[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100515219":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":27,"locations":32,"responsibleParty":77,"collaborators":79,"id":84,"slug":85,"hasResults":86,"nctId":87,"briefTitle":88,"officialTitle":88,"acronym":10,"eligibilityCriteria":89,"healthyVolunteers":86,"sex":90,"minAge":91,"maxAge":10,"enrollmentInfo":92,"targetDuration":10,"studyType":95,"phases":10,"briefSummary":96,"conditions":97,"keywords":100,"overallStatus":35,"whyStopped":10,"lastUpdateSubmitDate":105,"lastUpdatePostDateStruct":106,"startDateStruct":109,"completionDateStruct":111,"leadSponsor":113,"locationsCount":114},{"fullName":5,"class":6},"University of Chicago","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Study cohort",null,"Patients will be identified as high risk based on their AKI risk score (ESTOP- AKI 2.0) being in the top 10% of all hospitalized patients",[13],"Device: ESTOP - AKI 2.0",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DEVICE","ESTOP - AKI 2.0","Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject\u002Fclinical care.",[9],[21,24],{"name":22,"affiliation":5,"role":23},"Jay Koyner, MD","PRINCIPAL_INVESTIGATOR",{"name":25,"affiliation":26,"role":23},"Matthew Churpek, MD,MPH,PhD","University of Wisconsin, Madison",[28],{"name":22,"role":29,"phone":30,"phoneExt":10,"email":31},"CONTACT","773-702-4842","jkoyner@uchicago.edu",[33,57],{"facility":34,"status":35,"city":36,"state":37,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":41,"geoPoint":46,"contacts":47},"University of Chicago Medical Center","RECRUITING","Chicago","Illinois","60637","United States","US",{"type":42,"coordinates":43},"Point",[44,45],-87.65005,41.85003,{"lat":45,"lon":44},[48,52,56],{"name":49,"role":29,"phone":50,"phoneExt":10,"email":51},"Aiman Fatima, MBBS","773-702-6201","aimanfatima@uchicagomedicine.org",{"name":53,"role":29,"phone":54,"phoneExt":10,"email":55},"Ola Anjorin, MBBS,DA,MPH","773-704-3168","oanjorin@bsd.uchicago.edu",{"name":22,"role":23,"phone":10,"phoneExt":10,"email":10},{"facility":58,"status":35,"city":59,"state":60,"zip":61,"country":39,"countryCode":40,"cosmosGeoPoint":62,"geoPoint":66,"contacts":67},"University of Wisconsin Hospital","Madison","Wisconsin","53792",{"type":42,"coordinates":63},[64,65],-89.40123,43.07305,{"lat":65,"lon":64},[68,72,76],{"name":69,"role":29,"phone":70,"phoneExt":10,"email":71},"Madeline Ogus, MS","608-265-2878","mkoguss@medicine.wisc.edu",{"name":73,"role":29,"phone":74,"phoneExt":10,"email":75},"Michael Weber","608-263-3369","mjweber@medicine.wisc.edu",{"name":25,"role":23,"phone":10,"phoneExt":10,"email":10},{"type":78,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[80,83],{"name":81,"class":82},"National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)","NIH",{"name":26,"class":6},"100515219","combining-biomarkers-and-electronic-risk-scores-to-predict-aki-in-hospitalized-patients-100515219",false,"NCT05988658","Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients","Inclusion Criteria:\n\n1. Age ≥ 18 years\n2. E-STOP AKI 2.0 score in the top 10% of risk (historically from all hospitalized patients) within the last 12 hours. (First time across this 10% risk threshold during this hospital stay).\n3. Admitted to an inpatient ward, intermediate, or ICU care at the University of Chicago Medical Center (UCMC) or University of Wisconsin Health (UWHealth). (No Emergency Department patients)\n4. Patient or their legally authorized representative must be able to read, speak, and understand English, for the purposes of consenting. Otherwise, inclusion in this protocol will be done without regard to race, ethnic origin or gender\n\nExclusion Criteria:\n\n1. Voluntary refusal or missing written consent of the patient \u002F legal representative.\n2. Patients with a known history of end-stage renal disease on dialysis (including renal transplantation).\n3. Patients without a measured serum creatinine value during their inpatient stay.\n4. Patients with a creatinine \\>4.0 mg\u002Fdl at the time of admission or available in the EHR from the last 6 months\n5. Patients with prior episode of KDIGO defined AKI during this same hospitalization- regardless of E-STOP AKI 2.0 score\n6. Patients with prior renal consultation during their admission.\n7. Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago during this same hospital stay.\n8. Incarcerated patients\n9. Pregnant patients","ALL","18 Years",{"count":93,"type":94},800,"ESTIMATED","OBSERVATIONAL","The study's objective is to evaluate the additive value of renal biomarkers (from blood and urine) for identifying individuals at high risk for severe acute kidney injury (AKI) above that of a novel natural language processing (NLP)-based AKI risk algorithm. The risk algorithm is based on electronic health records (EHR) data (labs, vitals, clinical notes, and test reports). Patients will enroll at the University of Chicago Medical Center and the University of Wisconsin Hospital, where the risk score will run in real time. The risk score will identify those patients with the highest risk for the future development of Stage 2 AKI and collect blood and urine for biomarker measurement over the subsequent 3 days.",[98,99],"Acute Kidney Injury","Biomarkers",[98,99,101,102,103,104],"Renal Replacement Therapy","Artificial Intelligence","Risk Assessment","Clinical Nephrology","2025-09-05",{"date":107,"type":108},"2025-09-12","ACTUAL",{"date":110,"type":108},"2024-01-05",{"date":112,"type":94},"2028-03-01",{"name":5,"class":6},2]