Sunil Munakomi.[J].中华创伤杂志英文版,2016,19(1):25-27
A comparative study between Marshall and Rotterdam CT scores in predicting early deaths in patients withtraumatic brain injury in a major tertiary care hospital in Nepal
  
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KeyWord: Brain injuries Tomography computed Prognosis Marshall score Rotterdam score
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Sunil Munakomi Department of Neurosurgery, College of Medical Sciences 
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Abstract:
      Purpose: CT plays a crucial role in the early assessment of patients with traumatic brain injury (TBI). Marshall and Rotterdam are the mostly used scoring systems, in which CT findings are grouped differently. We sought to determine the values of the scoring system and initial CT findings in predicting the death at hospital discharge (early death) in patients with TBI. Methods: There were consecutive 634 traumatic neurosurgical patients with mild-to-severe TBI admitted to the emergency department of College of Medical Sciences. Their initial CT and status at hospital discharge (dead or alive) were reviewed, and both CT scores were calculated. We examined whether each score is related to early death; compared the two scoring systems' performance in predicting early death, and identified the CT findings that are independent predictors for early death. Results: Both imaging score (Marshall) and clinical score (Rotterdam) can be used to reliably predict mortality in patients with acute traumatic brain injury with high prognostic accuracy. Other specific CT characteristics that can be used to predict early mortality are traumatic subarachnoid hemorrhage, midline shift and status of the peri-mesencephalic cisterns. Conclusions: Marshall CT classification has strong predictive power, but greater discrimination can be obtained if the individual CT parameters underlying the CT classification are included in a prognostic model as in Rotterdam score. Consequently, for prognostic purposes, we recommend the use of individual characteristics rather than the CT classification. Performance of CT models for predicting outcome in TBI can be significantly improved by including more details of variables and by adding other variables to the models.
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