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

Overview

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.

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.

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