title: External Validation of Two Classification and Regression Tree Models to Predict the Outcome of Inpatient Cardiopulmonary Resuscitation.
abstract: OBJECTIVE
  To prospectively validate a previously developed classification and regression tree (CART) model that predicts the likelihood of a good outcome among patients undergoing inpatient cardiopulmonary resuscitation.
  
  
  METHODS
  Prospective validation of a clinical decision rule.
  
  
  METHODS
  Skåne University Hospital in Malmo, Sweden.
  
  
  METHODS
  All adult patients (N = 287) experiencing in-hospital cardiopulmonary arrest and undergoing cardiopulmonary resuscitation between 2007 and 2010.
  
  
  METHODS
  Patients from Skåne University Hospital who underwent CPR (N = 287) were classified using the CART models to predict their likelihood of survival neurologically intact or with minimal deficits, based on a cerebral performance category score of 1. Discrimination and classification accuracy of the score in the Swedish population was compared to that in the original (derivation and internal validation) populations.
  
  
  RESULTS
  For model 1, the area under the receiver-operating characteristic curve (AUROCC) was 0.77, compared with 0.76 and 0.73 in the original derivation and validation populations, respectively. Model 1 classified 71 (2.8%) of 287 patients as being at a very low risk of a good neurologic outcome compared with 157 (26.1%) of 287 patients predicted to be at an above average risk of a good neurologic outcome. Model 2 had a similar AUROCC as the original validation population of 0.71 but lower than the original derivation population. Model 2 performed similarly to Model 1 with regards to its ability to correctly classify patients as very low or higher than average likelihood of a good neurologic outcome.
  
  
  CONCLUSIONS
  Two CART models validated well in a different population, displaying similar discrimination and classification accuracy compared to the original population. Although additional validation in larger populations is desirable before widespread adoption, these results are very encouraging.
authors.affiliation: 1 Mercer University School of Medicine, Macon, GA, USA.
authors.forename: Ryan W R
authors.initials: RWR
authors.lastname: Guilbault
authors.affiliation: 2 Lund University Department of Internal Medicine, Malmo, Sweden.
authors.forename: Marcus Andreas
authors.initials: MA
authors.lastname: Ohlsson
authors.affiliation: 3 Duke University School of Medicine, Durham, NC, USA.
authors.forename: Anna M
authors.initials: AM
authors.lastname: Afonso
authors.affiliation: 4 Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA, USA.
authors.forename: Mark H
authors.initials: MH
authors.lastname: Ebell
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D016887:Cardiopulmonary Resuscitation / Q000379:methods / Q000706:statistics & numerical data*; D003661:Decision Support Techniques; D005260:Female; D006323:Heart Arrest / Q000401:mortality / Q000628:therapy*; D006801:Humans; D007297:Inpatients; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012372:ROC Curve; D012044:Regression Analysis; D016896:Treatment Outcome
keywords: cardiac arrest; cardiopulmonary resuscitation; classification and regression tree; clinical decision rule