Elsevier

Resuscitation

Volume 122, January 2018, Pages 19-24
Resuscitation

Clinical paper
Predicting ROSC in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: Is trend-detection instead of absolute threshold values the key?

https://doi.org/10.1016/j.resuscitation.2017.11.040Get rights and content

Abstract

Aim

Guidelines recommend detecting return of spontaneous circulation (ROSC) by a rising concentration of carbon dioxide in the exhalation air. As CO2 is influenced by numerous factors, no absolute cut-off values of CO2 to detect ROSC are agreed on so far. As trends in CO2 might be less affected by influencing factors, we investigated an approach which is based on detecting CO2-trends in real-time.

Methods

We conducted a retrospective case-control study on 169 CO2 time series from out of hospital cardiac arrests resuscitated by Muenster City Ambulance-Service, Germany. A recently developed statistical method for real-time trend-detection (SCARM) was applied to each time series. For each series, the percentage of time points with detected positive and negative trends was determined.

Results

ROSC time series had larger percentages of positive trends than No-ROSC time series (p = 0.003). The median percentage of positive trends was 15% in the ROSC time series (IQR: 5% to 23%) and 7% in the No-ROSC time series (IQR: 3% to 14%). A receiver operating characteristic (ROC) analysis yielded an optimal threshold of 13% to differentiate between ROSC and No-ROSC cases with a specificity of 58.4% and sensitivity of 73.9%; the area under the curve was 63.5%.

Conclusion

Patients with ROSC differed from patients without ROSC as to the percentage of detected CO2 trends, indicating the potential of our real-time trend-detection approach. Since the study was designed as a proof of principle and its calculated specificity and sensitivity are low, more research is required to implement CO2-trend-detection into clinical use.

Introduction

The primary aim of cardiopulmonary resuscitation (CPR) in cardiac arrest is to achieve the return of spontaneous circulation (ROSC). The treatment of cardiac arrest increased in the last years, which results in improved patients’ outcome [1], [2]. The European Resuscitation Council Guidelines offer profound expertise how cardiac arrest should be treated [3]. Thereby, high-quality CPR demands uninterrupted chest compression [4] but timely detection of ROSC is required to stop CPR and avoid unnecessary administration of drugs.

Although multiple approaches have been made and proposed to predict and detect that ROSC was achieved, it is still a challenge [5], [6]. Particularly in out of hospital cardiac arrests (OHCA), which remain a leading cause of death worldwide, with exemplarily more than 300,000 deaths per year in the United States of America [7], the detection of ROSC is intricate.

Thus, the current guideline recommends considering different aspects when checking for ROSC. Apart from the return of signs of life, it is recommended to analyse the heart rhythm, check for a pulse and to evaluate carbon dioxide (CO2) [8]. Waveform capnography depicts the end-tidal CO2 in real-time and can be used continuously during CPR without the need to interrupt chest compressions. As CO2 indicates changes of cardiac output and pulmonary blood flow, it is bound to increase with improved cardiac output in ROSC. Several studies could demonstrate that patients experiencing ROSC had a significantly higher CO2 once they had ROSC and higher CO2 than patients without achieving ROSC [9], [10], [11], [12], [13].

Furthermore, there is weak evidence that a rising CO2 might be appropriate to forecast whether a ROSC will be obtained or not [14], [15], [16], [17]. However, there are no commonly agreed absolute CO2 values that can be used for prognosis. Thus, in the currently proposed Termination of Resuscitation (TOR) criteria, CO2 is not included [18], [19]. Nevertheless, the Resuscitation Council recommends considering CO2 levels as one aspect in the multifarious decision process [8].

Additionally to the factors which generally influence CO2 in ventilated patients (e.g. volume and frequency of ventilation, patients weight), CO2 is also influenced by the cause of cardiac arrest (e.g. higher values after an initial asphyxia arrest) and by the quality of CPR (e.g. quality of chest compressions, time between cardiac arrest and start of CPR) [16], [20], [21]. Hence, it might be promising not to consider absolute ETCO2 values but the trends in CO2 during CPR instead.

In this paper we investigate the potential of an approach which is based on detecting CO2 trends in real-time. We conducted a retrospective case-control study on 169 CO2 time series from out of hospital cardiac arrests. A recently developed statistical method for real-time trend-detection (SCARM) was applied to each time series. For each time series, the percentages of time points with detected positive and negative trends were determined. As a proof of principle, our main objective was to test if ROSC cases show a larger percentage of detected trends than No-ROSC cases.

Section snippets

Study design and background

In this case-control study, we analysed retrospectively CO2 time series measured during resuscitations in out of hospital cardiac arrests (OHCA), which occurred in the city of Muenster, Germany, between May 1st 2010 and December 31th 2013. The emergency medical service in the study-region is a physician based system. Ambulances and physicians’ cars of the Muenster Fire Brigade provide emergency medical care to a population of about 300,000 using a rendezvous system. Cases of cardiac arrest are

Patient and CPR characteristics

After applying the inclusion and exclusion criteria to the 671 resuscitations in OHCA, which occurred between May 1st 2010 and December 31th 2013, 169 cases remained with 77 cases in the ROSC and 92 in the No-ROSC group, as depicted in Fig. 1. Out of the 77 ROSC-cases, 65 patients had ROSC at hospital admission to the emergency department (ROSC to ED) and 12 patients maintained ROSC only for a short period (any ROSC). Patients’ characteristics are shown in the Utstein-style-protocol in Table 1.

Discussion

The present study applies a currently developed statistical method for the real-time detection of trends in time series of CO2 levels in CPR cases. We observed that during CPR patients who experienced ROSC had more positive than negative trends and that patients who experienced ROSC had more positive trends than patients who did not.

Investigating trends instead of absolute CO2 values might be meaningful, as absolute CO2 levels during CPR are influenced by diverse aspects not being standardized

Conclusions

Patients with ROSC differed from patients without ROSC as to the percentage of detected CO2 trends, indicating the potential of our real-time trend-detection approach. Since the study was designed as a proof of principle and its calculated specificity and sensitivity are low, considerably more research is required to implement CO2-trend-detection into clinical use.

Conflicts of interest

The authors received no funding and declare not to have any conflicts of interest.

References (27)

Cited by (0)

A Spanish translated version of the abstract of this article appears as Appendix in the final online version at https://doi.org/10.1016/j.resuscitation.2017.11.040.

1

The first two authors contributed equally and share first authorship.

View full text