title: A prediction model for out-of-hospital cardiopulmonary resuscitation.
abstract: BACKGROUND
  We created a prediction model to be used in cardiopulmonary resuscitation (CPR) attempts as a decision tool to omit futile CPR attempts and to save resources.
  
  
  METHODS
  In this post hoc analysis, we assessed predictive parameters for neurological recovery after successful CPR. The original study was designed as a blinded, randomized, prospective, controlled, multicenter clinical trial.
  
  
  RESULTS
  We identified 1166 prehospital cardiac arrest patients being treated with advanced cardiac life support. Seven hundred eighty-six of 1166 patients (67.4%) died at the scene and 380 of 1166 (32.6%) were brought to the hospital. Two hundred sixty-five of 1166 patients (22.7%) died in the hospital. One hundred fifteen of 1166 (9.8%) were discharged from the hospital and 92 of the 115 patients (80%) could be followed-up. Good cerebral performance was regained by 54% of discharged patients (50 of 92 patients). In 46% of patients (42/92), unconsciousness or severe disability remained. Ventricular fibrillation was more likely to have occurred in patients with good neurological recovery (42/50 = 84.0%), whereas asystole was more likely in patients with poor neurological recovery (9/42 = 21.4%). A score was developed to predict the probability of death using logistic regression analysis. Predicting death in the hospital revealed a sensitivity of 99.8% (953/955), but only a specificity of 2.9% (3/104; threshold 0.5). Predicting survival until discharge from the hospital revealed a sensitivity of 99% (103/104), but only a specificity of 8% (72/955; threshold 0.99). A receiver operating characteristic curve yielded an area under the curve of 0.795 (0.751-0.839) at a confidence interval of 95%.
  
  
  CONCLUSIONS
  For out-of-hospital patients with cardiac arrest, parameters documented in the field did not allow accurate prediction of hospital survival.
authors.affiliation: Department of Anesthesiology and Critical Care Medicine, Innsbruck Medical University, Anichstrasse 35, 6020 Innsbruck, Austria. iris.pircher@i-med.ac.at
authors.forename: Iris R
authors.initials: IR
authors.lastname: Pircher
authors.forename: Karl-Heinz
authors.initials: KH
authors.lastname: Stadlbauer
authors.forename: Anette C
authors.initials: AC
authors.lastname: Severing
authors.forename: Viktoria D
authors.initials: VD
authors.lastname: Mayr
authors.forename: Hannes G
authors.initials: HG
authors.lastname: Lienhart
authors.forename: Beate
authors.initials: B
authors.lastname: Jahn
authors.forename: Karl H
authors.initials: KH
authors.lastname: Lindner
authors.forename: Volker
authors.initials: V
authors.lastname: Wenzel
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016887:Cardiopulmonary Resuscitation / Q000401:mortality; D003661:Decision Support Techniques; D004632:Emergency Medical Services; D005060:Europe / Q000453:epidemiology; D005260:Female; D006323:Heart Arrest / Q000401:mortality / Q000503:physiopathology / Q000628:therapy*; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D018447:Medical Futility; D008875:Middle Aged; D015337:Multicenter Studies as Topic; D016017:Odds Ratio; D018579:Patient Selection; D018458:Persistent Vegetative State; D011237:Predictive Value of Tests; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic; D020127:Recovery of Function; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D013997:Time Factors; D016896:Treatment Outcome