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Why do some studies find that CPR fraction is not a predictor of survival?
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Resuscitation xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Resuscitation journal homepage: www.elsevier.com/locate/resuscitation
Clinical paper
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Why do some studies find that CPR fraction is not a predictor of survival?夽
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Lars Wik a,∗ , Jan-Aage Olsen a,b , David Persse c , Fritz Sterz d , Michael Lozano Jr. e , Marc A. Brouwer f , Mark Westfall g,h , Chris M. Souders c , David T. Travis e , Ulrich R. Herken i , E. Brooke Lerner j a
Norwegian National Advisory Unit on Prehospital Emergency Medicine, Oslo University Hospital, Oslo, Norway Institute of Clinical Medicine, University of Oslo, Norway c Houston Fire Department and the Baylor College of Medicine, Houston, TX, United States d Department of Emergency Medicine, Medical University of Vienna, Vienna, Austria e Hillsborough County Fire Rescue, Tampa, FL, United States f Heart Lung Center, Department of Cardiology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands g Gold Cross Ambulance Service, Appleton Neenah-Menasha and Grand Chute Fire Departments, WI, United States h Theda Clark Regional Medical Center, Neenah, WI, United States i ZOLL Medical Corporation, Chelmsford, MA, United States j Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, WI, United States b
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Article history: Received 8 January 2016 Received in revised form 15 March 2016 Accepted 20 April 2016
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Keywords: Cardiac arrest Emergency medical services CPR Chest compression fraction
Introduction: An 80% chest compression fraction (CCF) during resuscitation is recommended. However, heterogeneous results in CCF studies were found during the 2015 Consensus on Science (CoS), which may be because chest compressions are stopped for a wide variety of reasons including providing lifesaving care, provider distraction, fatigue, confusion, and inability to perform lifesaving skills efficiently. Objective: The effect of confounding variables on CCF to predict cardiac arrest survival. Methods: A secondary analysis of emergency medical services (EMS) treated out-of-hospital cardiac arrest (OHCA) patients who received manual compressions. CCF (percent of time patients received compressions) was determined from electronic defibrillator files. Two Sample Wilcoxon Rank Sum or regression determined a statistical association between CCF and age, gender, bystander CPR, public location, witnessed arrest, shockable rhythm, resuscitation duration, study site, and number of shocks. Univariate and multivariate logistic regressions were used to determine CCF effect on survival. Results: Of 2132 patients with manual compressions 1997 had complete data. Shockable rhythm (p < 0.001), public location (p < 0.004), treatment duration (p < 0.001), and number of shocks (p < 0.001) were associated with lower CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01–0.36). Multivariate regression controlling for factors associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02–39.5). Conclusion: CCF cannot be looked at in isolation as a predictor of survival, but in the context of other resuscitation activities. When controlling for the effects of other resuscitation activities, a higher CCF is predictive of survival. This may explain the heterogeneity of findings during the CoS review. © 2016 Elsevier Ireland Ltd. All rights reserved.
Introduction
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夽 ClinicalTrials.gov Identifier: NCT00597207 (http://clinicaltrials.gov/ct2/show/ NCT00597207). ∗ Corresponding author at: Norwegian National Advisory Unit on Prehospital Emergency Medicine, Oslo University Hospital, Postboks 4956 Nydalen, 0424 Oslo, Norway. E-mail address: [email protected] (L. Wik).
In 2013 an American Heart Association Expert Panel recommended that emergency care providers strive for an 80% chest compression fraction (CCF) during resuscitation.1 The recently published consensus on science found conflicting results in the literature.2 Observational studies have shown a correlation between decreased compression fraction and survival,3–8 but a randomized clinical trial evaluating a bundle of changes showed no
http://dx.doi.org/10.1016/j.resuscitation.2016.04.013 0300-9572/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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improvement in survival when perishock pauses were reduced.9 Based on this the International Liaison Committee on Resuscitation (ILCOR) maintained the recommendation to limit chest compression pauses during resuscitation and to strive for a 60% CCF.2 During resuscitation providers may stop chest compressions for reasons that are integral to patient care such as assessing heart rhythm and pulse, or defibrillation. Alternatively, providers may pause chest compressions for reasons that are not related to patient care such as distraction, fatigue, confusion, or inability to perform lifesaving skills efficiently. Because all types of pauses reduce the overall CCF this may lead to a confusing picture when CCF is analyzed in isolation and may explain the heterogeneous findings the Consensus on Science identified.2 Recent sub-analyses of clinical trial data found that patients with a higher CCF were less likely to survive.10,11 Of these, the CIRC trial evaluated the entire out-of-hospital resuscitation and calculated the CCF based on the full resuscitation rather than a shorter initial period (e.g., 5 min). The current study was conducted to investigate the effect of confounding variables on the ability of CCF to predict cardiac arrest survival using CIRC trial data. This might help to explain the conflicting findings in prior studies and clarify how best to use CCF when interpreting the results of future cardiac arrest studies.
