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.
Design: Prospective validation of a clinical decision rule.
Setting: Skåne University Hospital in Malmo, Sweden.
Patients: All adult patients (N = 287) experiencing in-hospital cardiopulmonary arrest and undergoing cardiopulmonary resuscitation between 2007 and 2010.
Interventions: 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.
Measurements and main 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.
Conclusion: 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.
Keywords: cardiac arrest; cardiopulmonary resuscitation; classification and regression tree; clinical decision rule.