[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656953":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":17,"studyPopulation":18,"armGroups":13,"interventions":13,"overallOfficials":13,"centralContacts":19,"locations":25,"responsibleParty":41,"collaborators":13,"id":45,"slug":46,"hasResults":47,"nctId":48,"briefTitle":49,"officialTitle":49,"acronym":13,"eligibilityCriteria":50,"healthyVolunteers":47,"sex":51,"minAge":52,"maxAge":13,"enrollmentInfo":53,"targetDuration":13,"studyType":56,"phases":13,"briefSummary":57,"conditions":58,"keywords":60,"overallStatus":63,"whyStopped":13,"lastUpdateSubmitDate":64,"lastUpdatePostDateStruct":65,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":73},{"fullName":5,"class":6},"Ruijin Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"success rate of ventilator weaning","The proportion of participants who successfully achieve liberation from invasive mechanical ventilation (weaning success), expressed as a percentage of all participants who undergo a weaning attempt","28 days",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"COHORT","RETROSPECTIVE","Mechanical ventilation is the cornerstone of life support for critically ill patients with respiratory failure in the ICU. However, adjustments to ventilator settings and decisions regarding weaning remain highly dependent on clinical experience, resulting in substantial inter-clinician variability. A fundamental limitation of current practice is that commonly used single-point metrics-such as the oxygenation index-cannot reliably reflect a patient's true recovery state across different levels of respiratory support. The same oxygenation level may indicate genuine recovery in a patient on low support, or persistent respiratory failure masked by high ventilatory support. This conflation of \"true recovery\" with \"support-dependent stability\" is a major contributor to weaning failure and delayed extubation.\n\nTo address this challenge, this study will leverage electronic health record (EHR) data from ICU patients who received invasive mechanical ventilation at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between 2016 and 2026 (RuiICU-P1 cohort, n = 4,232), to develop a deep learning-based physiological world model of respiratory dynamics (LAVENT). The model is designed to: (1) learn the co-evolutionary dynamics between a patient's physiological state and respiratory support; (2) generate 96-hour multivariate clinical trajectories given the current patient state and candidate ventilator settings (FiO₂, PEEP); and (3) compare the differential clinical outcomes of alternative ventilator adjustment strategies within the same patient state. External validation will be performed using the public MIMIC-IV database (n = 29,899) to assess model transferability across healthcare systems.\n\nThis is a retrospective, observational study using only existing EHR data, without any prospective patient intervention, additional examinations, or biospecimen collection.","Adult ICU patients (≥18 years) who received invasive mechanical ventilation at the Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between January 2016 and June 2026.",[20],{"name":21,"role":22,"phone":23,"phoneExt":13,"email":24},"Jing Xu","CONTACT","+8618217732985","xj40952@rjh.com.cn",[26],{"facility":27,"status":13,"city":28,"state":29,"zip":30,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":39},"Ruijin hospital","Shanghai","Shanghai Municipality","200025","China","CN",{"type":34,"coordinates":35},"Point",[36,37],121.45806,31.22222,{"lat":37,"lon":36},[40],{"name":21,"role":22,"phone":23,"phoneExt":13,"email":24},{"type":42,"investigatorFullName":43,"investigatorTitle":44,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"PRINCIPAL_INVESTIGATOR","Jialin Liu","Chief Physician","100656953","research-on-respiratory-support-decision-making-for-mechanically-ventilated-icu-patients-based-on-a-physiological-world-model-100656953",false,"NCT07834671","Research on Respiratory Support Decision-Making for Mechanically Ventilated ICU Patients Based on a Physiological World Model","Inclusion Criteria:\n\n* Inclusion criteria:\n* age ≥ 18 years\n* received invasive mechanical ventilation during ICU stay\n* had complete 48hour baseline data and 96hour followup data.\n\nExclusion Criteria:\n\n* age \\\u003C 18 years\n* duration of mechanical ventilation \\\u003C 24 hours\n* missing rate \\> 50% for key variables (FiO₂, PEEP, SpO₂, respiratory rate)\n* received ECMO therapy.","ALL","18 Years",{"count":54,"type":55},4232,"ESTIMATED","OBSERVATIONAL","Mechanical ventilation is essential for ICU patients with respiratory failure, yet ventilator adjustment and weaning decisions remain experience-dependent and highly variable. Current single-point metrics such as the oxygenation index cannot distinguish true recovery from support-dependent stability-the same oxygenation level may reflect either. This study uses EHR data from 4,232 invasively ventilated ICU patients at Ruijin Hospital (2016-2026) to develop LAVENT, a deep learning-based physiological world model of respiratory dynamics. LAVENT learns the co-evolution of patient physiology and respiratory support, generates 96-hour multivariate trajectories under candidate FiO₂\u002FPEEP settings, and compares alternative ventilation strategies within the same patient state. External validation uses the MIMIC-IV database (n = 29,899). This retrospective observational study uses only existing EHR data, with no prospective intervention or biospecimen collection.",[59],"Acute Respiratory Failure",[61,62],"ventilator weaning","mechanical ventilation","NOT_YET_RECRUITING","2026-09-16",{"date":66,"type":67},"2026-09-22","ACTUAL",{"date":69,"type":55},"2026-09-14",{"date":71,"type":55},"2029-12-31",{"name":5,"class":6},1]