Methods The CIRC trial was a multicenter randomized clinical trial comparing integrated manual and load distributing band (LDB) CPR with manual CPR alone in EMS treated adult out of hospital cardiac arrest (OHCA) of presumed cardiac etiology.12 Only data collected on the manual CPR arm during the CIRC trial were used to conduct this secondary analysis. This allowed us to better match the populations and settings used in prior studies on CCF that were the basis for the ILCOR consensus statement.2 CCF was calculated as part of the primary study and it was defined as the percentage of time when the patient received chest compressions during resuscitation. It was obtained from electronic EMS defibrillator files for each minute of the resuscitation by two reviewers who were blinded to outcome, but not study arm. CCF was calculated following previously published methodology.13,14 Any interruption in compressions longer than 1.5 s was considered a pause. CCF was calculated for all cases over the entire prehospital resuscitation. Any minute with ROSC was excluded from the CCF calculation, but if a patient re-arrested those minutes were included in the CCF calculation. ROSC was identified as periods where the patient had an organized rhythm, received no chest compressions, and study documentation indicated that the patient achieved a pulse generating rhythm during that time interval. For this analysis we used the previously calculated CCFs for all patients in the manual CPR arm of the CIRC trial. Two Sample Wilcoxon Rank Sum was used to determine if there was a statistical association between CCF and the dichotomous variables: gender, bystander CPR, public location, and witnessed arrest, shockable rhythm. Regression was used to determine if there was an association between CCF and the variables: age, resuscitation duration, study site, and number of shocks. Due to their non-normal distribution, age and number of shocks were analyzed as categorical variables. Univariate and multivariate logistic regressions were then conducted to determine the effect of CCF on survival to hospital discharge. Variables were included in the multivariate model if they were found to be statistically associated with survival and/or CCF. A 95% confidence interval that did not include unity was considered statistically significant.
The CIRC trial was reviewed by the Institutional Review Board or the Ethics Board at each participating institution with an Emergency Exception from Informed Consent.
Results There were 2132 patients enrolled in the parent trial who received manual compressions. Of those, 48 had incomplete chest compression data and 67 had no recorded chest compressions. No information about delivered shocks was available in 14 cases and survival data was missing in 7. A total of 1997 cases had complete data and were used for this secondary analysis. Table 1 illustrates the variables that were found to be associated with CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01–0.36) (Table 1). However, multivariate regression controlling for those factors that were significantly associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02–39.5).
Discussion This study found that without adjustment for other factors cases with a higher CCF had a lower rate of survival than those with a lower CCF. This result is counter intuitive given the common recommendation that providers maximize CCF when treating cardiac arrest patients. This result cannot be dismissed as an aberration since Christenson et al. had similar findings at their highest CCF rates,7 and the International Liaison Committee on Resuscitation found heterogeneity in the literature that they reviewed on CCF.2 The univariate finding that a higher CCF is associated with lower survival likely does not mean that CCF is not important for improved cardiac arrest survival. This is supported by our finding that after controlling for other variables that are associated with survival to hospital discharge, many of which by their nature also lower the CCF, higher CCF is predictive of survival to hospital discharge. This is probably because evaluating CCF in isolation does not separate the “good” pauses from the “bad” pauses. The univariate analysis of CCF’s relationship to survival does not differentiate between CCF’s that are diminished due to concurrent interventions that are life promoting and those that are not. A patient who is defibrillated and then goes into asystole may have a higher CCF than a case that remains in ventricular fibrillation and requires additional defibrillation. Patients who remain in ventricular fibrillation are more likely to survive than patients who immediately develop asystole even though they will have a lower CCF. The findings from this analysis may have implications for future cardiac arrest research. This study demonstrates that CCF should not be analyzed in isolation, but these findings may also have implications for future randomized clinical trials. Generally clinical trials use randomization to create equal groups that can then be used to compare an intervention. The population that is randomized has a specific disease and randomization is intended to distribute known and unknown confounders equally between the groups allowing researchers to determine the effect of an intervention on a specific disease typically without statistical adjustment. However, if the randomization is not successful, usually because of enrolling too few subjects, then the groups will not be equal and the findings of any unadjusted analysis will be suspect. This can happen even if there is an equal distribution of the known confounders between the study arms since the combinations of variables may not be equal (e.g., a similar proportion of patients with witnessed cardiac arrest and initial shockable rhythms, but different proportions of patients with both witnessed cardiac arrest and an initial shockable rhythm).
