title: Classification of cardiopulmonary resuscitation chest compression patterns: manual versus automated approaches.
abstract: OBJECTIVE
  New chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.
  
  
  METHODS
  This was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.
  
  
  RESULTS
  The majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was κ = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was κ = 0.84 (95% CI = 0.59 to 0.95).
  
  
  CONCLUSIONS
  In this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.
authors: Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL.
authors: Henry E
authors: HE
authors: Wang
authors: Robert H
authors: RH
authors: Schmicker
authors: Heather
authors: H
authors: Herren
authors: Siobhan
authors: S
authors: Brown
authors: John P
authors: JP
authors: Donnelly
authors: Randal
authors: R
authors: Gray
authors: Sally
authors: S
authors: Ragsdale
authors: Andrew
authors: A
authors: Gleeson
authors: Adam
authors: A
authors: Byers
authors: Jamie
authors: J
authors: Jasti
authors: Christina
authors: C
authors: Aguirre
authors: Pam
authors: P
authors: Owens
authors: Joe
authors: J
authors: Condle
authors: Brian
authors: B
authors: Leroux
mesh_terms: D000465:Algorithms; D016887:Cardiopulmonary Resuscitation / Q000145:classification* / Q000592:standards*; D005260:Female; D006323:Heart Arrest / Q000628:therapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011312:Pressure; D015203:Reproducibility of Results; D013909:Thorax
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