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A Novel Model of Mild Traumatic Brain Injury for Juvenile Rats
Published on: December 8, 2014
Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical
Bing Si1, Gina Dumkrieger1,2, Teresa Wu1
1School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
Background:
The current classification of traumatic brain injury (TBI) into "mild", "moderate", or "severe" does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify "sub-groups" of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.
Methods:
Data from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.
Results:
Amongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.
Conclusions:
Sub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient's sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.
Insights
Researchers identified five distinct subgroups within mild traumatic brain injury (mTBI) patients using initial clinical data. These mTBI subgroups showed varied outcomes at 90 and 180 days, suggesting a need for more personalized patient classification.
Area of Science:
- Neuroscience
- Clinical Neurology
- Public Health
Background:
- Current mild traumatic brain injury (mTBI) classifications lack granularity, failing to capture patient heterogeneity.
- This limits the ability to predict individual patient outcomes and tailor treatment strategies effectively.
Purpose of the Study:
- To identify distinct patient subgroups within the mild TBI (mTBI) population.
- To determine if these identified subgroups correlate with patient outcomes at 90 and 180 days post-injury.
Main Methods:
- Utilized sparse hierarchical clustering on 53 clinical variables from the TRACK-TBI Pilot dataset for patients with Glasgow Coma Scale scores of 13-15.
- Identified optimal patient subgroups based on cluster quality assessment and compared outcomes across these groups.
Main Results:
- Clustering of 485 mTBI patients based on 12 clinical variables revealed 5 distinct subgroups.
- These subgroups differed significantly in global outcomes, persistent symptoms, and neuropsychological impairment at 90 and 180 days.
Conclusions:
- Clinically meaningful subgroups of mTBI patients can be identified using readily available initial evaluation data.
- Subgroup assignment is associated with multidimensional patient outcomes, supporting refined classification beyond current broad categories.
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