Ignite Creation Date:
2026-03-26 @ 3:14 PM
Ignite Modification Date:
2026-03-30 @ 8:57 PM
Study NCT ID:
NCT07445061
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-03
First Post:
2026-02-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning Prediction of Mortality After Prone Positioning in ARDS
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D012128', 'term': 'Respiratory Distress Syndrome'}], 'ancestors': [{'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D012120', 'term': 'Respiration Disorders'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 377}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-01', 'studyFirstSubmitDate': '2026-02-24', 'studyFirstSubmitQcDate': '2026-03-01', 'lastUpdatePostDateStruct': {'date': '2026-03-03', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-03', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-04-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'ARDS subphenotype classification based on machine learning model.', 'timeFrame': 'Baseline (at initiation of prone position ventilation).', 'description': 'Number of patients classified into different ARDS subphenotypes using a machine learning model based on clinical and physiological variables collected at baseline.'}], 'primaryOutcomes': [{'measure': 'ICU Mortality', 'timeFrame': 'Up to 90 days.', 'description': 'Death from any cause during the intensive care unit (ICU) stay among patients with acute respiratory distress syndrome receiving prone position ventilation.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Acute Respiratory Distress Syndrome (ARDS)', 'Prone Position Ventilation', 'Machine Learning', 'ICU', 'ARDS']}, 'descriptionModule': {'briefSummary': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adult patients diagnosed with acute respiratory distress syndrome (ARDS) who received prone position ventilation in the intensive care unit at Zhongshan Hospital, Fudan University. Clinical data were collected retrospectively from electronic medical records.', 'healthyVolunteers': False, 'eligibilityCriteria': '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 \\<18 years;\n* PPV duration \\<6 hours;\n* ICU length of stay \\<24 hours;\n* Pregnancy;\n* Missing key clinical data.'}, 'identificationModule': {'nctId': 'NCT07445061', 'briefTitle': 'Machine Learning Prediction of Mortality After Prone Positioning in ARDS', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Zhongshan Hospital'}, 'officialTitle': 'A Machine Learning Model to Predict Mortality in Patients With Acute Respiratory Distress Syndrome After Prone Positioning', 'orgStudyIdInfo': {'id': 'B2026-019'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'ARDS Patients Receiving Prone Position Ventilation', 'description': '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.', 'interventionNames': ['Other: Prone Position Ventilation']}], 'interventions': [{'name': 'Prone Position Ventilation', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['ARDS Patients Receiving Prone Position Ventilation']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual participant data will not be publicly shared due to institutional and ethical restrictions.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Zhongshan Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}