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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Table 1 Multivariate analysis of survival to hospital discharge. Included patients (n = 1997)
Association with CCF Coef (95% CI) or Z, p-value
Association with survival OR (95% CI)
Multivariate association with survival OR (95% CI)
0.07 (0.01–0.36)
6.34 (1.02–39.5)
Reference 1.01 (0.73–1.40) 0.48 (0.33–0.71) 1.21 (0.89–1.63) 3.51 (2.55–4.83) 3.16 (2.27–4.4) 5.02 (3.73–6.75)
Reference 1.06 (0.7–1.61) 0.51 (0.32–0.82) 1.02 (0.69–1.49) 2.0 (1.36–2.94) 1.74 (1.1–2.75) 2.54 (1.39–4.65)
.0005 (.0002 to .0008)
Reference 3.44 (2.33–5.08) 4.43 (3.0–6.56) 2.99 (1.85–4.82) 1.19 (0.62–2.30) 1.48 (1.07–2.03) 0.83 (0.82–0.85)
Reference 1.75 (0.95–3.2) 1.6 (0.78–3.28) 2.69 (1.18–6.15) 3.75 (1.3–10.9) 1.48 (0.87–2.51) 0.82 (0.80–0.85)
Reference −0.03 (−0.06 to −0.01) 0.04 (0.02 to 0.05) −0.01 (−0.02 to −0.001) −.029 (−0.05 to 0.01)
Reference 0.48 (0.16–1.4) 0.53 (0.31–0.90) 0.73 (0.49–1.08) 1.35 (0.67–2.7)
Reference 0.58 (0.16–2.04) 0.93 (0.49–1.79) 2.66 (1.48–4.8) 1.24 (0.50–3.04)
CCF Age 18–59 years 60–74 years
Male gender
62%
Reference −0.007 (−0.016 to 0.002) 0.004 (−0.005 to 0.013) Z = 2.25, p = 0.025
Witnessed arrest
47%
Z = 4.45, p < 0.001
Public location of the OHCA
13%
Z = 2.92, p < 0.004
Shockable rhythm
25%
Z = 6.15, p < 0.001
Number of EMS shocks No shocks 1 shock
58% 14%
Reference −0.02 (−0.031 to −0.009) −0.029 (−0.041 to −0.017) −0.034 (−0.048 to −0.017) −0.033 (−0.047 to −0.019) Z = −3.83, p < 0.001
75 years and older
34% 32% 34%
2–3 shocks
12%
4–5 shocks
8%
More than 5 shocks
8%
Received bystander chest compressions
22%
Median treatment duration (IQR)
21 (12–31)
Site IDa CIRC Site 1 CIRC Site 2
– –
CIRC Site 3
–
CIRC Site 4
–
CIRC Site 5
–
Coef: regression coefficient, Z: Wilcoxon Rank Sum test Z-value, OR: odds ratio, CI: confidence interval, IQR: interquartile range, SD: standard deviation, CCF: chest compression fraction. a The percent of included patients by site was not included so that the specific sites cannot be identified.
