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Comparison of methods for the determination of cardiopulmonary resuscitation chest compression fraction
Masayuki Iyanaga1, Randal Gray, Shannon W Stephens
1Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 65249-7013, USA.
Insights
Chest compression fraction (CCF) calculation methods for out-of-hospital cardiac arrest (OHCA) showed minimal variation. Automated CCF determination appears sufficient for assessing cardiopulmonary resuscitation (CPR) quality.
Area of Science:
- Emergency Medicine
- Cardiology
- Resuscitation Science
Background:
- Chest compression fraction (CCF) is crucial for out-of-hospital cardiac arrest (OHCA) survival.
- Standardized methods for CCF calculation are lacking, hindering accurate quality assessment.
Purpose of the Study:
- To compare nine different methods for calculating CCF in adult OHCA patients.
- To determine if automated CCF calculation is sufficient for evaluating CPR quality.
Main Methods:
- Adult OHCA patients from Alabama EMS agencies (Jan-Oct 2010) were studied.
- Portable cardiac monitors with real-time chest compression detection were used.
- Nine CCF calculation methods were compared, including automated and manual epoch-based approaches.
Main Results:
- Mean CCF varied minimally across the nine methods, with most showing no statistically significant differences compared to automated calculation.
- Method 7 (start to first shock) showed a slightly lower CCF.
- Correlations between methods ranged from 0.48 to 0.85.
Conclusions:
- CCF calculation methods yield similar results, suggesting minimal impact on outcome assessment.
- Automated CCF determination is likely adequate for evaluating CPR quality in OHCA.
Objective:
While cardiopulmonary resuscitation (CPR) chest compression fraction (CCF) is associated with out-of-hospital cardiac arrest (OHCA) outcomes, there is no standard method for the determination of CCF. We compared nine methods for calculating CCF.
Methods:
We studied consecutive adult OHCA patients treated by Alabama Emergency Medical Services (EMS) agencies of the Resuscitation Outcomes Consortium (ROC) during January 1, 2010 to October 28, 2010. Paramedics used portable cardiac monitors with real-time chest compression detection technology (LifePak 12, Physio-Control, Redmond, WA). We performed both automated CCF calculation for the entire care episode as well as manual review of CPR data in 1-min epochs, defining CCF as the proportion of each treatment interval with active chest compressions. We compared the CCF values resulting from 9 calculation methods: (1) mean CCF for the entire patient care episode (automated calculation by manufacturer software), (2) mean CCF for first 3 min of patient care, (3) mean CCF for first 5 min, (4) mean CCF for first 10 min, (5) mean CCF for the entire episode except first 5 min, (6) mean CCF for last 5 min, (7) mean CCF from start to first shock, (8) mean CCF for the first half of resuscitation, and (9) mean CCF for the second half of resuscitation. We compared CCF for Methods 2-9 with Method 1 using paired t-tests with a Bonferroni-adjusted p-value of 0.006 (99.5% confidence intervals).
Results:
Among 102 adult OHCA, patient demographics were: mean age 60.3 years (SD 20.8 years), African American 56.9%, male 63.7%, and shockable ECG rhythm 23.5%. Mean CPR duration was 728 s (95% CI: 647-809 s). Mean CCF for the 9 CCF calculation methods were: (1) 0.587%; (2) 0.526%; (3) 0.541%; (4) 0.566%; (5) 0.562%; (6) 0.597%; (7) 0.530%; (8) 0.550%; and (9) 0.590%. Compared with Method 1, Method 7 CCF (start to first shock) was slightly lower (-0.057; 99.5% CI: -0.100 to -0.014). There were no other statistically significant CCF differences (range: -0.054 to 0.013). Correlation between CCF 2-9 and CCF varied (ρ=0.48-0.85).
Conclusion:
CCF varies minimally with different calculation methods. Automated CCF determination may prove sufficient for evaluating CPR quality.
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