Modelling early recovery patterns after paediatric traumatic brain injury

Rob J Forsyth1, Cynthia F Salorio, James R Christensen

  • 1Sir James Spence Institute, Royal Victoria Infirmary, Newcastle University, Newcastle upon Tyne NE1 4LP, UK. r.j.forsyth@newcastle.ac.uk

Insights

This study analyzed early recovery patterns in children with traumatic brain injury (TBI) using the WeeFIM scale. Early recovery indicators, like time to follow commands, can predict functional outcomes in pediatric TBI rehabilitation.

Area of Science:

  • Neurology
  • Rehabilitation Medicine
  • Pediatrics

Background:

  • Traumatic brain injury (TBI) in children necessitates inpatient rehabilitation.
  • Understanding early recovery patterns is crucial for predicting functional outcomes.
  • The WeeFIM scale is a validated measure of functional independence in children.

Purpose of the Study:

  • To describe the range of early recovery patterns in pediatric TBI patients.
  • To develop simple predictive models for expected recovery trajectories.
  • To identify early indicators of recovery in children post-TBI.

Main Methods:

  • Analysis of 103 pediatric admissions to a neurological rehabilitation facility post-TBI.
  • Utilized repeated WeeFIM scores to construct recovery trajectories.
  • Employed non-linear mixed effects modeling to define typical recoveries and identify predictors.

Main Results:

  • WeeFIM recovery curves exhibited a sigmoidal pattern: slow initial phase, rapid mid-phase improvement, and plateau.
  • Final WeeFIM scores varied widely (median 105), with significant range in time to reach 50% of final score (median 27 days).
  • Time to follow commands (TFC) correlated with final WeeFIM scores and time to reach 50% improvement, serving as a key predictor.

Conclusions:

  • Simple predictive models for pediatric TBI recovery can be developed using early recovery indices like TFC.
  • These models facilitate informed discussions with families regarding expected recovery rates and confidence intervals.
  • Such models can aid in identifying children with better- or worse-than-expected recovery trajectories.
Abstract