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184
In most cardiac arrest research investigators include all patients who present to EMS in cardiac arrest. This sampling technique creates a heterogeneous sample whose chances of survival are variable based on their condition and the treatments that they receive. This may lead to a failure of the randomization to equally distribute all confounders between the study groups equally. This can be seen in the demographic information presented in the recently published study by Nichol et al. on continuous or interrupted chest compressions, which had significant differences between the study arms for witnessed arrest and study site15 ; and in the CIRC trial a difference was seen in the proportion of cases with a shockable initial rhythm in each study arm.10 Controlling for confounders is critical in clinical trials, but it is particularly difficult in trials that include a broad pool of patients such as OHCA research. Investigators must either conduct studies with a more homogenous pool of patients or use statistical adjustments in addition to randomization to control for confounding. Using research in myocardial infarction as an example we see that those studies differentiate between the locations of the infarct; thus homogenizing the sample and ensuring there is no need for statistical adjustment in the final analysis.16–18 This may be an explanation for so many recent large clinical cardiac arrest trials showing no difference
in outcome when all of the pretrial data seems to indicate there should be one. The variability in the data may be drowning out the results. This study has limitations. It was a secondary analysis of a subset of patients from the CIRC trial. The CCF was calculated based on human reviewers determining the number of compressions per minute and the number of seconds without compressions. This process could have introduced a small amount of variability in the calculation, which likely broadened the range of CCF. Finally, we were unable to assess compression quality beyond rate or determine the reason for low CCF.
Conclusion This study found that CCF cannot be looked at in isolation as a predictor of cardiac arrest survival. It must be looked at in the context of other resuscitation activities. When controlling for the effect of other resuscitation activities a higher CCF is predictive of survival. This may explain the heterogeneity of findings in studies that look at the effect of CCF on cardiac arrest survival.
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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Conflict of interest statement
The authors have no relevant financial conflicts of interest with 206 the exception of Ulrich R. Herken who is an employee of ZOLL Med207 ical and Lars Wik who was the PI for the CIRC study and represents 208 the Norwegian National Advisory Unit on Prehospital Emergency 209 Medicine (NAKOS) in the Medical Advisory Board of Physio-Control. 210 Q3 All other author’s institutions received funding from ZOLL Medical 211 for their time on the CIRC study. Olsen was partly funded by an 212 unrestricted grant from Norwegian Health Region South-East and 213 partly by a research grant from ZOLL Medical to NAKOS. 205
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Funding
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ZOLL Medical Corporation funded the CIRC Trial. ZOLL funded, but did not participate in, the collection, management, or primary analysis of the data. An employee of ZOLL (UH) participated in the development of this manuscript and is listed as an author. All other authors’ institutions received funding from ZOLL for their participation in the trial.
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Acknowledgements
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The authors would like to acknowledge the EMS providers who contributed to this study as well as other individuals who made this study possible. We thank the coordinators and monitors at each of the participating sites for their careful and persistent work with the data collection.
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References
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1. Meaney PA, Bobrow BJ, Mancini ME, et al. Cardiopulmonary resuscitation quality: improving cardiac resuscitation outcomes both inside and outside the hospital: a consensus statement from the American Heart Association. Circulation 2013;128:417–35. 2. Kleinman ME, Brennan EE, Goldberger ZD, et al. Part 5: Adult basic life support and cardiopulmonary resuscitation quality: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 2015;132(Suppl. 2):S414–35. 3. Edelson DP, Abella BS, Kramer-Johansen J, et al. Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. Resuscitation 2006;71:137–45.
4. Sell RE, Sarno R, Lawrence B, et al. Minimizing pre- and post-defibrillation pauses increases the likelihood of return of spontaneous circulation (ROSC). Resuscitation 2010;81:822–5. 5. Cheskes S, Schmicker RH, Christenson J, et al. Perishock pause: an independent predictor of survival from out-of-hospital shockable cardiac arrest. Circulation 2011;124:58–66. 6. Cheskes S, Schmicker RH, Verbeek PR, et al. The impact of peri-shock pause on survival from out-of-hospital shockable cardiac arrest during the Resuscitation Outcomes Consortium PRIMED trial. Resuscitation 2014;85: 336–42. 7. Christenson J, Andrusiek D, Everson-Stewart S, et al. Chest compression fraction determines survival in patients with out-of-hospital ventricular fibrillation. Circulation 2009;120:1241–7. 8. Vaillancourt C, Everson-Stewart S, Christenson J, et al. The impact of increased chest compression fraction on return of spontaneous circulation for out-ofhospital cardiac arrest patients not in ventricular fibrillation. Resuscitation 2011;82:1501–7. 