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Abstract
Objective
Cardiopulmonary arrest survival is dependent on optimization of perfusion via high quality cardiopulmonary resuscitation (CPR), defined by a complex dynamic between rate, depth, and recoil velocity. Here we explore the interaction between these metrics and create a model that explores the impact of these variables on compression efficacy.
Methods
This study was performed in a large urban/suburban fire-based emergency medical services (EMS) system over a nine-month period from 2019 to 2020. Manual chest compression parameters [rate/depth/recoil velocity] from a cohort of out-of-hospital cardiac arrest (OOHCA) victims were abstracted from monitor defibrillators (ZOLL X-series) and end-tidal carbon dioxide (ETCO2) from sensors. The mean values of these parameters were modeled against each other using multiple regression and structural equation modeling with ETCO2 as a dependent variable.
Results
Data from a total of 335 patients were analyzed. Strong linear relationships were observed between compression depth/recoil velocity (r = .87, p < .001), ETCO2/depth (r = .23, p < .001) and ETCO2/recoil velocity (r = .61, p < .001). Parabolic relationships were observed between rate/depth (r = .39, p < .001), rate/recoil velocity (r = .26, p < .001), and ETCO2/rate (r = .20, p = .003). Rate, depth, and recoil velocity were modeled as independent variables and ETCO2 as a dependent variable with excellence model performance suggesting the primary driver of stroke volume to be recoil velocity rather than compression depth.
Conclusions
We used manual CPR metrics from out of hospital cardiac arrests to model the relationship between CPR metrics. These results consistently support the importance of chest recoil on CPR hemodynamics, suggesting that guidelines for optimal CPR should emphasize the importance of maximum chest recoil.
Acknowledgments
The authors would like to thank the paramedics and Emergency Medical Technicians of the Riverside County Fire Department for their excellent service to the citizens of Riverside County.
Declaration of Generative AI in Scientific Writing
The authors did not use a generative artificial intelligence (AI) tool or service to assist with preparation or editing of this work. The author(s) take full responsibility for the content of this publication.
Disclosure Statement
No potential conflict of interest was reported by the author(s).