Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: A Classification and Regression Tree Analysis

Abstract

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. To develop a simple decision tree model of in-hospital mortality risk stratification for ICH patients. 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. 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). 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.