Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data – Tested in Zoll X Series. (March 2022)

• Record Type: Journal Article
• Authors: Orlob, Simon; Kern, Wolfgang J.; Alpers, Birgitt; Schörghuber, Michael; Bohn, Andreas; Holler, Martin; Gräsner, Jan-Thorsten; Wnent, Jan
• Is Part Of: Resuscitation. Volume 172(2022)
• Page Start: 162
• Page End: 169
• Publication Date: 2022-03
• Subjects: CCF chest compression fraction -- CPR cardiopulmonary resuscitation -- ICC intraclass correlation coefficient -- IQR interquartile range -- MCC Matthews correlation coefficient -- SD standard deviation -- 95%-CI 95% confidence interval; Cardiac arrest -- Cardiopulmonary resuscitation -- Chest compressions -- Chest compression fraction -- Data science -- Accelerometry; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2021.12.028
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Aim: To introduce and evaluate a new, open-source algorithm to detect chest compression periods automatically by the rhythmic, high amplitude signals from an accelerometer, without processing single chest compression events, and to consecutively calculate the chest compression fraction (CCF). Methods: A consecutive sample of defibrillator records from the German Resuscitation Registry was obtained and manually annotated in consensus as ground truth. Chest compression periods were determined by different automatic approaches, including the new algorithm. The diagnostic performance of these approaches was assessed. Further, using the different approaches in conjunction with different granularities of manual annotation, several CCF versions were calculated and compared by intraclass correlation coefficient (ICC). Results: 131 defibrillator recordings with a total duration of 5755 minutes were analysed. The new algorithm had a sensitivity of 99.39 (95% CI 99.38, 99.41)% and specificity of 99.17 (95% CI 99.15; 99.18)% to detect chest compressions at any given timepoint. The ICC compared to ground truth was 0.998 for the new algorithm and 0.999 for manual annotation, while the ICC of the proposed algorithm compared to the proprietary software was 0.978. The time required for manual annotation to calculate CCF was reduced by 70.48 (22.55, [94.35, 14.45])%. Conclusion: The proposed algorithm reliably detects chest compressions in defibrillator recordings. It can markedly reduce the workload for manual annotation, which may facilitate uniform reporting of measured quality of cardiopulmonary resuscitation. The algorithm is made freely available and may be used in big data analysis and machine learning approaches.