Ignite Creation Date:
2025-12-24 @ 4:37 PM
Ignite Modification Date:
2025-12-25 @ 2:27 PM
Study NCT ID:
NCT07210866
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-10-07
First Post:
2025-09-30
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
An Artificial Intelligence Model for Intensive Care Length of Stay, Neurological Outcome and Costs Estimation After Cardiopulmonary Resuscitation: a Cohort Study.
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2025-09-29', 'size': 382703, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2025-09-29T05:28', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 5000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-10-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-30', 'studyFirstSubmitDate': '2025-09-30', 'studyFirstSubmitQcDate': '2025-09-30', 'lastUpdatePostDateStruct': {'date': '2025-10-07', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-10-07', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Machine Learning Python programme', 'timeFrame': '3 months', 'description': 'The created database will be analyzed using a machine learning artificial intelligence algorithm with the Python programming language. After processing missing and incomplete data by artificial intelligence, the database will be divided into two parts: model training and model validation. Meaningful data will be selected through model training, and a prediction model will be built based on these data. To increase the interpretability of the prediction model and help users understand how and why certain predictions are made, the SHapley Additive exPlanations (SHAP) algorithm will be used. In machine learning, the SHAP technique is used to interpret the decision-making processes of complex machine learning models.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['machine learning', 'cardiopulmonary rescucitation', 'lenght of stay', 'cost analysis', 'neurological outcome'], 'conditions': ['Cost Analysis', 'Length of ICU Stay', 'Neurological Outcome']}, 'descriptionModule': {'briefSummary': 'The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. The goal is to determine length of stay in reanimation, neurological clinical outcome and costs of these patients at discharge from the department. All these data is intended to be evaluated by artificial intelligence to evaluate a predictive model.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients above 18 years after successful cardiopulmonary resuscitation with ROSC registered at Bezmialem Vakıf University Hospital Intensive care unit from October 2010 to September 2025 will be included in the resesarch.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* age\\>18 years\n* successive cardiopulmonary resuscitation\n* at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)\n\nExclusion Criteria:\n\n* age \\< 18 years\n* \\>80% missing data in patient records\n* patients with no ROSC'}, 'identificationModule': {'nctId': 'NCT07210866', 'briefTitle': 'An Artificial Intelligence Model for Intensive Care Length of Stay, Neurological Outcome and Costs Estimation After Cardiopulmonary Resuscitation: a Cohort Study.', 'organization': {'class': 'OTHER', 'fullName': 'Bezmialem Vakif University'}, 'officialTitle': 'AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY', 'orgStudyIdInfo': {'id': 'Nkangarli002'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation', 'interventionNames': ['Other: no physical or medical interventions']}], 'interventions': [{'name': 'no physical or medical interventions', 'type': 'OTHER', 'description': 'Data from patients after successive rescucitaion will be evaluated by machine learning programs.', 'armGroupLabels': ['Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Bezmialem Vakif University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Uzman Doctor', 'investigatorFullName': 'Nıgar Kangarlı', 'investigatorAffiliation': 'Bezmialem Vakif University'}}}}