9. Jost D, Degrange H, Verret C, et al. DEFI 2005: a randomized controlled trial of the effect of automated external defibrillator cardiopulmonary resuscitation protocol on outcome from out-of-hospital cardiac arrest. Circulation 2010;121:1614–22. 10. Wik L, Olsen JA, Persse D, et al. Manual vs. integrated automatic load-distributing band CPR with equal survival after out of hospital cardiac arrest. The randomized CIRC trial. Resuscitation 2014;85:741–8. 11. Cheskes S, Schmicker RH, Rea T, et al. Chest compression fraction: a time dependent variable of survival in shockable out-of-hospital cardiac arrest. Resuscitation 2015;97:129–35. 12. Lerner EB, Persse D, Souders CM, et al. Design of the Circulation Improving Resuscitation Care (CIRC) Trial: a new state of the art design for out-of-hospital cardiac arrest research. Resuscitation 2011;82:294–9. 13. Kramer-Johansen J, Edelson DP, Losert H, Kohler K, Abella BS. Uniform reporting of measured quality of cardiopulmonary resuscitation (CPR). Resuscitation 2007;74:406–17. 14. Stecher FS, Olsen JA, Stickney RE, Wik L. Transthoracic impedance used to evaluate performance of cardiopulmonary resuscitation during out of hospital cardiac arrest. Resuscitation 2008;79:432–7. 15. Nichol G, Leroux B, Wang H, et al. Trial of continuous or interrupted chest com- Q4 pressions during CPR. N Engl J Med 2015. 16. Gibson CM, Giugliano RP, Kloner RA, et al. EMBRACE STEMI study: a Phase 2a trial to evaluate the safety, tolerability, and efficacy of intravenous MTP-131 on reperfusion injury in patients undergoing primary percutaneous coronary intervention. Eur Heart J 2015. 17. Cung T-T, Morel O, Cayla G, et al. Cyclosporine before PCI in patients with acute myocardial infarction. N Engl J Med 2015;373:1021–31. 18. Pizarro G, Fernandez-Friera L, Fuster V, et al. Long-term benefit of early pre-reperfusion metoprolol administration in patients with acute myocardial infarction: results from the METOCARD-CNIC trial (Effect of Metoprolol in Cardioprotection During an Acute Myocardial Infarction). J Am Coll Cardiol 2014;63:2356–62.
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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Resuscitation xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Resuscitation journal homepage: www.elsevier.com/locate/resuscitation
Clinical paper
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Why do some studies find that CPR fraction is not a predictor of survival?夽
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4
5 6 7 8 9 10 11 12 13 14 15 16 17
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Lars Wik a,∗ , Jan-Aage Olsen a,b , David Persse c , Fritz Sterz d , Michael Lozano Jr. e , Marc A. Brouwer f , Mark Westfall g,h , Chris M. Souders c , David T. Travis e , Ulrich R. Herken i , E. Brooke Lerner j a
Norwegian National Advisory Unit on Prehospital Emergency Medicine, Oslo University Hospital, Oslo, Norway Institute of Clinical Medicine, University of Oslo, Norway c Houston Fire Department and the Baylor College of Medicine, Houston, TX, United States d Department of Emergency Medicine, Medical University of Vienna, Vienna, Austria e Hillsborough County Fire Rescue, Tampa, FL, United States f Heart Lung Center, Department of Cardiology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands g Gold Cross Ambulance Service, Appleton Neenah-Menasha and Grand Chute Fire Departments, WI, United States h Theda Clark Regional Medical Center, Neenah, WI, United States i ZOLL Medical Corporation, Chelmsford, MA, United States j Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, WI, United States b
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Article history: Received 8 January 2016 Received in revised form 15 March 2016 Accepted 20 April 2016
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Keywords: Cardiac arrest Emergency medical services CPR Chest compression fraction
Introduction: An 80% chest compression fraction (CCF) during resuscitation is recommended. However, heterogeneous results in CCF studies were found during the 2015 Consensus on Science (CoS), which may be because chest compressions are stopped for a wide variety of reasons including providing lifesaving care, provider distraction, fatigue, confusion, and inability to perform lifesaving skills efficiently. Objective: The effect of confounding variables on CCF to predict cardiac arrest survival. Methods: A secondary analysis of emergency medical services (EMS) treated out-of-hospital cardiac arrest (OHCA) patients who received manual compressions. CCF (percent of time patients received compressions) was determined from electronic defibrillator files. Two Sample Wilcoxon Rank Sum or regression determined a statistical association between CCF and age, gender, bystander CPR, public location, witnessed arrest, shockable rhythm, resuscitation duration, study site, and number of shocks. Univariate and multivariate logistic regressions were used to determine CCF effect on survival. Results: Of 2132 patients with manual compressions 1997 had complete data. Shockable rhythm (p < 0.001), public location (p < 0.004), treatment duration (p < 0.001), and number of shocks (p < 0.001) were associated with lower CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01–0.36). Multivariate regression controlling for factors associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02–39.5). Conclusion: CCF cannot be looked at in isolation as a predictor of survival, but in the context of other resuscitation activities. When controlling for the effects of other resuscitation activities, a higher CCF is predictive of survival. This may explain the heterogeneity of findings during the CoS review. © 2016 Elsevier Ireland Ltd. All rights reserved.
