TY  - JOUR
PY  - 2021//
TI  - Magnetic resonance imaging findings are associated with long-term global neurological function or death following traumatic brain injury in critically ill children
JO  - Journal of neurotrauma
A1  - McInnis, Carter
A1  - Solana Garcia, María José
A1  - Widjaja, Elysa
A1  - Frndova, Helena
A1  - Van Huyse, Judith
A1  - Guerguerian, Anne-Marie
A1  - Oyefiade, Adeoye
A1  - Laughlin, Suzanne
A1  - Raybaud, Charles
A1  - Miller, Elka
A1  - Tay, Keng
A1  - Bigler, Erin D. PhD
A1  - Dennis, Maureen
A1  - Fraser, Douglas
A1  - Campbell, Craig
A1  - Choong, Karen
A1  - Danani, Sonny
A1  - Lacroix, Jacques
A1  - Farrell, Catherine
A1  - Beauchamp, Miriam Helen
A1  - Schachar, Russell
A1  - Hutchison, James S.
A1  - Wheeler, Anne
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - The identification of children with traumatic brain injury (TBI) who are at risk of death or poor global neurological functional outcome remains a challenge. Magnetic resonance imaging (MRI) can detect several brain pathologies that are a result of TBI, however, the types and locations of pathology that are the most predictive remain to be determined. Forty-two critically ill children with TBI were prospectively recruited from pediatric intensive care units at five Canadian children's hospitals. Pathologies detected on subacute phase MRIs including cerebral hematoma, herniation, cerebral laceration, cerebral edema, midline shift, and the presence and location of cerebral contusion or diffuse axonal injury (DAI) in 28 regions of interest were assessed. Global functional outcome or death more than 12-months post-injury was assessed using the Pediatric Cerebral Performance Category score. Linear modeling was employed to evaluate the utility of an MRI composite score for predicting long term global neurological function or death after injury, and non-linear Random Forest modeling was used to identify which MRI features have the most predictive utility. A linear predictive model of favorable versus unfavorable long-term outcomes was significantly improved when an MRI composite score was added to clinical variables. Non-linear Random Forest modeling identified five MRI variables as stable predictors of poor outcomes: presence of herniation, DAI in the parietal lobe, DAI in the subcortical white matter, DAI in the posterior corpus callosum, and cerebral contusion in the anterior temporal lobe. Clinical MRI has prognostic value to identify children with TBI at risk of long-term unfavorable outcomes.<p />  <p>Language: en</p>
LA  - en
SN  - 0897-7151
UR  - http://dx.doi.org/10.1089/neu.2020.7514
ID  - ref1
ER  -