title: Classification of Different Degrees of Disability Following Intracerebral Hemorrhage: A Decision Tree Analysis from VISTA-ICH Collaboration.
abstract: OBJECTIVE
  Prognostication following intracerebral hemorrhage (ICH) has focused on poor outcome at the expense of lumping together mild and moderate disability. We aimed to develop a novel approach at classifying a range of disability following ICH.
  
  
  METHODS
  The Virtual International Stroke Trial Archive collaboration database was searched for patients with ICH and known volume of ICH on baseline CT scans. Disability was partitioned into mild [modified Rankin Scale (mRS) at 90 days of 0-2], moderate (mRS = 3-4), and severe disabilities (mRS = 5-6). We used binary and trichotomy decision tree methodology. The data were randomly divided into training (2/3 of data) and validation (1/3 data) datasets. The area under the receiver operating characteristic curve (AUC) was used to calculate the accuracy of the decision tree model.
  
  
  RESULTS
  We identified 957 patients, age 65.9 ± 12.3 years, 63.7% males, and ICH volume 22.6 ± 22.1 ml. The binary tree showed that lower ICH volume (<13.7 ml), age (<66.5 years), serum glucose (<8.95 mmol/l), and systolic blood pressure (<170 mm Hg) discriminate between mild versus moderate-to-severe disabilities with AUC of 0.79 (95% CI 0.73-0.85). Large ICH volume (>27.9 ml), older age (>69.5 years), and low Glasgow Coma Scale (<15) classify severe disability with AUC of 0.80 (95% CI 0.75-0.86). The trichotomy tree showed that ICH volume, age, and serum glucose can separate mild, moderate, and severe disability groups with AUC 0.79 (95% CI 0.71-0.87).
  
  
  CONCLUSIONS
  Both the binary and trichotomy methods provide equivalent discrimination of disability outcome after ICH. The trichotomy method can classify three categories at once, whereas this action was not possible with the binary method. The trichotomy method may be of use to clinicians and trialists for classifying a range of disability in ICH.
authors.affiliation: Neurosciences, Monash Health , Melbourne, VIC , Australia.
authors.forename: Thanh G
authors.initials: TG
authors.lastname: Phan
authors.affiliation: Department of Medicine, School of Clinical Sciences, Monash University , Clayton, VIC , Australia.
authors.forename: Jian
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Medicine, School of Clinical Sciences, Monash University , Clayton, VIC , Australia.
authors.forename: Richard
authors.initials: R
authors.lastname: Beare
authors.affiliation: Neurosciences, Monash Health, Melbourne, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia.
authors.forename: Henry
authors.initials: H
authors.lastname: Ma
authors.affiliation: Neurosciences, Monash Health, Melbourne, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Clissold
authors.affiliation: Neurosciences, Monash Health, Melbourne, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Van Ly
authors.affiliation: Neurosciences, Monash Health, Melbourne, VIC, Australia; Department of Medicine, Central Clinical School, Monash University, Frankston, VIC, Australia.
authors.forename: Velandai
authors.initials: V
authors.lastname: Srikanth
keywords: decision trees; disability evaluation; intracerebral hemorrhage; outcome; stroke