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Acute Kidney Injury
Keywords: Acute Kidney Injury
AKI
Prediction
Machine Learning
Emergency Department
Clinical Decision Support
Lead sponsor: Johns Hopkins University
Sponsor class: OTHER
Collaborators: name: Agency For Healthcare Research & Quality
First posted: 2025-02-21
Enrollment: 200000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Emergency Department

Brief summary: Study Purpose: This study is testing an artificial intelligence (AI)-powered clinical decision support (CDS) system designed to help emergency department (ED) doctors detect and manage acute kidney injury (AKI) earlier. The goal is to see whether the tool improves patient care, clinician decision-making, and hospital outcomes when used in real-world ED settings.

Study Design:

The AI tool will be gradually introduced at three hospital EDs:

Johns Hopkins Hospital (JHH) Bayview Medical Center (BMC) Howard County General Hospital (HCGH)

Before the tool is activated, it will run in the background to collect baseline data without influencing care. Once implemented, doctors will receive training, and researchers will track how often the tool is used and whether it improves AKI care.

What the Study Measures:

Process Outcomes: Does the tool help doctors identify AKI sooner, avoid harmful medications, and improve decision-making about hospitalization?

Clinical Outcomes: Does the tool reduce the number and severity of AKI cases and improve kidney-related health outcomes?

Implementation Outcomes: Do ED doctors find the tool useful? Does it fit into the ED doctor's workflow without slowing the ED doctor's down?

Expected Impact: If successful, the AI tool could be expanded to other hospitals and used to improve early detection and treatment of AKI, reducing kidney complications and improving patient care nationwide.


Eligibility criteria: Inclusion Criteria:

Participants must meet all of the following conditions to be eligible for the study:

1. Emergency Department Visit

   Patients presenting to one of the three study site emergency departments (EDs):

   Johns Hopkins Hospital (JHH) Bayview Medical Center (BMC) Howard County General Hospital (HCGH) Serum Creatinine Measurement
2. At least one serum creatinine (sCr) test performed during the ED visit.
3. Age: Adults (≥18 years old) at the time of ED visit.
4. Follow-Up Data Available: To be included in analysis for the investigator's secondary effectiveness outcome (new or progressive AKI) patients must have repeat creatinine concentration measurement available within 72 hours

Exclusion Criteria:

1. No ED Serum Creatinine Data: Patients who do not have a measured serum creatinine (sCr) value during the ED visit will not have decision support provided during the ED stay.
2. Patients Discharged Without Follow-Up Data: Patients discharged from the ED who do not undergo a follow-up serum creatinine test within 72 hours will be excluded from analysis of new or progressive AKI outcome assessment.
3. Age \<18 Years
4. End-Stage Kidney Disease (ESKD) or Chronic Dialysis: Patients with a documented history of end-stage kidney disease (ESKD), (patients receiving chronic dialysis (hemodialysis or peritoneal dialysis) will will be excluded from analysis of new or progressive AKI outcome assessment).

Primary outcomes:
measure: Number of Patients who receive Guideline-Concordant Kidney Care
timeFrame: From time of decision support provision until departure from the ED, approximately 6 hours
Secondary outcomes:
measure: New or Progressive Acute Kidney Injury
timeFrame: Within 72 hours of first creatinine measurement in the ED
measure: Perceived usefulness and usability of AKIDS software as assessed by the System Usability Score
timeFrame: Prior to AKIDS implementation, immediately after implementation, and post implementation 6 months
Other outcomes:
Locations:
Study officials:
affiliation: Johns Hopkins University
name: Jeremiah Hinson, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: