[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100627163":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":10,"locations":10,"responsibleParty":19,"collaborators":10,"id":21,"slug":22,"hasResults":23,"nctId":24,"briefTitle":25,"officialTitle":26,"acronym":10,"eligibilityCriteria":27,"healthyVolunteers":23,"sex":28,"minAge":29,"maxAge":10,"enrollmentInfo":30,"targetDuration":10,"studyType":33,"phases":10,"briefSummary":34,"conditions":35,"keywords":10,"overallStatus":40,"whyStopped":10,"lastUpdateSubmitDate":41,"lastUpdatePostDateStruct":42,"startDateStruct":45,"completionDateStruct":46,"leadSponsor":48,"locationsCount":10},{"fullName":5,"class":6},"Shanghai Zhongshan Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"ARDS Patients Receiving Prone Position Ventilation",null,"Adult patients diagnosed with acute respiratory distress syndrome (ARDS) who received prone position ventilation during intensive care unit (ICU) admission. Clinical data from electronic medical records will be collected retrospectively for the development and validation of machine learning models to predict ICU mortality.",[13],"Other: Prone Position Ventilation",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"Prone Position Ventilation","Prone position ventilation applied as part of routine clinical care for patients with acute respiratory distress syndrome. No experimental intervention was assigned in this observational study.",[9],{"type":20,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100627163","machine-learning-prediction-of-mortality-after-prone-positioning-in-ards-100627163",false,"NCT07445061","Machine Learning Prediction of Mortality After Prone Positioning in ARDS","A Machine Learning Model to Predict Mortality in Patients With Acute Respiratory Distress Syndrome After Prone Positioning","Inclusion Criteria:\n\n* Diagnosis of ARDS according to the Berlin definition \\[15\\];\n* Receipt of at least one session of prone position ventilation (PPV) during hospitalization;\n* Requirement for mechanical ventilation.\n\nExclusion Criteria:\n\n* Age \\\u003C18 years;\n* PPV duration \\\u003C6 hours;\n* ICU length of stay \\\u003C24 hours;\n* Pregnancy;\n* Missing key clinical data.","ALL","18 Years",{"count":31,"type":32},377,"ESTIMATED","OBSERVATIONAL","Acute respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Prone position ventilation (PPV) is an evidence-based therapy that improves oxygenation and survival in patients with moderate to severe ARDS; however, outcomes remain heterogeneous. Early identification of patients at high risk of mortality after PPV may improve clinical decision-making and individualized management.\n\nThis retrospective observational study aims to develop and validate a machine learning model to predict intensive care unit (ICU) mortality in ARDS patients receiving prone position ventilation. Clinical, laboratory, and treatment variables collected from ICU electronic medical records will be used to construct prediction models using multiple machine learning algorithms. The performance of these models will be evaluated and compared to identify the optimal model for mortality prediction.",[36,16,37,38,39],"Acute Respiratory Distress Syndrome (ARDS)","Machine Learning","ICU","ARDS","NOT_YET_RECRUITING","2026-03-01",{"date":43,"type":44},"2026-03-03","ACTUAL",{"date":41,"type":32},{"date":47,"type":32},"2026-05-01",{"name":5,"class":6}]