Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-31 @ 8:46 AM
Study NCT ID:
NCT07337356
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-01-13
First Post:
2026-01-04
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Research on the Development and Validation of an Early Prediction Model for Delirium
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003693', 'term': 'Delirium'}], 'ancestors': [{'id': 'D003221', 'term': 'Confusion'}, {'id': 'D019954', 'term': 'Neurobehavioral Manifestations'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D019965', 'term': 'Neurocognitive Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 795}, 'targetDuration': '7 Days', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-02-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2027-02-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-04', 'studyFirstSubmitDate': '2026-01-04', 'studyFirstSubmitQcDate': '2026-01-04', 'lastUpdatePostDateStruct': {'date': '2026-01-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number of participants with delirium as assessed by DSM-5', 'timeFrame': '7th day after ICU admission', 'description': 'Zero is equivalent to no delirium and a high score means a higher occurrence of delirium'}], 'secondaryOutcomes': [{'measure': 'Accuracy', 'timeFrame': '7th day after ICU admission', 'description': 'Zero is equivalent to the minimum accuracy, while a value of 1 represents perfect accuracy'}, {'measure': 'Precision', 'timeFrame': '7th day after ICU admission', 'description': 'The proportion of truly positive samples among those predicted as positive; the closer the score is to 1, the higher the precision'}, {'measure': 'Recall', 'timeFrame': '7th day after ICU admission', 'description': 'The proportion of truly positive samples that are correctly predicted; the closer the score is to 1, the higher the diagnostic sensitivity'}, {'measure': 'F1-score', 'timeFrame': '7th day after ICU admission', 'description': 'The harmonic mean of precision and recall; the higher the score, the better the diagnostic performance of the model'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Delirium', 'Prediction Models', 'Machine Learning']}, 'descriptionModule': {'briefSummary': 'Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability. This study employs non-contact identification technology based on machine vision analysis to quantitatively analyze characteristic biological feature data such as micro-expressions. It then investigates the correlation between these features and delirium subtypes. By integrating clinical phenotypic data and using machine learning algorithms, a multi-modal early prediction model for delirium is constructed to meet the clinical need for early warning of delirium subtypes and enhance the efficacy of delirium identification.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adult patients (≥18 years old) admitted to the ICU of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, including elderly patients, those post-surgery, with sepsis, and trauma, which are common high-risk populations for delirium.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;\n\nExclusion Criteria:\n\n* Patients with severe facial trauma/deformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).'}, 'identificationModule': {'nctId': 'NCT07337356', 'briefTitle': 'Research on the Development and Validation of an Early Prediction Model for Delirium', 'organization': {'class': 'OTHER', 'fullName': 'Ruijin Hospital'}, 'officialTitle': 'Research on the Development and Validation of an Early Prediction Model for Delirium Based on Machine Vision Analysis', 'orgStudyIdInfo': {'id': 'ZWQ21886-2025-EC-579'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'observational', 'description': '1. Meets the delirium diagnostic criteria specified in the Diagnostic and Statistical Manual of Mental Disorders (5th Edition) (DSM-5), which requires the concurrent presence of: ① disturbance in awareness (reduced clarity of awareness of the environment) ; ② change in cognition (e.g., memory impairment, disorientation);\n2. Undergoes consecutive daily assessments for 7 days using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) at three time points (8:00, 14:00, 20:00) with an interval of ≥ 6 hours between each assessment, with at least two positive results;'}, {'label': 'control', 'description': 'Admitted to the ICU during the same period, with negative results on consecutive daily CAM-ICU assessments for 3 days (three assessments per day as the observational group).'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'weiqing Zhang Ph.D, Ph.D', 'role': 'CONTACT', 'email': 'weiq.zh@163.com', 'phone': '8618521525300'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Ruijin Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}