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BACKGROUND: Significant challenges exist in measuring ventilation quality during out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Since ventilation is associated with outcomes in cardiac arrest, tools that objectively describe ventilation dynamics are needed. We sought to characterize thoracic impedance (TI) oscillations associated with ventilation waveforms in the Pragmatic Airway Resuscitation Trial (PART). METHODS: We analyzed CPR process files collected from adult OHCA enrolled in PART. We limited the analysis to cases with simultaneous capnography ventilation recordings at the Dallas-Fort Worth site. We identified ventilation waveforms in the thoracic impedance signal by applying automated signal processing with adaptive filtering techniques to remove overlying artifacts from chest compressions. We correlated detected ventilations with the end-tidal capnography signals. We determined the amplitudes (Ai, Ae) and durations (Di, De) of both insufflation and exhalation phases. We compared differences between laryngeal tube (LT) and endotracheal intubation (ETI) airway management during mechanical or manual chest compressions using Mann-Whitney U-test. RESULTS: We included 303 CPR process cases in the analysis; 209 manual (77 ETI, 132 LT), 94 mechanical (41 ETI, 53 LT). Ventilation Ai and Ae were higher for ETI than LT in both manual (ETI: Ai 0.71Ω, Ae 0.70Ω vs LT: Ai 0.46Ω, Ae 0.45Ω; p

Ventilation control is important during resuscitation from out-of-hospital cardiac arrest (OHCA). We compared different methods for calculating ventilation rates (VR) during OHCA.We analyzed data from the Pragmatic Airway Resuscitation Trial, identifying ventilations through capnogram recordings. We determined VR by: 1) counting the number of breaths within a time epoch ("counted" VR), and 2) calculating the mean of the inverse of measured time between breaths within a time epoch ("measured" VR). We repeated the VR estimates using different time epochs (10, 20, 30, 60 seconds). We defined hypo- and hyperventilation as VR6 and12 breaths/min, respectively. We assessed differences in estimated hypo- and hyperventilation with each VR measurement technique.Of 3,004 patients, data were available for 1,010. With the counted method, total hypoventilation increased with longer time epochs ([10-second epoch: 75 seconds hypoventilation] to [60-second epoch: 97 seconds hypoventilation]). However, with the measured method, total hypoventilation decreased with longer time epochs ([10-second epoch: 223 seconds hypoventilation] to [60-second epoch: 150 seconds hypoventilation]). With the counted method, the total duration of hyperventilation decreased with longer time epochs ([10-second epochs: 35 seconds hyperventilation] to [60-second epoch: 0 seconds hyperventilation]). With the measured method, total hyperventilation decreased with longer time epochs ([10-second epoch: 78 seconds hyperventilation] to [60-second epoch: 0 seconds hyperventilation]). Differences between the measured and counted estimates were smallest with a 60-second time epoch.Quantifications of hypo- and hyperventilation vary with the applied measurement methods. Measurement methods are important when characterizing ventilation rates in OHCA.

We sought to describe ventilation rates during out-of-hospital cardiac arrest (OHCA) resuscitation and their associations with airway management strategy and outcomes.We analyzed continuous end-tidal carbon dioxide capnography data from adult OHCA enrolled in the Pragmatic Airway Resuscitation Trial (PART). Using automated signal processing techniques, we determined continuous ventilation rates for consecutive 10-second epochs after airway insertion. We defined hypoventilation as a ventilation rate 6 breaths/min. We defined hyperventilation as a ventilation rate 12 breaths/min. We compared differences in total and percentage post-airway hyper- and hypoventilation between airway interventions (laryngeal tube (LT) vs. endotracheal intubation (ETI)). We also determined associations between hypo-/hyperventilation and OHCA outcomes (ROSC, 72-hour survival, hospital survival, hospital survival with favorable neurologic status).Adequate post-airway capnography were available for 1,010 (LT n = 714, ETI n = 296) of 3,004 patients. Median ventilation rates were: LT 8.0 (IQR 6.5-9.6) breaths/min, ETI 7.9 (6.5-9.7) breaths/min. Total duration and percentage of post-airway time with hypoventilation were similar between LT and ETI: median 1.8 vs. 1.7 minutes, p = 0.94; median 10.5% vs. 11.5%, p = 0.60. Total duration and percentage of post-airway time with hyperventilation were similar between LT and ETI: median 0.4 vs. 0.4 minutes, p = 0.91; median 2.1% vs. 1.9%, p = 0.99. Hypo- and hyperventilation exhibited limited associations with OHCA outcomes.In the PART Trial, EMS personnel delivered post-airway ventilations at rates satisfying international guidelines, with only limited hypo- or hyperventilation. Hypo- and hyperventilation durations did not differ between airway management strategy and exhibited uncertain associations with OCHA outcomes.

