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J Clin Neurosci. 2001 Mar;8(2):120-3.

Outcome prediction in severe head injury: analyses of clinical prognostic factors.

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Department of Neurosurgery, Chiba University School of Medicine, Japan.


Retrospective analysis of 272 patients with severe head injury was performed. Patient age, Glasgow Coma Scale (GCS) score, pupillary abnormalities, impaired oculocephalic response, presence of subarachnoid haemorrhage, and multiplicity of parenchymal lesions on computerised tomography (CT) were examined. The CT findings were divided into 2 groups, diffuse brain injury (DBI) and mass lesion, according to the classification of the Traumatic Coma Data Bank. The DBI, basically, has no high or mixed density lesion more than 25 ml on CT, and was classified into 4 subgroups: DBI I includes injuries where there is no visible pathology; DBI II includes all injuries in which the cisterns are present with a midline shift of less than 5 mm; DBI III includes injuries with swelling where the cisterns are compressed or absent and the midline shift is less than 5 mm; DBI IV includes injuries with a midline shift of more than 5 mm. The mass lesions were categorised into 3 subgroups: epidural haematoma; acute subdural haematoma; and intracerebral haematoma. Outcomes were determined at 6 months following trauma using the Glasgow Outcome Scale. All DBI I patients recovered well. In the DBI II group, age, GCS score and detection of multiple parenchymal lesions on CT were significantly correlated with outcome. For the DBI III and IV groups, the only significant prognostic factor was the GCS score. In patients with a mass lesion, the GCS score was the only significant prognostic factor in the epidural haematoma group, but the GCS score and the presence of subarachnoid haemorrhage were predictive factors in the acute subdural haematoma group. Outcomes were unfavourable in the majority of patients with intracerebral haematoma. GCS score could predict outcome in all groups. The confidence of the outcome prediction ranged from 75.8 to 92.1%, depending on logistic regression analysis.

[Indexed for MEDLINE]

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