Introduction
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夽 ClinicalTrials.gov Identifier: NCT00597207 (http://clinicaltrials.gov/ct2/show/ NCT00597207). ∗ Corresponding author at: Norwegian National Advisory Unit on Prehospital Emergency Medicine, Oslo University Hospital, Postboks 4956 Nydalen, 0424 Oslo, Norway. E-mail address: [email protected] (L. Wik).
In 2013 an American Heart Association Expert Panel recommended that emergency care providers strive for an 80% chest compression fraction (CCF) during resuscitation.1 The recently published consensus on science found conflicting results in the literature.2 Observational studies have shown a correlation between decreased compression fraction and survival,3–8 but a randomized clinical trial evaluating a bundle of changes showed no
http://dx.doi.org/10.1016/j.resuscitation.2016.04.013 0300-9572/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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improvement in survival when perishock pauses were reduced.9 Based on this the International Liaison Committee on Resuscitation (ILCOR) maintained the recommendation to limit chest compression pauses during resuscitation and to strive for a 60% CCF.2 During resuscitation providers may stop chest compressions for reasons that are integral to patient care such as assessing heart rhythm and pulse, or defibrillation. Alternatively, providers may pause chest compressions for reasons that are not related to patient care such as distraction, fatigue, confusion, or inability to perform lifesaving skills efficiently. Because all types of pauses reduce the overall CCF this may lead to a confusing picture when CCF is analyzed in isolation and may explain the heterogeneous findings the Consensus on Science identified.2 Recent sub-analyses of clinical trial data found that patients with a higher CCF were less likely to survive.10,11 Of these, the CIRC trial evaluated the entire out-of-hospital resuscitation and calculated the CCF based on the full resuscitation rather than a shorter initial period (e.g., 5 min). The current study was conducted to investigate the effect of confounding variables on the ability of CCF to predict cardiac arrest survival using CIRC trial data. This might help to explain the conflicting findings in prior studies and clarify how best to use CCF when interpreting the results of future cardiac arrest studies.
Methods The CIRC trial was a multicenter randomized clinical trial comparing integrated manual and load distributing band (LDB) CPR with manual CPR alone in EMS treated adult out of hospital cardiac arrest (OHCA) of presumed cardiac etiology.12 Only data collected on the manual CPR arm during the CIRC trial were used to conduct this secondary analysis. This allowed us to better match the populations and settings used in prior studies on CCF that were the basis for the ILCOR consensus statement.2 CCF was calculated as part of the primary study and it was defined as the percentage of time when the patient received chest compressions during resuscitation. It was obtained from electronic EMS defibrillator files for each minute of the resuscitation by two reviewers who were blinded to outcome, but not study arm. CCF was calculated following previously published methodology.13,14 Any interruption in compressions longer than 1.5 s was considered a pause. CCF was calculated for all cases over the entire prehospital resuscitation. Any minute with ROSC was excluded from the CCF calculation, but if a patient re-arrested those minutes were included in the CCF calculation. ROSC was identified as periods where the patient had an organized rhythm, received no chest compressions, and study documentation indicated that the patient achieved a pulse generating rhythm during that time interval. For this analysis we used the previously calculated CCFs for all patients in the manual CPR arm of the CIRC trial. Two Sample Wilcoxon Rank Sum was used to determine if there was a statistical association between CCF and the dichotomous variables: gender, bystander CPR, public location, and witnessed arrest, shockable rhythm. Regression was used to determine if there was an association between CCF and the variables: age, resuscitation duration, study site, and number of shocks. Due to their non-normal distribution, age and number of shocks were analyzed as categorical variables. Univariate and multivariate logistic regressions were then conducted to determine the effect of CCF on survival to hospital discharge. Variables were included in the multivariate model if they were found to be statistically associated with survival and/or CCF. A 95% confidence interval that did not include unity was considered statistically significant.
