A novel technique to assess the quality of ventilation during pre-hospital cardiopulmonary resuscitation

Background: Devices that measure ventilation in the pre-hospital setting are deficient especially during early cardiopulmonary resuscitation (CPR) before placement of an advanced airway. Consequently, evidence is limited regarding the role of ventilation during early CPR and its effect on outcomes. Objective: To develop software that automatically identifies ventilation waveforms recorded by defibrillators based on changes in transthoracic impedance during standard CPR. Methods: This was an observational, retrospective analysis of non-traumatic pre-hospital cardiac arrest patients who received 30:2 CPR by emergency medical service rescuers. Data was collected from 550 cases recorded by the bioimpedance channel of defibrillators. Two expert clinicians independently assessed all episodes from the time of initial CPR until placement of an advanced airway, defined acceptable ventilation waveforms, and annotated the pauses between compressions with ventilation waveforms. We then developed software that incorporated the expert criteria and automatically annotated pauses with acceptable ventilations. Results: A total of 7396 pauses were analyzed, mean(SD) duration of 30:2 CPR was 13 (8) min, with 13 (10) pauses/patient, and mean pause duration of 6 (3) s. Reviewer 1 and reviewer 2 identified 2375 and 2249 pauses with any acceptable ventilation, respectively, with an inter-rater reliability of 0.94. The novel software program reproduced expert annotation with excellent agreement ( > 0.8) and high accuracy, both sensitivity and specificity above 90%, compared to two reviewers. The software presented a substantial agreement with the reviewers (kappa > 0.73) for ventilation counts in the pauses. Conclusion: We developed a novel and reliable strategy that enables investigation of ventilation quality during standard CPR using thoracic bioimpedance. This strategy would allow a timely and reliable automatic annotation of large scale resuscitation datasets.