title: A mathematical model for predicting the outcome in moderate head injury.
abstract: BACKGROUND
  Virtually all the literature on head injury has focused on the outcome prediction of severe and mild head injuries and very few studies have been dedicated to patients sustaining moderate head injuries.
  
  
  OBJECTIVE
  To identify the patient following moderate head injury who may die, develop severe disability or significant cognitive and behavioral problems on the first day of injury itself.
  
  
  METHODS
  Tertiary teaching hospital.
  
  
  METHODS
  Prospective study divided into two groups.
  
  
  METHODS
  The study included 85 patients whose Glasgow coma scale score were 9-12 and who had isolated moderate head injury. Among the above patients a preliminary prospective study was conducted in first group of 64 patients using 7 clinical factors, 18 neuro-behavioral sequel and CT brain data in prediction of outcome with moderate head injury. From the results obtained in the above study three statistically significant factors were identified and a mathematical model was developed and used prospectively in the next 21 patients and its accuracy was evaluated. STATISTICAL METHODS USED: Multiple regression analysis and Kendall's tau non- parametric test using statistical package for social sciences (SPSS 11-5-version) were used to find out the predictive factors.
  
  
  RESULTS
  Results of these patients showed combination of CT scan brain data, verbal response and neurological signs could provide a reliable prediction in moderate head injury.
  
  
  CONCLUSIONS
  Based upon the above results a mathematical model was developed giving a value for the above-mentioned factors. The mathematical model was "CT brain data x (Verbal response + Neurological Signs)". Its overall accuracy when used on the day of admission was around 80%.
authors: Department of Neurosurgery, PSG Institute of Medical Sciences, Coimbatore and MIOT Hospitals, Chennai, India. ussrini@vsnl.net
authors: U S
authors: US
authors: Srinivasan