Ignite Creation Date:
2025-12-25 @ 2:22 AM
Ignite Modification Date:
2025-12-27 @ 1:19 PM
Study NCT ID:
NCT04157634
Status:
UNKNOWN
Last Update Posted:
2019-11-08
First Post:
2019-11-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Prediction of Neurological Outcome of Children After a Traumatic Brain Injury Based on an Integrated Predictive Model
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000070642', 'term': 'Brain Injuries, Traumatic'}], 'ancestors': [{'id': 'D001930', 'term': 'Brain Injuries'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D006259', 'term': 'Craniocerebral Trauma'}, {'id': 'D020196', 'term': 'Trauma, Nervous System'}, {'id': 'D014947', 'term': 'Wounds and Injuries'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'OTHER', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': 'Prospective cohort study'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 70}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2020-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2019-11', 'completionDateStruct': {'date': '2022-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2019-11-07', 'studyFirstSubmitDate': '2019-11-06', 'studyFirstSubmitQcDate': '2019-11-07', 'lastUpdatePostDateStruct': {'date': '2019-11-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-11-08', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Association with poor neurological outcome', 'timeFrame': 'We will assess neurocognitive function of patients at 6 ±2 months following the discharge from the Pediatric Intensive Care Unit', 'description': 'A poor neurological outcome will be defined as on death or neurocognitive dysfunction in survivors'}], 'secondaryOutcomes': [{'measure': 'Adverse events', 'timeFrame': 'In the 72 hours following TBI', 'description': 'Adverse events will be defined as increased intracranial pressure, decreased cerebral perfusion pressure, seizure or cardiac arrest'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['outcome', 'paediatric critical care'], 'conditions': ['Traumatic Brain Injury']}, 'descriptionModule': {'briefSummary': 'This study aims to develop a integrated predictive model based on serum biomarkers, HRV, and an innovative computerized classifier output, to predict the patient long term neurological outcome after a moderate or severe TBI in children.', 'detailedDescription': "Traumatic brain injury (TBI) is a major cause of morbidity and mortality in children. Most children with moderate and severe TBI have long term sequelae including neurological deficit, cognitive impairment and behavioural disorders. In the acute care setting, neither clinicians nor researchers are able to adequately predict the long term outcome of children with TBI, consequently limiting their ability to tailor medical care, rehabilitation and support services. Improving our understanding of a TBI patient's exact cerebral status and prognosis is a critical step toward optimized and personalized patient management. In this research study, an innovative and integrated model will be developed to improve the prognostication in the early phase of a TBI. This model will combine key clinical variables commonly collected in the acute care setting and combine these with cutting-edge empirical measures: 1) biomarkers; 2) a new physiological monitoring based on heart-rate variability (HRV) to assess the integrity of the autonomic system; and 3) a computerized classification tool developed using the concept of artificial intelligence to continuously categorize the patient's cerebral status."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT'], 'maximumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nChildren 1) \\<18 years; 2) admitted to the Paediatric Intensive Care Unit at the Centre Hospitalier Universitaire Ste-Justine; 3) moderate (Glasgow Coma Scale (GCS): 9-12) or severe TBI (GCS ≤8), assessed after initial resuscitation; 4) admitted to the Paediatric Intensive Care Unit less than 24 hours after the initial TBI and 5) written parental consent. Patients with either inflicted or accidental TBI will be included as they characterize different but important population, our model should be valuable for both.\n\nExclusion Criteria:\n\nChildren 1) suspected of being brain death at the time of Paediatric Intensive Care Unit entry (GCS 3 and loss of all brain stem reflexes); 2) with a pacemaker (HRV monitoring unreliable); and/or 3) patients or parents who do not speak or read English or French.'}, 'identificationModule': {'nctId': 'NCT04157634', 'briefTitle': 'Prediction of Neurological Outcome of Children After a Traumatic Brain Injury Based on an Integrated Predictive Model', 'organization': {'class': 'OTHER', 'fullName': "St. Justine's Hospital"}, 'officialTitle': 'Prediction of Neurological Outcome of Children After a Moderate or Severe Traumatic Brain Injury, Based on an Integrated Predictive Model (Serum Biomarkers, Heart Rate Variability, Computerized Classifier Output)', 'orgStudyIdInfo': {'id': 'HSJ - 2020-2526'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Prognostication model', 'description': 'In a prospective cohort of children hospitalized in a PICU, development of a model based on biomarkers, HRV, and a computerized classifier output, to predict long-term neurological outcome after a moderate or severe TBI in children aged 0 to 18 years.', 'interventionNames': ['Diagnostic Test: Prognostication model']}], 'interventions': [{'name': 'Prognostication model', 'type': 'DIAGNOSTIC_TEST', 'description': 'In a prospective cohort of children hospitalized in a PICU, developement a model based on biomarkers, HRV, and a computerized classifier output, to predict long-term neurological outcome after a moderate or severe TBI in children aged 0 to 18 years.', 'armGroupLabels': ['Prognostication model']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "St. Justine's Hospital", 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Paediatric Intensivist', 'investigatorFullName': 'Laurence Ducharme-Crevier', 'investigatorAffiliation': "St. Justine's Hospital"}}}}