[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100597673":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":19,"locations":25,"responsibleParty":41,"collaborators":10,"id":44,"slug":45,"hasResults":46,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":10,"eligibilityCriteria":50,"healthyVolunteers":46,"sex":51,"minAge":52,"maxAge":53,"enrollmentInfo":54,"targetDuration":10,"studyType":57,"phases":10,"briefSummary":58,"conditions":59,"keywords":61,"overallStatus":27,"whyStopped":10,"lastUpdateSubmitDate":66,"lastUpdatePostDateStruct":67,"startDateStruct":70,"completionDateStruct":72,"leadSponsor":74,"locationsCount":75},{"fullName":5,"class":6},"Samsung Medical Center","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"capnography-cardiac output cohort",null,"Adult patients who underwent surgery under general anesthesia with capnography and invasive arterial blood pressure monitor",[13],"Other: No Intervention: Observational Cohort",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"No Intervention: Observational Cohort","No intervention",[9],[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Heejoon Jeong, MD","CONTACT","+82-2-3410-0841","heejoonjeong@skku.edu",[26],{"facility":5,"status":27,"city":28,"state":10,"zip":29,"country":30,"countryCode":10,"cosmosGeoPoint":31,"geoPoint":36,"contacts":37},"RECRUITING","Seoul","06351","South Korea",{"type":32,"coordinates":33},"Point",[34,35],126.9784,37.566,{"lat":35,"lon":34},[38,39],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},{"name":21,"role":40,"phone":10,"phoneExt":10,"email":10},"PRINCIPAL_INVESTIGATOR",{"type":40,"investigatorFullName":42,"investigatorTitle":43,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Heejoon Jeong","Clinical Assistant Professor","100597673","algorithm-predicting-intraoperative-changes-in-cardiac-output-using-capnography-100597673",false,"NCT07061548","Algorithm Predicting Intraoperative Changes in Cardiac Output Using Capnography","Development of an Artificial Intelligence Model for Predicting Intraoperative Changes in Cardiac Output Using Capnography During General Anesthesia","Inclusion Criteria:\n\n* Elective surgery under general anesthesia\n* Adult patients (18 \\\u003C age \\\u003C 76)\n* Patients who were monitored invasive arterial blood pressure (waveform) and capnography (numeric)\n\nExclusion Criteria:\n\n* Emergency surgery\n* Cardiovascular and thoracic surgery\n* Known Asthma and Chronic obstructive pulmonary disease (COPD)\n* Preoperative pulmonary function test (PFT) abnormality over moderate grade\n* Intraoperative monitoring duration less than 30 minutes","ALL","19 Years","75 Years",{"count":55,"type":56},2005,"ESTIMATED","OBSERVATIONAL","Conventional monitoring of cardiac output requires an invasive procedure and an additional device, which can lead to increased risk and cost. Investigators developed an artificial intelligence algorithm to predict intraoperative changes in cardiac output using capnography in patients undergoing surgery under general anesthesia.",[60],"General Anesthesia Using Endotracheal Intubation",[62,63,64,65],"artificial intelligence","general anesthesia","capnography","cardiac output","2025-07-15",{"date":68,"type":69},"2025-07-18","ACTUAL",{"date":71,"type":69},"2025-07-03",{"date":73,"type":56},"2025-12-31",{"name":5,"class":6},1]