[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100598351":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":15,"locations":21,"responsibleParty":72,"collaborators":76,"id":82,"slug":83,"hasResults":84,"nctId":85,"briefTitle":86,"officialTitle":87,"acronym":10,"eligibilityCriteria":88,"healthyVolunteers":84,"sex":89,"minAge":90,"maxAge":10,"enrollmentInfo":91,"targetDuration":10,"studyType":94,"phases":10,"briefSummary":95,"conditions":96,"keywords":104,"overallStatus":24,"whyStopped":10,"lastUpdateSubmitDate":110,"lastUpdatePostDateStruct":111,"startDateStruct":114,"completionDateStruct":116,"leadSponsor":118,"locationsCount":119},{"fullName":5,"class":6},"Beijing Tsinghua Chang Gung Hospital","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"Multicentre External Validation Cohort",null,"Consecutive eligible adults who underwent major hepatobiliary or pancreatic surgery or liver transplantation at three independent external centres: Peking University International Hospital (WYA), Southwest Hospital of Army Medical University (WYB), and Huangdao District People's Hospital of Qingdao (WYC). The frozen full perioperative M3 model developed at BTCH was applied using the same predictor definitions, imputation rules, model parameters, and classification threshold, without centre-specific model fitting or recalibration. Centre-specific and pooled validation analyses were performed. This was a retrospective observational cohort; no intervention was assigned and routine clinical care was not altered.",{"label":13,"type":10,"description":14,"interventionNames":10},"BTCH Development and Validation Cohort","Consecutive eligible adults who underwent major hepatobiliary or pancreatic surgery or liver transplantation at Beijing Tsinghua Changgung Hospital (BTCH) between October 16, 2015, and December 24, 2025. The earliest 70% formed the development cohort and the most recent 30% formed the temporal-validation cohort. Model development was confined to the development cohort, and the frozen models were evaluated in the temporal-validation cohort. This was a retrospective observational study; no intervention was assigned and routine clinical care was not altered.",[16],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},"Gao Zhifeng, MD","CONTACT","+8615801249466","btchgzf@hotmail.com",[22,36,46,59],{"facility":23,"status":24,"city":25,"state":26,"zip":27,"country":28,"countryCode":29,"cosmosGeoPoint":30,"geoPoint":35,"contacts":10},"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University","RECRUITING","Beijing","Beijing Municipality","102218","China","CN",{"type":31,"coordinates":32},"Point",[33,34],116.39723,39.9075,{"lat":34,"lon":33},{"facility":37,"status":24,"city":25,"state":10,"zip":10,"country":28,"countryCode":29,"cosmosGeoPoint":38,"geoPoint":40,"contacts":41},"Peking University International Hospital",{"type":31,"coordinates":39},[33,34],{"lat":34,"lon":33},[42],{"name":43,"role":18,"phone":44,"phoneExt":10,"email":45},"Lan Yao, Dr.","86+13671010819","yaolan@pkuih.edu.cn",{"facility":47,"status":24,"city":48,"state":10,"zip":10,"country":28,"countryCode":29,"cosmosGeoPoint":49,"geoPoint":53,"contacts":54},"Southwest Hospital, The First Affiliated Hospital of Army Medical University","Chongqing",{"type":31,"coordinates":50},[51,52],106.55771,29.56026,{"lat":52,"lon":51},[55],{"name":56,"role":18,"phone":57,"phoneExt":10,"email":58},"Zhiyu Chen, Dr.","86+18515886827","fixseve@qq.com",{"facility":60,"status":24,"city":61,"state":10,"zip":10,"country":28,"countryCode":29,"cosmosGeoPoint":62,"geoPoint":66,"contacts":67},"Huangdao District People's Hospital of Qingdao","Qingdao",{"type":31,"coordinates":63},[64,65],120.38042,36.06488,{"lat":65,"lon":64},[68],{"name":69,"role":18,"phone":70,"phoneExt":10,"email":71},"Gang Wang, Dr.","+86-13051535765","826387501@qq.com",{"type":73,"investigatorFullName":74,"investigatorTitle":75,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Zhifeng Gao","Chief physician",[77,79,80],{"name":60,"class":78},"UNKNOWN",{"name":37,"class":6},{"name":81,"class":78},"The First Affiliated Hospital of Army Medical University (Southwest Hospital)","100598351","digital-early-warning-system-for-acute-lung-injury-in-liver-surgery-100598351",false,"NCT07070362","Digital Early Warning System for Acute Lung Injury in Liver Surgery","The Construction of a Digital Intelligence Early Warning System for the Whole Process of Acute Lung Injury in Liver Surgery Based on Cardiopulmonary Interaction Characteristics","Inclusion Criteria:\n\n* Age ≥ 18 years\n* Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)\n* Voluntary participation with signed informed consent","ALL","18 Years",{"count":92,"type":93},3000,"ESTIMATED","OBSERVATIONAL","This study aims to develop an explainable machine learning model that takes into account the characteristics of cardiopulmonary interactions. This model will enable early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will create a digital early-warning system for ALI, thereby supporting clinical diagnosis and treatment decisions. This, in turn, should help reduce the incidence and mortality rates associated with ALI.",[97,98,99,100,101,102,103],"Acute Lung Injury(ALI)","Liver Cirrhosis","ARDS, Human","MASLD","MASLD\u002FMASH (Metabolic Dysfunction-Associated Steatotic Liver Disease \u002F Metabolic Dysfunction-Associated Steatohepatitis)","NAFLD (Nonalcoholic Fatty Liver Disease)","Liver Cancer, Adult",[105,106,107,108,109],"Digital Intelligence","Acute Lung Injury","PPCs","Liver Surgery","Cardiopulmonary Interaction;","2026-08-13",{"date":112,"type":113},"2026-08-17","ACTUAL",{"date":115,"type":113},"2024-11-01",{"date":117,"type":93},"2027-11-30",{"name":5,"class":6},4]