[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100639499":3},{"organization":4,"armGroups":7,"interventions":24,"overallOfficials":37,"centralContacts":41,"locations":46,"responsibleParty":59,"collaborators":61,"id":65,"slug":66,"hasResults":67,"nctId":68,"briefTitle":69,"officialTitle":70,"acronym":71,"eligibilityCriteria":72,"healthyVolunteers":73,"sex":74,"minAge":75,"maxAge":12,"enrollmentInfo":76,"targetDuration":12,"studyType":79,"phases":80,"briefSummary":82,"conditions":83,"keywords":87,"overallStatus":92,"whyStopped":12,"lastUpdateSubmitDate":93,"lastUpdatePostDateStruct":94,"startDateStruct":97,"completionDateStruct":99,"leadSponsor":101,"locationsCount":102},{"fullName":5,"class":6},"University of California, San Francisco","OTHER",[8,13,19],{"label":9,"type":10,"description":11,"interventionNames":12},"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.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"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.",[18],"Device: EHR-Embedded AKI Risk Score",{"label":20,"type":15,"description":21,"interventionNames":22},"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.",[23],"Device: EHR-Embedded AKI Risk Score with Best Practice Advisory",[25,32],{"type":26,"name":27,"description":28,"armGroupLabels":29,"otherNames":30},"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.",[14],[31],"EHR-Embedded AKI Clinical Decision Support Tool",{"type":26,"name":33,"description":34,"armGroupLabels":35,"otherNames":36},"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.",[20],[31],[38],{"name":39,"affiliation":5,"role":40},"Andrew Bishara, MD","PRINCIPAL_INVESTIGATOR",[42],{"name":39,"role":43,"phone":44,"phoneExt":12,"email":45},"CONTACT","415-502-5880","andrew.bishara@ucsf.edu",[47],{"facility":5,"status":12,"city":48,"state":49,"zip":50,"country":51,"countryCode":52,"cosmosGeoPoint":53,"geoPoint":58,"contacts":12},"San Francisco","California","94158","United States","US",{"type":54,"coordinates":55},"Point",[56,57],-122.41942,37.77493,{"lat":57,"lon":56},{"type":60,"investigatorFullName":12,"investigatorTitle":12,"investigatorAffiliation":12,"oldNameTitle":12,"oldOrganization":12},"SPONSOR",[62],{"name":63,"class":64},"National Institute of General Medical Sciences (NIGMS)","NIH","100639499","real-time-acute-kidney-injury-perioperative-prediction-clinical-trial-100639499",false,"NCT07604662","Real-Time Acute Kidney Injury Perioperative Prediction Clinical Trial","Prediction of Acute Kidney Injury (AKI) After Surgery: A Pragmatic Three-Arm Cluster-Randomized Trial","ML-AKI","Inclusion Criteria:\n\n* Adults ≥18 years undergoing non-obstetric surgery at UCSF.\n* Inpatient cases with expected overnight stay.\n* Baseline eGFR ≥15 mL\u002Fmin\u002F1.73 m².\n* Managed by an attending anesthesiologist randomized to one of three arms (CRNAs\u002Fresidents follow attending).\n* Data available in the UCSF EHR for risk scoring and outcomes.\n\nExclusion Criteria:\n\n* Obstetric procedures.\n* Chronic dialysis patients.\n* Kidney transplant recipients.\n* Cases without baseline creatinine\u002FeGFR or missing essential EHR elements needed for scoring\u002Foutcomes (operational exclusions).\n* Outpatient procedures without expected overnight stay.",true,"ALL","18 Years",{"count":77,"type":78},25518,"ESTIMATED","INTERVENTIONAL",[81],"NA","This investigator-initiated, pragmatic trial evaluates whether displaying a machine learning (ML)- derived perioperative AKI risk score-alone or paired with an interruptive Best\u002FOur Practice Advisory (BPA\u002FOPA)-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\u002Fresidents follow their attending' s assignment. Adult inpatients (age ≥18) with expected overnight stay and eGFR ≥15 mL\u002Fmin\u002F1.73 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\u002FdL) 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.",[84,85,86],"Acute Kidney Injury","Surgery Complications","Anesthesia",[84,88,89,90,91],"Surgical Outcomes","Machine Learning","Clinical Decision Support","Electronic Health Records","NOT_YET_RECRUITING","2026-05-19",{"date":95,"type":96},"2026-05-22","ACTUAL",{"date":98,"type":78},"2026-10-15",{"date":100,"type":78},"2027-12-15",{"name":5,"class":6},1]