Real-Time Acute Kidney Injury Perioperative Prediction Clinical Trial

NCT07604662 | Not Yet Recruiting FDA Device

• 2 other identifiers: 22-37005K23GM151611-03
• Study Type: interventional
• Target: 25,518
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Acute Kidney Injury; Surgery Complications; Anesthesia

Keywords

Acute Kidney Injury; Surgical Outcomes; Machine Learning; Clinical Decision Support; Electronic Health Records

Outcome Measures

Primary Outcomes (1)

• Post-operative Change in Creatinine

  Maximum continuous change in serum creatinine (mg/dL) from baseline to post-operative day 1-2

  From pre-operative baseline to 1-2 days post-operative level

Secondary Outcomes (16)

• Acute Kidney Injury

  Operation to Post-operative Day 7

• KDIGO Bundle Adherence

  Intra-operative

• Intra-Operative Time and Severity of Hypotension

  Intra-operative

• Total intra-operative intravenous fluid volume administered (mL)

  Intra-operative

• Length of Stay

  Operation to Post-operative Day 180

Study Arms (3)

Control Arm

NO INTERVENTION

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.

Acute Kidney Injury Risk Score Only

EXPERIMENTAL

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.

Device: EHR-Embedded AKI Risk Score

Acute Kidney Injury Risk Score with Best Practice Advisory

EXPERIMENTAL

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.

Device: EHR-Embedded AKI Risk Score with Best Practice Advisory

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.

Also known as: EHR-Embedded AKI Clinical Decision Support Tool

Acute Kidney Injury Risk Score Only

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.

Also known as: EHR-Embedded AKI Clinical Decision Support Tool

Acute Kidney Injury Risk Score with Best Practice Advisory

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adults ≥18 years undergoing non-obstetric surgery at UCSF.
• Inpatient cases with expected overnight stay.
• Baseline eGFR ≥15 mL/min/1.73 m².
• Managed by an attending anesthesiologist randomized to one of three arms (CRNAs/residents follow attending).
• Data available in the UCSF EHR for risk scoring and outcomes.

You may not qualify if:

• Obstetric procedures.
• Chronic dialysis patients.
• Kidney transplant recipients.
• Outpatient procedures without expected overnight stay.

Sponsors & Collaborators

• University of California, San Francisco: lead
• National Institute of General Medical Sciences (NIGMS): collaborator

Study Sites (1)

University of California, San Francisco

San Francisco, California, 94158, United States

Location

Related Publications (7)

• James MT, Dixon E, Tan Z, Mathura P, Datta I, Lall RN, Landry J, Minty EP, Samis GA, Winkelaar GB, Pannu N. Stepped-Wedge Trial of Decision Support for Acute Kidney Injury on Surgical Units. Kidney Int Rep. 2024 Jul 31;9(10):2996-3005. doi: 10.1016/j.ekir.2024.07.025. eCollection 2024 Oct.

  PMID: 39430177 BACKGROUND

• Zarbock A, Kullmar M, Ostermann M, Lucchese G, Baig K, Cennamo A, Rajani R, McCorkell S, Arndt C, Wulf H, Irqsusi M, Monaco F, Di Prima AL, Garcia Alvarez M, Italiano S, Miralles Bagan J, Kunst G, Nair S, L'Acqua C, Hoste E, Vandenberghe W, Honore PM, Kellum JA, Forni LG, Grieshaber P, Massoth C, Weiss R, Gerss J, Wempe C, Meersch M. Prevention of Cardiac Surgery-Associated Acute Kidney Injury by Implementing the KDIGO Guidelines in High-Risk Patients Identified by Biomarkers: The PrevAKI-Multicenter Randomized Controlled Trial. Anesth Analg. 2021 Aug 1;133(2):292-302. doi: 10.1213/ANE.0000000000005458.

  PMID: 33684086 BACKGROUND

• Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179-84. doi: 10.1159/000339789. Epub 2012 Aug 7. No abstract available.

  PMID: 22890468 BACKGROUND

• Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013 Sep;119(3):507-15. doi: 10.1097/ALN.0b013e3182a10e26.

  PMID: 23835589 BACKGROUND

• Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015 Sep;123(3):515-23. doi: 10.1097/ALN.0000000000000765.

  PMID: 26181335 BACKGROUND

• Kork F, Balzer F, Spies CD, Wernecke KD, Ginde AA, Jankowski J, Eltzschig HK. Minor Postoperative Increases of Creatinine Are Associated with Higher Mortality and Longer Hospital Length of Stay in Surgical Patients. Anesthesiology. 2015 Dec;123(6):1301-11. doi: 10.1097/ALN.0000000000000891.

  PMID: 26492475 BACKGROUND

• Fujii T, Takakura M, Taniguchi T, Tamura T, Nishiwaki K. Intraoperative hypotension affects postoperative acute kidney injury depending on the invasiveness of abdominal surgery: A retrospective cohort study. Medicine (Baltimore). 2023 Dec 1;102(48):e36465. doi: 10.1097/MD.0000000000036465.

  PMID: 38050260 BACKGROUND

MeSH Terms

Conditions

Acute Kidney Injury

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Andrew Bishara, MD

  University of California, San Francisco

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Andrew Bishara, MD

CONTACT

415-502-5880; andrew.bishara@ucsf.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: This is a pragmatic, single-center, three-arm, parallel-group, cluster-randomized controlled trial. Attending anesthesiologists are the unit of randomization and are assigned in a 1:1:1 ratio to one of three groups: (1) control (AKI risk score not displayed), (2) score only (visible preoperative machine learning-derived AKI risk score with passive KDIGO bundle recommendation), or (3) score plus Best Practice Advisory (visible risk score with an interruptive KDIGO-based alert for high-risk patients). All eligible surgical cases managed by a given attending anesthesiologist inherit that provider's assigned study arm. Trainees and nurse anesthetists follow the assignment of the supervising attending. The intervention is delivered within the electronic health record at the point of care. The clinical decision support tools are advisory only and do not mandate any clinical actions. There is no crossover between groups, and allocation remains fixed for the duration of the study.

Study Record Dates

• First Submitted: April 9, 2026
• First Posted: May 22, 2026
• Study Start (Estimated): October 15, 2026
• Primary Completion (Estimated): October 15, 2027
• Study Completion (Estimated): December 15, 2027
• Last Updated: May 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)