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Predicting Outcome 12 Months after Mild Traumatic Brain Injury in Patients Admitted to a Neurosurgery Service

View Article: PubMed Central - PubMed

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) magnetic resonance imaging (MRI)-based brain morphometric and volumetric characterization at 4-week postinjury and (2) demographic, preinjury, injury-related, and postinjury variables on 12-month outcomes, including global functioning level, postconcussion 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 3 T 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 outcomes for the clinical model (r = 0.55, p < 0.001). The MRI model performed at the chance level (r = 0.03, p = 0.80) and the combined model (r = 0.45, p < 0.002) were slightly weaker than the clinical model. Univariate correlation analyses revealed the strongest association with outcome for postinjury factors of posttraumatic stress (Posttraumatic Symptom Scale-10, r = 0.61), psychological distress (Hospital Anxiety and Depression Scale, 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.

No MeSH data available.


Related in: MedlinePlus

Flow diagram of mild traumatic brain injury (MTBI).
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Figure 1: Flow diagram of mild traumatic brain injury (MTBI).

Mentions: Of the 223 patients who fulfilled the inclusion criteria, 36 were excluded, 28 did not attend to MRI or withdrew, five interrupted MRI, and three had an MRI-incompatible implant. Eight patients did not show up for 8-week follow-up. MRI data sets for 11 patients were discarded due to motion artifact or gross abnormal intracranial findings, which severely interfered with the automated reconstruction in FreeSurfer. Seventeen patients did not show up for 12-month follow-up, and four patients were excluded after quality assurance of the segmentation, resulting in a final sample size of 147 patients (Figure 1).


Predicting Outcome 12 Months after Mild Traumatic Brain Injury in Patients Admitted to a Neurosurgery Service
Flow diagram of mild traumatic brain injury (MTBI).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385465&req=5

Figure 1: Flow diagram of mild traumatic brain injury (MTBI).
Mentions: Of the 223 patients who fulfilled the inclusion criteria, 36 were excluded, 28 did not attend to MRI or withdrew, five interrupted MRI, and three had an MRI-incompatible implant. Eight patients did not show up for 8-week follow-up. MRI data sets for 11 patients were discarded due to motion artifact or gross abnormal intracranial findings, which severely interfered with the automated reconstruction in FreeSurfer. Seventeen patients did not show up for 12-month follow-up, and four patients were excluded after quality assurance of the segmentation, resulting in a final sample size of 147 patients (Figure 1).

View Article: PubMed Central - PubMed

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) magnetic resonance imaging (MRI)-based brain morphometric and volumetric characterization at 4-week postinjury and (2) demographic, preinjury, injury-related, and postinjury variables on 12-month outcomes, including global functioning level, postconcussion 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 3 T 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 outcomes for the clinical model (r = 0.55, p < 0.001). The MRI model performed at the chance level (r = 0.03, p = 0.80) and the combined model (r = 0.45, p < 0.002) were slightly weaker than the clinical model. Univariate correlation analyses revealed the strongest association with outcome for postinjury factors of posttraumatic stress (Posttraumatic Symptom Scale-10, r = 0.61), psychological distress (Hospital Anxiety and Depression Scale, 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.

No MeSH data available.


Related in: MedlinePlus