Prediction of Neurological Outcome of Children After a Traumatic Brain Injury Based on an Integrated Predictive Model

NCT ID: NCT04157634

Last Updated: 2019-11-08

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 70 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-01-31
• Study Completion Date: 2022-12-31

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.

Conditions

Traumatic Brain Injury

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Prognostication model

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.

Group Type: EXPERIMENTAL

Prognostication model

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

Prognostication model

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

Children 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.

Exclusion Criteria

Children 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.

• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

St. Justine's Hospital

OTHER

Sponsor Role: lead

Responsible Party

Laurence Ducharme-Crevier

Paediatric Intensivist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Other Identifiers

HSJ - 2020-2526

Identifier Type: -

Identifier Source: org_study_id