The CIRC trial was reviewed by the Institutional Review Board or the Ethics Board at each participating institution with an Emergency Exception from Informed Consent.
Results There were 2132 patients enrolled in the parent trial who received manual compressions. Of those, 48 had incomplete chest compression data and 67 had no recorded chest compressions. No information about delivered shocks was available in 14 cases and survival data was missing in 7. A total of 1997 cases had complete data and were used for this secondary analysis. Table 1 illustrates the variables that were found to be associated with CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01–0.36) (Table 1). However, multivariate regression controlling for those factors that were significantly associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02–39.5).
Discussion This study found that without adjustment for other factors cases with a higher CCF had a lower rate of survival than those with a lower CCF. This result is counter intuitive given the common recommendation that providers maximize CCF when treating cardiac arrest patients. This result cannot be dismissed as an aberration since Christenson et al. had similar findings at their highest CCF rates,7 and the International Liaison Committee on Resuscitation found heterogeneity in the literature that they reviewed on CCF.2 The univariate finding that a higher CCF is associated with lower survival likely does not mean that CCF is not important for improved cardiac arrest survival. This is supported by our finding that after controlling for other variables that are associated with survival to hospital discharge, many of which by their nature also lower the CCF, higher CCF is predictive of survival to hospital discharge. This is probably because evaluating CCF in isolation does not separate the “good” pauses from the “bad” pauses. The univariate analysis of CCF’s relationship to survival does not differentiate between CCF’s that are diminished due to concurrent interventions that are life promoting and those that are not. A patient who is defibrillated and then goes into asystole may have a higher CCF than a case that remains in ventricular fibrillation and requires additional defibrillation. Patients who remain in ventricular fibrillation are more likely to survive than patients who immediately develop asystole even though they will have a lower CCF. The findings from this analysis may have implications for future cardiac arrest research. This study demonstrates that CCF should not be analyzed in isolation, but these findings may also have implications for future randomized clinical trials. Generally clinical trials use randomization to create equal groups that can then be used to compare an intervention. The population that is randomized has a specific disease and randomization is intended to distribute known and unknown confounders equally between the groups allowing researchers to determine the effect of an intervention on a specific disease typically without statistical adjustment. However, if the randomization is not successful, usually because of enrolling too few subjects, then the groups will not be equal and the findings of any unadjusted analysis will be suspect. This can happen even if there is an equal distribution of the known confounders between the study arms since the combinations of variables may not be equal (e.g., a similar proportion of patients with witnessed cardiac arrest and initial shockable rhythms, but different proportions of patients with both witnessed cardiac arrest and an initial shockable rhythm).
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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Table 1 Multivariate analysis of survival to hospital discharge. Included patients (n = 1997)
Association with CCF Coef (95% CI) or Z, p-value
Association with survival OR (95% CI)
Multivariate association with survival OR (95% CI)
0.07 (0.01–0.36)
6.34 (1.02–39.5)
Reference 1.01 (0.73–1.40) 0.48 (0.33–0.71) 1.21 (0.89–1.63) 3.51 (2.55–4.83) 3.16 (2.27–4.4) 5.02 (3.73–6.75)
Reference 1.06 (0.7–1.61) 0.51 (0.32–0.82) 1.02 (0.69–1.49) 2.0 (1.36–2.94) 1.74 (1.1–2.75) 2.54 (1.39–4.65)
.0005 (.0002 to .0008)
Reference 3.44 (2.33–5.08) 4.43 (3.0–6.56) 2.99 (1.85–4.82) 1.19 (0.62–2.30) 1.48 (1.07–2.03) 0.83 (0.82–0.85)
Reference 1.75 (0.95–3.2) 1.6 (0.78–3.28) 2.69 (1.18–6.15) 3.75 (1.3–10.9) 1.48 (0.87–2.51) 0.82 (0.80–0.85)
Reference −0.03 (−0.06 to −0.01) 0.04 (0.02 to 0.05) −0.01 (−0.02 to −0.001) −.029 (−0.05 to 0.01)
Reference 0.48 (0.16–1.4) 0.53 (0.31–0.90) 0.73 (0.49–1.08) 1.35 (0.67–2.7)
Reference 0.58 (0.16–2.04) 0.93 (0.49–1.79) 2.66 (1.48–4.8) 1.24 (0.50–3.04)
CCF Age 18–59 years 60–74 years
Male gender
62%
Reference −0.007 (−0.016 to 0.002) 0.004 (−0.005 to 0.013) Z = 2.25, p = 0.025
Witnessed arrest
47%
Z = 4.45, p < 0.001
Public location of the OHCA
13%
Z = 2.92, p < 0.004
Shockable rhythm
25%
Z = 6.15, p < 0.001
Number of EMS shocks No shocks 1 shock
58% 14%
Reference −0.02 (−0.031 to −0.009) −0.029 (−0.041 to −0.017) −0.034 (−0.048 to −0.017) −0.033 (−0.047 to −0.019) Z = −3.83, p < 0.001
75 years and older
34% 32% 34%
2–3 shocks
12%
4–5 shocks
8%
More than 5 shocks
8%
Received bystander chest compressions
22%
Median treatment duration (IQR)
21 (12–31)
Site IDa CIRC Site 1 CIRC Site 2
– –
CIRC Site 3
–
CIRC Site 4
–
CIRC Site 5
–
Coef: regression coefficient, Z: Wilcoxon Rank Sum test Z-value, OR: odds ratio, CI: confidence interval, IQR: interquartile range, SD: standard deviation, CCF: chest compression fraction. a The percent of included patients by site was not included so that the specific sites cannot be identified.