Introduction: Controlled ventilation is important in OHCA resuscitation, but there are few validated methods for accurate detection of ventilations. We sought to utilize changes in thoracic impedance (TI) to characterize resuscitation ventilations in the Pragmatic Airway Resuscitation Trial (PART). Methods: We analyzed CPR process files collected from adult OHCA enrolled in PART. We limited the analysis to cases with simultaneous capnography ventilation recordings at the Dallas-Ft Worth site. We identified ventilation waveforms in the thoracic impedance signal by applying automated signal processing with adaptive filtering techniques to remove overlying artifacts from chest compressions. We correlated detected ventilations with the end-tidal capnography signal. We determined the amplitudes (Ai, Ae) and durations (Di, De) of both insufflation and exhalation phases of the ventilation impedance signal (Figure 1). We compared differences between laryngeal tube (LT) and endotracheal intubation (ETI) airway management during mechanical or manual chest compressions using Mann-Whitney U-test. Results: We included 303 CPR process cases in the analysis; 209 manual (77 ETI, 132 LT), 94 mechanical (41 ETI, 53 LT). Ventilation Ai and Ae were higher for ETI than LT in both manual (ETI: Ai 0.71Ω, Ae 0.70Ω vs LT: Ai 0.46Ω Ae 0.45Ω, p Conclusion: Compared with LT, ETI thoracic impedance ventilation insufflation and exhalation amplitude were higher while duration did not differ. TI may provide a novel approach to characterizing ventilation during OHCA.

Background Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services. Aim To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources. Methods A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated. Results Full-episode median errors below 2% in CCF, 1 min−1 in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors ( > 10% in CCF and interruption ratio, and > 10 min−1 in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval. Conclusions An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.

Cardiopulmonary resuscitation (CPR) is a core therapy to treat out-of-hospital cardiac arrest (OHCA). Thoracic impedance (TI) can be used to assess ventilations during CPR, but the signal is also affected by chest compression (CC) artifacts. This study presents a method for TI-based ventilation detection during concurrent manual CCs. Data from 152 OHCA patients were analyzed. A total of 423 TI segments of at least 60 s during ongoing CCs were extracted. True ventilations were annotated using the capnogram. The final dataset comprised 1210 min of TI recordings and 9665 ground truth ventilations. A three-stage detection algorithm was developed. First, the TI signal was filtered to obtain ventilation waveforms, including a least mean squares filter to remove artifacts due to CCs. Potential ventilations were then identified with a heuristic detector and characterized by a set of 16 features. These were finally fed to a random forest classifier to discriminate between true ventilations and false positives. Patients were split into 100 distinct training (70%) and test (30%) partitions. The median (interquartile range) sensitivity, PPV and F-score were 83.9 (70.2-91.2) %, 86.1 (75.0-93.3) % and 84.3 (72.1-91.4) %. Our method would allow feedback on ventilation rates as well as surrogate measures of insufflated air volume during CPR.

