MP027: Automated cardiopulmonary resuscitation quality data abstraction for complete episodes of out-of-hospital cardiac arrest resuscitation. (2nd June 2016)

• Record Type: Journal Article
• Authors: Taher, A.K.; Lin, S.; Turgulov, A.; Buick, J.E.; Byers, A.; Drennan, I.; Hu, S.; Morrison, L.J.
• Is Part Of: CJEM. Volume 18(2016:Jan.)Supplement 1
• Page Start: S75
• Page End: S75
• Publication Date: 2016-06-02
• Subjects: cardiopulmonary resuscitation (CPR), -- quality, -- emergency medical services (EMS); Emergency Treatment -- Periodicals; Emergency Medicine -- Periodicals; Emergency medical services -- Canada -- Periodicals; Medical emergencies -- Canada -- Periodicals; Emergency medical services; Medical emergencies; Canada; Periodicals; 616.02505
• DOI: 10.1017/cem.2016.168
• Languages: English
• ISSNs: 1481-8035

Abstract

Abstract : Introduction: Cardiopulmonary resuscitation (CPR) quality assurance and research has traditionally been limited to the first five minutes of resuscitation due to significant costs in time, resources and personnel from manual data abstraction. Moreover, CPR quality can be affected during prolonged resuscitations, which represents significant knowledge gaps. The objective of this study was to develop a software program to help automate the abstraction of CPR quality data from electronic defibrillators.Methods: We developed a software program to facilitate and help automate data abstraction from electronic defibrillator files for entire resuscitation episodes. Internal validation of the software program was performed on 50 randomly selected cardiac arrest cases with resuscitation durations of up to 60 minutes. CPR quality data variables such as number of ventilations, number of compressions, minute compression rate, minute compression depth, minute compression fraction, minute end-tidal CO2, were manually abstracted independently by two trained data abstractors and by the automated software program. Error rates and the time needed for data abstraction were measured.Results: A total of 9826 data points were abstracted. Manual data abstraction resulted in a total of six errors (0.06%) compared to zero errors by the software program. The mean time ± SD needed for manual data abstraction was 20.3 ± 2.7 minutes compared to 5.3 ± 1.4 minutes using the software program (p=0.003).Conclusion: Our CPR quality data abstraction software was 100% accurate in abstracting CPR quality data for complete resuscitation episodes and showed a significant reduction in data abstraction duration. This software will enable quality assurance programs and future cardiac arrest studies to evaluate the impact of CPR quality during prolonged resuscitations.