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In most cardiac arrest research investigators include all patients who present to EMS in cardiac arrest. This sampling technique creates a heterogeneous sample whose chances of survival are variable based on their condition and the treatments that they receive. This may lead to a failure of the randomization to equally distribute all confounders between the study groups equally. This can be seen in the demographic information presented in the recently published study by Nichol et al. on continuous or interrupted chest compressions, which had significant differences between the study arms for witnessed arrest and study site15 ; and in the CIRC trial a difference was seen in the proportion of cases with a shockable initial rhythm in each study arm.10 Controlling for confounders is critical in clinical trials, but it is particularly difficult in trials that include a broad pool of patients such as OHCA research. Investigators must either conduct studies with a more homogenous pool of patients or use statistical adjustments in addition to randomization to control for confounding. Using research in myocardial infarction as an example we see that those studies differentiate between the locations of the infarct; thus homogenizing the sample and ensuring there is no need for statistical adjustment in the final analysis.16–18 This may be an explanation for so many recent large clinical cardiac arrest trials showing no difference
in outcome when all of the pretrial data seems to indicate there should be one. The variability in the data may be drowning out the results. This study has limitations. It was a secondary analysis of a subset of patients from the CIRC trial. The CCF was calculated based on human reviewers determining the number of compressions per minute and the number of seconds without compressions. This process could have introduced a small amount of variability in the calculation, which likely broadened the range of CCF. Finally, we were unable to assess compression quality beyond rate or determine the reason for low CCF.
Conclusion This study found that CCF cannot be looked at in isolation as a predictor of cardiac arrest survival. It must be looked at in the context of other resuscitation activities. When controlling for the effect of other resuscitation activities a higher CCF is predictive of survival. This may explain the heterogeneity of findings in studies that look at the effect of CCF on cardiac arrest survival.
Please cite this article in press as: Wik L, et al. Why do some studies find that CPR fraction is not a predictor of survival? Resuscitation (2016), http://dx.doi.org/10.1016/j.resuscitation.2016.04.013
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Conflict of interest statement
The authors have no relevant financial conflicts of interest with 206 the exception of Ulrich R. Herken who is an employee of ZOLL Med207 ical and Lars Wik who was the PI for the CIRC study and represents 208 the Norwegian National Advisory Unit on Prehospital Emergency 209 Medicine (NAKOS) in the Medical Advisory Board of Physio-Control. 210 Q3 All other author’s institutions received funding from ZOLL Medical 211 for their time on the CIRC study. Olsen was partly funded by an 212 unrestricted grant from Norwegian Health Region South-East and 213 partly by a research grant from ZOLL Medical to NAKOS. 205
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Funding
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ZOLL Medical Corporation funded the CIRC Trial. ZOLL funded, but did not participate in, the collection, management, or primary analysis of the data. An employee of ZOLL (UH) participated in the development of this manuscript and is listed as an author. All other authors’ institutions received funding from ZOLL for their participation in the trial.
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Acknowledgements
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The authors would like to acknowledge the EMS providers who contributed to this study as well as other individuals who made this study possible. We thank the coordinators and monitors at each of the participating sites for their careful and persistent work with the data collection.
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References
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