[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100643702":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":19,"locations":25,"responsibleParty":41,"collaborators":10,"id":45,"slug":46,"hasResults":47,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":51,"eligibilityCriteria":52,"healthyVolunteers":47,"sex":53,"minAge":54,"maxAge":10,"enrollmentInfo":55,"targetDuration":10,"studyType":58,"phases":10,"briefSummary":59,"conditions":60,"keywords":62,"overallStatus":28,"whyStopped":10,"lastUpdateSubmitDate":69,"lastUpdatePostDateStruct":70,"startDateStruct":73,"completionDateStruct":75,"leadSponsor":77,"locationsCount":78},{"fullName":5,"class":6},"Kutahya Health Sciences University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Adult Intensive Care Unit Patients",null,"Adult patients admitted to the intensive care unit who are monitored during routine clinical care. Routinely collected non-invasive blood pressure, heart rate, medication-dose, and fluid-balance data will be used for machine learning model development and internal validation. No treatment or clinical intervention will be assigned by the study protocol.",[13],"Other: Routine ICU Data Collection",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"Routine ICU Data Collection","Routinely collected intensive care unit data, including non-invasive blood pressure, heart rate, medication-dose records, and fluid-balance data, will be recorded and analyzed for development and internal validation of a machine learning model. The study does not assign any treatment, medication, device, alarm, or clinical decision.",[9],[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Serkan Telli, MD","CONTACT","905437156203","serkan.telli@ksbu.edu.tr",[26],{"facility":27,"status":28,"city":29,"state":29,"zip":30,"country":31,"countryCode":10,"cosmosGeoPoint":32,"geoPoint":37,"contacts":38},"Kutahya City Hospital","RECRUITING","Kütahya","43100","Turkey (Türkiye)",{"type":33,"coordinates":34},"Point",[35,36],29.98333,39.42417,{"lat":36,"lon":35},[39],{"name":21,"role":22,"phone":40,"phoneExt":10,"email":24},"+905437156203",{"type":42,"investigatorFullName":43,"investigatorTitle":44,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Serkan TELLİ","Assistant Professor of Anesthesiology and Reanimation","100643702","early-prediction-of-icu-hypotension-using-machine-learning-100643702",false,"NCT07627607","Early Prediction of ICU Hypotension Using Machine Learning","A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients","ICU-HypoAI","Inclusion Criteria:\n\n* Age 18 years or older\n* Admission to the adult intensive care unit during the study period\n* Length of stay in the intensive care unit of at least 24 hours\n* Availability of routine intensive care unit monitoring data\n* Availability of non-invasive blood pressure and heart rate measurements recorded during ICU monitoring\n* Availability of medication-dose and\u002For fluid-balance records during ICU monitoring\n\nExclusion Criteria:\n\n* Age younger than 18 years\n* Length of stay in the intensive care unit of less than 24 hours\n* Absence of usable blood pressure monitoring data\n* Records with irrecoverable timestamp inconsistencies\n* Insufficient monitoring duration for feature construction and future outcome labeling","ALL","18 Years",{"count":56,"type":57},100,"ESTIMATED","OBSERVATIONAL","This prospective observational study aims to develop and internally validate a machine learning model for the early prediction of hypotension in adult intensive care unit patients. The model will use routinely collected non-invasive vital signs, heart rate, medication-dose records, and fluid-balance data recorded during standard ICU care. No intervention will be assigned by the study, and patient management will not be changed according to the model output. The primary aim is to predict hypotension 30 minutes before its occurrence; shorter 5- and 15-minute prediction horizons will also be evaluated.",[61],"Hypotension",[63,64,65,66,67,68],"Intensive Care Unit","Machine Learning","Artificial Intelligence","Non-Invasive Blood Pressure","Hemodynamic Monitoring","Prediction Model","2026-06-04",{"date":71,"type":72},"2026-06-08","ACTUAL",{"date":74,"type":72},"2026-03-15",{"date":76,"type":57},"2026-07-15",{"name":5,"class":6},1]