Real-Time Acute Kidney Injury Perioperative Prediction Clinical Trial (ML-AKI)

Prediction of Acute Kidney Injury (AKI) After Surgery: A Pragmatic Three-Arm Cluster-Randomized Trial

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Acute Kidney Injury; Anesthesia; Surgery Complications
• Intervention / Treatment: Device: EHR-Embedded AKI Risk Score; Device: EHR-Embedded AKI Risk Score with Best Practice Advisory

• Study Type: Interventional
• Enrollment (Estimated): 25518
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Andrew Bishara, MD; Phone Number: 415-502-5880; Email: andrew.bishara@ucsf.edu

Study Locations

• United States
  • California
    • San Francisco, California, United States, 94158
      • University of California, San Francisco

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• Obstetric procedures.
• Chronic dialysis patients.
• Kidney transplant recipients.
• Cases without baseline creatinine/eGFR or missing essential EHR elements needed for scoring/outcomes (operational exclusions).
• Outpatient procedures without expected overnight stay.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
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.	Device: EHR-Embedded AKI Risk Score; 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.; Other Names: EHR-Embedded AKI Clinical Decision Support Tool
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.	Device: EHR-Embedded AKI Risk Score with Best Practice Advisory; 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.; Other Names: EHR-Embedded AKI Clinical Decision Support Tool

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Acute Kidney Injury	Acute Kidney Injury as defined by KDIGO	Operation to Post-operative Day 7
KDIGO Bundle Adherence	Measurement of provider adherence to KDIGO components	Intra-operative
Intra-Operative Time and Severity of Hypotension	Intra-Operative Time and Severity (meaning how far below the threshold) where patient is in hypotension, defined as systolic blood pressure <90 mmHg and mean arterial pressure <65 mmHg during surgery	Intra-operative
Total intra-operative intravenous fluid volume administered (mL)	Provider administration of intravenous fluids during the intra-operative period, measured in milliliters (mL). Intravenous fluids include normal saline, lactated Ringer's, Plasma-Lyte, other balanced crystalloids, and colloid solutions such as albumin.	Intra-operative
Length of Stay	Duration of patient admission in hospital in days	Operation to Post-operative Day 180
Intra-operative Hyperglycemic Events	Number of intra-operative hyperglycemic events, defined as the number of recorded blood glucose measurements exceeding 180 mg/dL.	Intra-operative
Intra-operative Nephrotoxin Exposure	Number of nephrotoxic medications administered intra-operatively and duration of intra-operative exposure	Intra-operative
In-Hospital Mortality	Patient death while admitted in the hospital	Operation to Post-operative Day 180
ICU Transfer and total time in the ICU	Any transfers to the ICU while admitted and the total time the patient spends in the ICU	Postoperative
Hospital Readmission	Readmission back to a UCSF hospital following operation	Operation to Post-operative Day 180
Dialysis Requirement	Patients requiring dialysis following surgery	Operation to Post-operative Day 180
Dilution Corrected KDIGO AKI measurement (Stage 1 or higher)	Acute kidney injury (AKI) assessed using KDIGO creatinine criteria applied to dilution-corrected postoperative serum creatinine. Creatinine is corrected for hemodilution from perioperative fluid retention using the formula: Corrected Creatinine (mg/dL) = Measured Creatinine × (1 + Net Fluid Balance / Total Body Water) Where: Net Fluid Balance (L) = Fluid inputs - urine output - blood loss - other outputs; Total Body Water (L) = 0.6 × weight (kg) for males; 0.5 × weight (kg) for females	AKI is defined per KDIGO as corrected creatinine increase ≥0.3 mg/dL within 48 hours or ≥1.5× baseline within 7 days. This measure captures "hidden AKI" - kidney injury masked by fluid dilution that would be missed using standard uncorrected creatinine.
Total intra-operative packed red blood cells administered (units transfused)	Provider administration of packed red blood cells during the intra-operative period, measured as total units transfused.	intraoperative
Total intra-operative fresh frozen plasma administered (units transfused)	Provider administration of fresh frozen plasma during the intra-operative period, measured as total units transfused.	intraoperative
Total intra-operative platelets administered (units transfused)	Provider administration of platelets during the intra-operative period, measured as total units transfused.	intraoperative
Total intra-operative cryoprecipitate administered (units transfused)	Provider administration of cryoprecipitate during the intra-operative period, measured as total units transfused.	intraoperative

Collaborators and Investigators

• Sponsor: University of California, San Francisco
• Collaborators: National Institute of General Medical Sciences (NIGMS)
• Investigators: Principal Investigator: Andrew Bishara, MD, University of California, San Francisco

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): October 15, 2026
• Primary Completion (Estimated): October 15, 2027
• Study Completion (Estimated): December 15, 2027

Study Registration Dates

• First Submitted: April 9, 2026
• First Submitted That Met QC Criteria: May 19, 2026
• First Posted (Actual): May 22, 2026

Study Record Updates

• Last Update Posted (Actual): May 22, 2026
• Last Update Submitted That Met QC Criteria: May 19, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Clinical Decision Support; Acute Kidney Injury; Machine Learning; Electronic Health Records; Surgical Outcomes
• Additional Relevant MeSH Terms: Urogenital Diseases; Male Urogenital Diseases; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Renal Insufficiency; Acute Kidney Injury
• Other Study ID Numbers: 22-37005; K23GM151611-03 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes