Mathematical modeling for prediction of survival from resuscitation based on computerized continuous capnography: proof of concept

Abstract

Objectives: The objective was to describe a new method of studying correlations between real-time end tidal carbon dioxide (ETCO(2) ) data and resuscitation outcomes.

Methods: This was a prospective cohort study of 30 patients who underwent cardiopulmonary resuscitation (CPR) in a university hospital. Sidestream capnograph data were collected during CPR and analyzed by a mathematician blinded to patient outcome. The primary outcome measure was to determine whether a meaningful relationship could be drawn between detailed computerized ETCO(2) characteristics and the return of spontaneous circulation (ROSC). Significance testing was performed for proof-of-concept purposes only.

Results: Median patient age was 74 years (interquartile range [IQR] = 60-80 years; range = 16-92 years). Events were mostly witnessed (63%), with a median call-to-arrival time of 150 seconds (IQR = 105-255 seconds; range = 60-300 seconds). The incidence of ROSC was 57% (17 of 30), and of hospital discharge 20% (six of 30). Ten minutes after intubation, patients with ROSC had higher peak ETCO(2) values (p = 0.035), larger areas under the ETCO(2) curve (p = 0.016), and rising ETCO(2) slopes versus flat or falling slopes (p = 0.016) when compared to patients without ROSC. Cumulative maxETCO(2) > 20 mm Hg at all time points measured between 5 and 10 minutes postintubation best predicted ROSC (sensitivity = 0.88; specificity = 0.77; p < 0.001). Mathematical modeling targeted toward avoiding misdiagnosis of patients with recovery potential (fixed condition, false-negative rate = 0) demonstrated that cumulative maxETCO(2) (at 5-10 minutes) > 25 mm Hg or a slope greater than 0 measured between 0 and 8 minutes correctly predicted patient outcome in 70% of cases within less than 10 minutes of intubation.

Conclusions: This preliminary study suggests that computerized ETCO(2) carries potential as a tool for early, real-time decision-making during some resuscitations.