Chest compression (CC) quality is associated with improved out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Airway management efforts may adversely influence CC quality. We sought to compare the effects of initial laryngeal tube (LT) and initial endotracheal intubation (ETI) airway management strategies upon chest compression fraction (CCF), rate and interruptions in the Pragmatic Airway Resuscitation Trial (PART).We analyzed CPR process files collected from adult OHCA enrolled in PART. We used automated signal processing techniques and a graphical user interface to calculate CC quality measures and defined interruptions as pauses in chest compressions longer than 3 s. We determined CC fraction, rate and interruptions (number and total duration) for the entire resuscitation and compared differences between LT and ETI using t-tests. We repeated the analysis stratified by time before, during and after airway insertion as well as by successive 3-min time segments. We also compared CC quality between single vs. multiple airway insertion attempts, as well as between bag-valve-mask (BVM-only) vs. ETI or LT.Of 3004 patients enrolled in PART, CPR process data were available for 1996 (1001 LT, 995 ETI). Mean CPR analysis duration were: LT 22.6 ± 10.8 min vs. ETI 25.3 ± 11.3 min (p 0.001). Mean CC fraction (LT 88% vs. ETI 87%, p = 0.05) and rate (LT 114 vs. ETI 114 compressions per minute (cpm), p = 0.59) were similar between LT and ETI. Median number of CC interruptions were: LT 11 vs. ETI 12 (p = 0.001). Total CC interruption duration was lower for LT than ETI (LT 160 vs. ETI 181 s, p = 0.002); this difference was larger before airway insertion (LT 56 vs. ETI 78 s, p 0.001). There were no differences in CC quality when stratified by 3-min time epochs.In the PART trial, compared with ETI, LT was associated with shorter total CC interruption duration but not other CC quality measures. CC quality may be associated with OHCA airway management.

Background: Ventilations during out-of-hospital cardiac arrest (OHCA) produce thoracic impedance(TI) waveforms due to air volume changes in the lungs. Different airway management techniques, i.e. laryngeal tube (LT) and endotracheal intubation (ETI), may produce distinct TI waveforms as a result of different air flows and dead space volumes. Methods: Adult OHCA cases from the Pragmatic Airway Resuscitation Trial were analyzed. Cases recorded with Philips MRx monitor-defibrillators and treated with LUCAS mechanical CPR devices were considered, ensuring homogeneous TI acquisition and compression therapy. Impedance and capnogram signal intervals were extracted after successful airway insertion and during ongoing chest compressions. Ventilations were confirmed in the capnogram, and the associated TI waveforms were analyzed. Adaptive filtering was applied to remove compression artifacts, and the amplitudes (A i , A e ) and durations (D i , D e ) of the insufflation and exhalation phases were computed (see Figure). Each case was characterized by its observed median values. Differences between airway groups were assessed with a Wilcoxon rank sum test. Results: Data from 100 OHCA cases (57 LT and 43 ETI) were analyzed, totaling 10348 ventilations, with median (IQR) of 87 (51 - 146) ventilations per case. Median TI amplitudes for ETI and LT groups showed significant differences (pi , and 1.0 (0.7 - 1.6) Ω and 0.6 (0.3 - 1.2) Ω for A e . No significant differences were observed for phase durations, 1.6 (1.3 - 2.0) s and 1.6 (1.2 - 1.8) s for D i , and 2.3 (1.8 - 3.3) s and 2.6 (2.0 - 3.3) s for D e . Conclusions: Significant differences on ventilation impedance waveform amplitudes were observed between patients treated with ETI and LT. This might be related to higher insufflated air volumes for ETI or larger dead space volumes for LT.

