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Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury
Published on: November 9, 2018
Long-term prognostic value of Rotterdam computed tomography score in traumatic brain injury after decompressive
Chun-Hung Liu1, Tsung-Han Lee1, Yu-Hua Huang2
1Department of Neurosurgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Objective:
Decompressive craniectomy (DC) is a critical intervention for traumatic brain injury (TBI) patients with intracranial hypertension. However, outcomes following the procedure can display considerable variability. While the Rotterdam computed tomography (CT) score was conceived to predict TBI prognosis, its utility in gauging long-term outcomes after DC remains unexplored. This study aimed to elucidate the long-term prognostic value of the Rotterdam CT score in TBI patients undergoing DC.
Methods:
This retrospective study enrolled 176 patients who underwent DC for TBI, with a follow-up of at least 12 months. The pre-DC CT scans were scored by the Rotterdam CT classification. Glasgow Outcome Scale (GOS) at the end of follow-up served as the primary outcome measure. Unfavorable outcome was defined by a GOS score of 1-3. We adjusted for known predictors of outcomes post-TBI through a multivariable logistic regression analysis.
Results:
Among the 176 patients, the mean follow-up period was 70.90 ± 73.04 months. Of these, 116 patients survived their hospital stay, with a mean follow-up duration of 106.91 ± 65.42 (12-209) months. Higher Rotterdam CT scores were directly associated with higher rates of unfavorable outcomes. After multivariate regression adjustments, the predictive effect of the Rotterdam CT score on unfavorable outcomes is statistically significant (odds ratio: 1.57, 95 % confidence interval: 1.00-2.46, p = 0.048).
Conclusion:
The Rotterdam CT score demonstrates significant long-term prognostic value following DC for TBI. By methodically evaluating injury patterns using CT scans, this score addresses the existing limitations in tools assessing clinical severity.

