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Predicting postconcussion syndrome after minor traumatic brain injury.
1Department of Emergency Medicine, University of Rochester Medical Center, Rochester, NY 14642, USA. jeef_bazarian@urmc.rochester.edu
Summary
A decision rule can identify patients with minor traumatic brain injury (mTBI) at low risk (<10%) or high risk (~90%) for postconcussion syndrome (PCS). This aids in stratifying mTBI patients for targeted management.
Area of Science:
- Neuroscience
- Clinical Medicine
- Public Health
Background:
- Postconcussion syndrome (PCS) affects up to 50% of patients following minor traumatic brain injury (mTBI).
- A critical need exists for a reliable decision rule to stratify mTBI patients based on their risk of developing PCS.
Purpose of the Study:
- To identify mTBI patients at low and high risk for PCS.
- To compare the predictive performance of logistic regression (LR) and recursive partitioning (RP) for PCS risk stratification.
Main Methods:
- A prospective, observational study included 69 mTBI patients (>16 years) presenting to an emergency department.
- Data collected included clinical/demographic factors and neurobehavioral test results.
- PCS diagnosis was confirmed via a validated telephone questionnaire at one month post-injury; LR and RP were applied to all variables.
Main Results:
- Overall, 58% of mTBI patients developed PCS at one month.
- Low-risk groups identified: men with HVLA scores >24 (9% PCS) via LR, and sports-injured patients with HVLA scores >22 (9% PCS) via RP.
- High-risk groups identified: women with Digit Span scores <9 (89% PCS) via LR, and fall/MVA-injured patients with HVLB2 scores <11.5 (92% PCS) via RP.
Conclusions:
- It is possible to identify distinct low-risk (<10% PCS) and high-risk (~90% PCS) subgroups among mTBI patients.
- Integrating logistic regression and recursive partitioning enhanced the classification of patients into high/low-risk categories.
- Further prospective validation of these findings is essential for clinical implementation.