Background: Chest compression (CC) quality is associated with improved out-of-hospital (OHCA) outcomes. Airway management efforts may adversely influence CC quality. We sought to compare the effects of initial laryngeal tube (LT) and initial endotracheal intubation (ETI) airway management strategies upon chest compression fraction (CCF), rate and interruptions in the Pragmatic Airway Resuscitation Trial (PART). Methods: We analyzed CPR process files collected from adult OHCA enrolled in PART. We used automated signal processing techniques (MATLAB) to identify and calculate CC quality measures. We defined a CC interruption as ≥3 second pause in chest compressions. We determined CC fraction, rate and interruptions (number and total duration) in 1-minute epochs for the entire resuscitation. We compared differences in CC quality between LT and ETI using t-tests. We repeated the analysis stratified by time before/after airway insertion as well as by successive 3-minute time segments. We also compared CC quality between single vs. multiple airway insertion attempts, as well as between BVM-only vs. ETI or LT. Results: Of 3,004 patients enrolled in PART, CPR process data were available for 1,996 (1,001 LT, 995 ETI). Mean resuscitation duration was: LT 22.6±10.8 min, ETI 25.3±11.3 min. Mean CC fraction (88% vs. 87%, p=0.045) and rate (114 vs. 114 bpm, p=0.59) were similar between LT and ETI. Median number of CC interruptions was similar between LT and ETI (11 vs. 12). Total CC interruption duration was lower for LT than ETI (160 vs. 181 sec, p=0.002); this difference was largest before airway insertion (56 vs. 78 sec, p=0.001). There were no differences in CC quality when stratified by 3-min time epochs. Total CC interruption duration was shorter for single than multiple airway insertion attempts (127 vs. 153 sec, p=0.005). Total CC interruption duration was shorter for BVM-only than LT or ETI (104 vs. 135 sec, p Conclusion: In PART, compared with ETI, LT was associated with shorter total CC interruption duration but not other CC quality measures. Single-attempt airway insertion and BVM-only were associated with shorter total CC interruption duration. OHCA airway management may influence CC quality.

Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO $_{2}$ channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F $_{1}$ -score of 96.32 (96.26–96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (−0.334–0.572) min−1 and 0.05 (−3.71–9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.

Introduction: Resuscitation from out-of-cardiac arrest (OHCA) requires control of both chest compressions and lung ventilation. There are few effective methods for detecting ventilations during cardiopulmonary resuscitation. Thoracic impedance (TI) is sensitive to changes in lung air volumes and may allow detection of ventilations but has not been tested with concurrent mechanical chest compressions. Hypothesis: It is possible to automatically detect and characterize ventilations from TI changes during mechanical chest compressions. Methods: A cohort of 420 OHCA cases (27 survivors to hospital discharge) were enrolled in the Dallas-Fort Worth Center for Resuscitation Research cardiac arrest registry. These patients were treated with the LUCAS-2 CPR device and had concurrent TI and capnogram recordings from MRx (Philips, Andover, MA) monitor-defibrillators. We developed a signal processing algorithm to suppress chest compression artifacts from the TI signal, allowing identification of ventilations. We used the capnogram as gold standard for delivered ventilations. We determined the accuracy of the algorithm for detecting capnogram-indicated ventilations, calculating sensitivity, the proportion of true ventilations detected in the TI, and positive predictive value (PPV), the proportion of true ventilations within the detections. We calculated per minute ventilation rate and mean TI amplitude, as surrogate for tidal volume. Statistical differences between survivors and non-survivors were assessed using the Mann-Whitney test. Results: We studied 4331 minutes of TI during CPR. There were a median of 10 (IQR 6-14) ventilations per min and 52 (30-81) ventilations per patient. Sensitivity of TI was 95.9% (95% CI, 74.5-100), and PPV was 95.8% (95% CI, 80.0-100). The median ventilation rates for survivors and non-survivors were 7.75 (5.37-9.91) min -1 and 5.64 (4.46-7.15) min -1 (p-3 ), and the median TI amplitudes were 1.33 (1.03-1.75) Ω and 1.14 (0.77-1.66) Ω (p=0.095). Conclusions: An accurate automatic TI ventilation detection algorithm was demonstrated during mechanical CPR. The relation between ventilation rate during mechanical CPR and survival was significant, but it was not for impedance amplitude.