Real-Time Acute Kidney Injury Perioperative Prediction Clinical Trial

Part of paid clinical trials in San Francisco, California.

Sponsor
University of California, San Francisco
Study ID
NCT07604662
Status
Not Yet Recruiting

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Conditions

  • Acute Kidney Injury
  • Anesthesia
  • Surgery Complications

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • EHR-Embedded AKI Risk Score — DEVICE
    A non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers without an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow.
  • EHR-Embedded AKI Risk Score with Best Practice Advisory — DEVICE
    A non-adaptive, machine learning-based clinical decision support tool integrated into the electronic health record that generates a preoperative probability of acute kidney injury (AKI) using routinely collected patient data. For patients identified as high risk, the tool displays the risk estimate to anesthesia providers with an accompanying Best Practice Advisory (BPA) recommending consideration of a KDIGO-based kidney-protective bundle. The intervention is advisory only, does not mandate clinical actions, and is designed to support provider decision-making within the existing clinical workflow.

Study Details

This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with an interruptive Best/Our Practice Advisory (BPA/OPA)-improves kidney-protective care and reduces kidney injury after non-obstetric surgery at UCSF. Approximately 75-100 attending anesthesiologists (clusters) are randomized 1:1:1 to: (a) Control (risk score hidden), (b) Score Only (visible preoperative AKI risk probability with passive KDIGO bundle recommendation), or (c) Score + BPA (visible risk plus interruptive KDIGO prompt for high-risk patients). CRNAs/residents follow their attending' s assignment. Adult inpatients (age ≥18) with expected overnight stay and eGFR ≥15 mL/min/1.73 m² are included; obstetrics, chronic dialysis, and kidney transplant patients are excluded. The underlying preoperative model was prospectively validated at UCSF and outperforms anesthesiologist risk estimation reported in the literature. The model was reviewed and approved by the AI Oversight Committee at UCSF. Primary endpoint is the continuous change in serum creatinine (mg/dL) from baseline to POD 1-2. Secondary outcomes include KDIGO-defined AKI, adherence to bundle elements (hemodynamics, balanced fluids, nephrotoxin avoidance, glycemic control), intraoperative hypotension time, fluid volumes, nephrotoxin exposure, perioperative hyperglycemia, length of stay, unplanned ICU transfer, readmission, dialysis, and in-hospital mortality. Data are obtained from the EHR; analysts are blinded. No direct subject interaction is planned; the investigators will request a waiver of patient consent. The study aims to demonstrate that ML-enabled, workflow-embedded decision support can safely and feasibly improve guideline concordant care and decrease early postoperative kidney injury.

Key Dates

Start date
Oct 15, 2026
Status verified
May 2026
Primary completion
Oct 15, 2027
Completion
Dec 15, 2027

Study Design

Enrollment
25,518 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING

Arms

  • No Intervention: Control Arm
    Participants receive usual perioperative care with a placeholder blank display without the machine learning-derived acute kidney injury (AKI) risk score. The clinical decision support tool remains hidden in the electronic health record, and no alerts or recommendations related to the study are shown.
  • Experimental: Acute Kidney Injury Risk Score Only
    A machine learning-derived preoperative AKI risk score is displayed within the electronic health record for high-risk patients. A passive recommendation indicating that the patient may benefit from a KDIGO-based kidney-protective bundle is provided. The information is advisory only, and no interruptive alerts are used.
  • Experimental: Acute Kidney Injury Risk Score with Best Practice Advisory
    The machine learning-derived AKI risk score is displayed within the electronic health record for high-risk patients, accompanied by an interruptive Best Practice Advisory (BPA) that notifies providers that the patient may benefit from a KDIGO-based kidney-protective bundle. The alert is advisory only and does not mandate clinical actions.

Primary Outcome Measure

Post-operative Change in Creatinine [ Time Frame: From pre-operative baseline to 1-2 days post-operative level ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
University of California, San FranciscoSan FranciscoCalifornia94158-

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