Citation

Hellstrøm T, Kaufmann T, Andelic N, Soberg HL, Sigurdardottir S, Helseth E, Andreassen OA, Westlye LT. Front. Neurol. 2017; 8: e125.

Copyright

(Copyright © 2017, Frontiers Research Foundation)

DOI

10.3389/fneur.2017.00125

PMID

unavailable

Abstract

OBJECTIVE: Accurate outcome prediction models for patients with mild traumatic brain injury (MTBI) are key for prognostic assessment and clinical decision-making. Using multivariate machine learning, we tested the unique and added predictive value of (1) MRI-based brain morphometric and volumetric characterization at 4-weeks post-injury and (2) demographic, pre-injury, injury-related and post-injury variables on 12-month outcomes, including disability level, post-concussion symptoms and mental health in patients with MTBI.

METHODS: A prospective, cohort study of patients (n=147) aged 16-65 years with a 12-month follow-up. T1-weighted 3T MRI data were processed in FreeSurfer, yielding accurate cortical reconstructions for surface-based analyses of cortical thickness, area and volume, and brain segmentation for subcortical and global brain volumes. The 12-month outcome was defined as a composite score using a principal component analysis including the Glasgow Outcome Scale Extended, Rivermead Postconcussion Questionnaire and Patient Health Questionnaire-9. Using leave-one-out cross-validation and permutation testing, we tested and compared three prediction models: (1) MRI model, (2) clinical model, and (3) MRI and clinical combined.

RESULTS: We found a strong correlation between observed and predicted outcome for the clinical model (r=.55, p<.001). The MRI model performed at the chance level (r=.03, p=.80) and the combined model (r=.45, p<.002) was slightly weaker than the clinical model. Univariate correlation analyses revealed the strongest association with outcome for post-injury factors of posttraumatic stress (PTSS-10, r=0.61), psychological distress (HADS, r=0.52) and widespread pain (r=0.43) assessed at 8 weeks.

CONCLUSION: We found no added predictive value of MRI-based measures of brain cortical morphometry and subcortical volumes over and above demographic and clinical features.

Language: en