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:
St. Justine's Hospital
Organization:
Study Overview
Official Title:
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)
Status:
UNKNOWN
Status Verified Date:
2019-11
Last Known Status:
NOT_YET_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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.
Detailed Description:
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.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: