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Prehosp Disaster Med. 2011 Jun;26(3):148-50. doi: 10.1017/S1049023X11006376.

End-tidal CO2 as a predictor of survival in out-of-hospital cardiac arrest.

Author information

1
Department of Emergency Medicine, University of Southern California, Los Angeles, CA, USA. eckstein@usc.edu

Abstract

OBJECTIVE:

The objective of this study was to evaluate initial end-tidal CO2 (EtCO2) as a predictor of survival in out-of-hospital cardiac arrest.

METHODS:

This was a retrospective study of all adult, non-traumatic, out-of-hospital, cardiac arrests during 2006 and 2007 in Los Angeles, California. The primary outcome variable was attaining return of spontaneous circulation (ROSC) in the field. All demographic information was reviewed and logistic regression analysis was performed to determine which variables of the cardiac arrest were significantly associated with ROSC.

RESULTS:

There were 3,121 cardiac arrests included in the study, of which 1,689 (54.4%) were witnessed, and 516 (16.9%) were primary ventricular fibrillation (VF). The mean initial EtCO2 was 18.7 (95%CI = 18.2-19.3) for all patients. Return of spontaneous circulation was achieved in 695 patients (22.4%) for which the mean initial EtCO2 was 27.6 (95%CI = 26.3-29.0). For patients who failed to achieve ROSC, the mean EtCO2 was 16.0 (95%CI = 15.5-16.5). The following variables were significantly associated with achieving ROSC: witnessed arrest (OR = 1.51; 95%CI = 1.07-2.12); initial EtCO2 >10 (OR = 4.79; 95%CI = 3.10-4.42); and EtCO2 dropping <25% during the resuscitation (OR = 2.82; 95%CI = 2.01-3.97).The combination of male gender, lack of bystander cardiopulmonary resuscitation, unwitnessed collapse, non-vfib arrest, initial EtCO2 ≤10 and EtCO2 falling > 25% was 97% predictive of failure to achieve ROSC.

CONCLUSIONS:

An initial EtCO2 >10 and the absence of a falling EtCO2 >25% from baseline were significantly associated with achieving ROSC in out-of-hospital cardiac arrest. These additional variables should be incorporated in termination of resuscitation algorithms in the prehospital setting.

PMID:
22107764
DOI:
10.1017/S1049023X11006376
[Indexed for MEDLINE]

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