title: Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: a Classification and Regression Tree analysis.
abstract: BACKGROUND
  Risk stratification for mortality in intracerebral haemorrhage (ICH) helps guide care, but existing clinical prediction rules are too cumbersome for clinical practice because of their complexity.
  
  
  OBJECTIVE
  To develop a simple decision tree model of in-hospital mortality risk stratification for ICH patients.
  
  
  METHODS
  We collected information on spontaneous ICH patients hospitalized in a teaching hospital in Japan from August, 1998 to December, 2001 (n = 374). All variables were abstracted from data available at the time of initial evaluation. A prediction rule for in-hospital mortality was developed by the Classification and Regression Tree (CART) methodology. The accuracy of the model was evaluated using the area under receiver-operator characteristic curve.
  
  
  RESULTS
  Overall in-hospital mortality rate was 20.2%. The CART methodology identified four groups for mortality risk, varying from low (2.1%) to high (58.9%). Level of consciousness (coma) was the best single predictor for mortality, followed by high ICH volume (cut-off 10.4 ml), and then age (cut-off 75 years). The accuracy of our CART model (0.86) exceeded that of a multivariate logistic regression model (0.81).
  
  
  CONCLUSIONS
  ICH patients can easily be stratified for mortality risk, based on three predictors available on admission. This simple decision tree model provides clinicians with a reliable and practical tool.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, St Luke's International Hospital, Tokyo, Japan. bur-kyt@umin.ac.jp
authors.forename: O
authors.initials: O
authors.lastname: Takahashi
authors.forename: E F
authors.initials: EF
authors.lastname: Cook
authors.forename: T
authors.initials: T
authors.lastname: Nakamura
authors.forename: J
authors.initials: J
authors.lastname: Saito
authors.forename: F
authors.initials: F
authors.lastname: Ikawa
authors.forename: T
authors.initials: T
authors.lastname: Fukui
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002543:Cerebral Hemorrhage / Q000175:diagnosis / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012044:Regression Analysis; D018570:Risk Assessment