[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100637674":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":12,"locations":18,"responsibleParty":35,"collaborators":10,"id":39,"slug":40,"hasResults":41,"nctId":42,"briefTitle":43,"officialTitle":44,"acronym":10,"eligibilityCriteria":45,"healthyVolunteers":41,"sex":46,"minAge":47,"maxAge":10,"enrollmentInfo":48,"targetDuration":10,"studyType":51,"phases":10,"briefSummary":52,"conditions":53,"keywords":55,"overallStatus":20,"whyStopped":10,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":64,"completionDateStruct":65,"leadSponsor":67,"locationsCount":68},{"fullName":5,"class":6},"Peking Union Medical College Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"Elderly Surgical Patients",null,"Patients aged over 65 years who are undergoing elective non-cardiac surgery under general anesthesia with endotracheal intubation. All patients will receive continuous non-invasive hemodynamic monitoring prior to anesthesia induction to calculate baseline BRS parameters.",[13],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},"Quexuan Cui, Dr.","CONTACT","+8613520921711","Cuiqx_garfield@126.com",[19],{"facility":5,"status":20,"city":21,"state":22,"zip":23,"country":22,"countryCode":24,"cosmosGeoPoint":25,"geoPoint":30,"contacts":31},"RECRUITING","Beijing","China","100730","CN",{"type":26,"coordinates":27},"Point",[28,29],116.39723,39.9075,{"lat":29,"lon":28},[32],{"name":33,"role":15,"phone":34,"phoneExt":10,"email":17},"Quexuan Cui","13520921711",{"type":36,"investigatorFullName":37,"investigatorTitle":38,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Huang YuGuang","Professor","100637674","machine-learning-model-based-on-baroreflex-sensitivity-for-predicting-post-induction-hypotension-in-elderly-patients-100637674",false,"NCT07618416","Machine Learning Model Based on Baroreflex Sensitivity for Predicting Post-Induction Hypotension in Elderly Patients","Development of a Baroreflex Sensitivity-Based Multifactorial Machine Learning Model for Predicting Post-Induction Hypotension in Elderly Patients","Inclusion Criteria:\n\n* Aged over 65 years;\n* Scheduled for elective non-cardiac surgery;\n* American Society of Anesthesiologists (ASA) physical status classification I-III;\n* Planned for general anesthesia with endotracheal intubation;\n* Patient and legal guardians are capable of understanding the study protocol and willing to provide written informed consent.\n\nExclusion Criteria:\n\n* Severe peripheral vascular diseases;\n* Secondary hypertension;\n* Presence of physical tremors (e.g., Parkinson's disease) preventing stable recording;\n* Inability to accurately measure upper limb blood pressure;\n* Pre-existing cardiac arrhythmias (e.g., atrial fibrillation) that render BRS;\n* Psychiatric disorders or cognitive impairments hindering basic cooperation.","ALL","65 Years",{"count":49,"type":50},500,"ESTIMATED","OBSERVATIONAL","The purpose of this study is to develop a high-performance machine learning model combining dynamic baroreflex sensitivity (BRS) metrics and multi-dimensional static clinical features to predict the risk of post-induction hypotension (PIH) in elderly patients undergoing elective non-cardiac surgery under general anesthesia.",[54],"Post Induction Hypotension",[56,57,58,59],"post-induction hypotension","elderly patient","baroreflex sensitivity","machine learning","2026-05-25",{"date":62,"type":63},"2026-06-01","ACTUAL",{"date":62,"type":50},{"date":66,"type":50},"2027-12-31",{"name":5,"class":6},1]