School Function In Children After Traumatic Brain Injury: Developing A New Outcome Measure

Trauma remains the leading cause of morbidity and mortality for children. Traumatic brain injury (TBI) is responsible for large population-level costs, and protracted burden on family and caregivers. TBI can significantly impact a child's ability to learn and attain important educational and social milestones. Coordinated reintegration into the school environment is paramount in order to achieve optimal outcome. To date, no validated instrument exists for teachers to be able assess the function of children in school after suffering TBI. By using Food and Drug Administration guidelines on Patient Reported Outcomes (PRO), and National Institutes of Health guidelines on mixed methods research, this thesis aims to develop a validated measure of school function in children after TBI, as assessed by the educational professional most closely involved with that child's education. There are multiple intended uses of this instrument. It will serve as a means for teachers to assess the function of their injured students in the classroom. It will also serve as a vehicle to provide tailored rehabilitation services to injured students. Clinicians will be able to assess the recovery of their patients who have returned to school using this outcome measure. Finally, it will serve as a validated outcome measure for clinical trials in pediatric TBI. This work was carried out in three phases. First, qualitative research methodology was used to develop a measurement concept of school function after TBI. School function was defined as the observable traits and behavioural manifestations of multiple cognitive, psychosocial, and neurologic processes, as well as performance on in-classroom academic tasks that represent a child's ability to achieve expected academic and social milestones. The qualitative data informed the second phase of instrument development, in which items were generated and reduced to form a 95-item prototype questionnaire. In the third phase, field testing was performed in order to validate the concept of school function and further reduce items. Only 58 questionnaires were completed; much further work is necessary to achieve the goal of generating a valid and reliable instrument. When complete, it will fill a large gap in the outcome assessment of this vulnerable population.Ph.D

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries

Background Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. Methods We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004–2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. Results Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as “high risk” for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%–96.6%) but improved specificity (67.4%–81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. Conclusions The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population

Measures of Intracranial Injury Size Do Not Improve Clinical Decision Making for Children With Mild Traumatic Brain Injuries and Intracranial Injuries

BackgroundWhen evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown.ObjectiveTo determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs.MethodsWe included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation >24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (<1%, <3%, and <5% predicted risk).ResultsThe GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, but the GLM model had higher sensitivity (89%-96% for GLM; 89% for RP). By comparison, the KIIDS-TBI model had slightly higher sensitivity (93%-100%) but lower specificity (27%-82%).ConclusionAlthough measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model