Not_yet_recruitingNANCT07604662

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

Sponsor: University of California, San Francisco

Trial Details

Condition	Acute Kidney Injury, Surgery Complications, Anesthesia
Intervention	EHR-Embedded AKI Risk Score; EHR-Embedded AKI Risk Score with Best Practice Advisory
Phase	NA
Status	Not_yet_recruiting
Participants	25,518
Est. Completion	15 Oct 2027
Last Updated	22 May 2026

Primary Outcome

Post-operative Change in Creatinine

Eligibility Criteria

Inclusion Criteria

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.

Trial Sites

Countries

United States

View Registry Entry

Related Analysis

Phase IV Cluster-Randomized Trial Tests Machine Learning AKI Risk Prediction in Perioperative Care — What Clinicians Need to Know

A large pragmatic trial at UCSF tests whether machine learning-based AKI risk scores integrated into electronic health records can improve perioperative kidney protection and reduce postoperative acute kidney injury in 25,518 surgical patients.

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