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

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.

Keywords: Out of hospital cardiac arrest; Real-time; Repeated median regression; Return of spontaneous circulation (ROSC); Trend detection.