[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100548634":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":19,"centralContacts":36,"locations":44,"responsibleParty":63,"collaborators":65,"id":68,"slug":69,"hasResults":70,"nctId":71,"briefTitle":72,"officialTitle":73,"acronym":10,"eligibilityCriteria":74,"healthyVolunteers":70,"sex":75,"minAge":76,"maxAge":77,"enrollmentInfo":78,"targetDuration":81,"studyType":82,"phases":10,"briefSummary":83,"conditions":84,"keywords":88,"overallStatus":47,"whyStopped":10,"lastUpdateSubmitDate":94,"lastUpdatePostDateStruct":95,"startDateStruct":98,"completionDateStruct":100,"leadSponsor":102,"locationsCount":103},{"fullName":5,"class":6},"Xuanwu Hospital, Beijing","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD)",null,"Delirium (CAM scale ) was assessed 7 days after surgery and divided into POD and non-POD groups; one of the above scenarios indicated postoperative delirium;The patients in the POD group were evaluated for cognitive function at 1 month and 12 months after surgery to determine whether pNCD occurred. The patients in the POD group were further divided into pNCD subgroup and non-PNCD subgroup, and EEG data collection and fMRI scanning were performed",[13],"Other: no intervention",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"no intervention","this is an observation study,no intervention",[9],[20,24,28,30,32,34],{"name":21,"affiliation":22,"role":23},"lei zhao","xuanwu hospital of capital medical university,Beijing","STUDY_CHAIR",{"name":25,"affiliation":26,"role":27},"yong yang","Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","PRINCIPAL_INVESTIGATOR",{"name":29,"affiliation":22,"role":27},"yi an",{"name":31,"affiliation":22,"role":27},"xia li li",{"name":33,"affiliation":22,"role":27},"yang liu",{"name":35,"affiliation":22,"role":27},"yi shu yang",[37,41],{"name":21,"role":38,"phone":39,"phoneExt":10,"email":40},"CONTACT","+8613811035886","zhaoalei@sina.com",{"name":31,"role":38,"phone":42,"phoneExt":10,"email":43},"+86818810616341","935496838@qq.com",[45],{"facility":46,"status":47,"city":48,"state":10,"zip":49,"country":50,"countryCode":51,"cosmosGeoPoint":52,"geoPoint":57,"contacts":58},"Xuanwu Hospital, Capital Medical University","RECRUITING","Beijing","100053","China","CN",{"type":53,"coordinates":54},"Point",[55,56],116.39723,39.9075,{"lat":56,"lon":55},[59,61],{"name":60,"role":38,"phone":39,"phoneExt":10,"email":40},"Lei Zhao, Doctor's",{"name":62,"role":38,"phone":42,"phoneExt":10,"email":43},"Lixia Li, Master's",{"type":64,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[66],{"name":26,"class":67},"UNKNOWN","100548634","risk-warning-model-of-postoperative-delirium-and-long-term-cognitive-dysfunction-in-elderly-patients-100548634",false,"NCT06423547","Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients","Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients Based on Autonomous Evolutionary Neural Network Algorithm","Inclusion Criteria:\n\n* Patients ≥65 years of age who have undergone surgical anesthesia; Sign informed consent\n\nExclusion Criteria:\n\n* Inability to complete cognitive function assessment; Illiteracy, hearing impairment or visual impairment; He has a history of epilepsy, depression, schizophrenia, Alzheimer's disease and other psychiatric and neurological diseases","ALL","65 Years","100 Years",{"count":79,"type":80},10000,"ESTIMATED","1 Year","OBSERVATIONAL","The incidence of postoperative delirium in elderly patients is high, which can lead to long-term postoperative neurocognitive disorders. Its high risk factors are not yet clear. At present, there is a lack of early diagnosis and alarm technology for perioperative neurocognitive disorders, which can not achieve early intervention and effective treatment. By artificial intelligence and autonomously evolutionary neural network algorithm, relying on multi-source clinical big data, we explored the use of Bayesian network to optimize the anesthesia decision-making system in enhanced recovery after surgery, and established risk prediction model for perioperative critical events. It is expected that this method will also help to establish a risk prediction model for postoperative delirium and long-term postoperative neurocognitive disorders. This project plans to collect the perioperative sensitive parameters of anesthesia machine, multi-parameter monitor, EEG monitor,fMRI and HIS system, to explore the evolution process of data characteristics by feature fusion.We also plan to quickly screen key perioperative risk characteristics of postoperative delirium from massive clinical data through feature selection, to explore the high risk factors of long-term postoperative neurocognitive disorders developing from postoperative delirium. Finally, with multi-center intelligent analysis，the risk prediction model of postoperative delirium and long-term postoperative neurocognitive disorders will be constructed.",[85,86,87],"Postoperative Delirium","Postoperative Neurocognitive Disorder","Surgery",[89,90,91,92,93],"postoperative delirium;","postoperative neurocognitive disorder;","risk prediction model;","artificial intelligence;","evolutionary neural network","2025-03-30",{"date":96,"type":97},"2025-04-03","ACTUAL",{"date":99,"type":97},"2024-07-30",{"date":101,"type":80},"2027-12-31",{"name":5,"class":6},1]