Temporal Trends in End-Tidal Capnography and Outcomes in Out-of-Hospital Cardiac Arrest: A Secondary Analysis of a Randomized Clinical Trial.

Importance: While widely measured, the time-varying association between exhaled end-tidal carbon dioxide (EtCO2) and out-of-hospital cardiac arrest (OHCA) outcomes is unclear. Objective: To evaluate temporal associations between EtCO2 and return of spontaneous circulation (ROSC) in the Pragmatic Airway Resuscitation Trial (PART). Design, Setting, and Participants: This study was a secondary analysis of a cluster randomized trial performed at multicenter emergency medical services agencies from the Resuscitation Outcomes Consortium. PART enrolled 3004 adults (aged ≥18 years) with nontraumatic OHCA from December 1, 2015, to November 4, 2017. EtCO2 was available in 1172 cases for this analysis performed in June 2023. Interventions: PART evaluated the effect of laryngeal tube vs endotracheal intubation on 72-hour survival. Emergency medical services agencies collected continuous EtCO2 recordings using standard monitors, and this secondary analysis identified maximal EtCO2 values per ventilation and determined mean EtCO2 in 1-minute epochs using previously validated automated signal processing. All advanced airway cases with greater than 50% interpretable EtCO2 signal were included, and the slope of EtCO2 change over resuscitation was calculated. Main Outcomes and Measures: The primary outcome was ROSC determined by prehospital or emergency department palpable pulses. EtCO2 values were compared at discrete time points using Mann-Whitney test, and temporal trends in EtCO2 were compared using Cochran-Armitage test of trend. Multivariable logistic regression was performed, adjusting for Utstein criteria and EtCO2 slope. Results: Among 1113 patients included in the study, 694 (62.4%) were male; 285 (25.6%) were Black or African American, 592 (53.2%) were White, and 236 (21.2%) were another race; and the median (IQR) age was 64 (52-75) years. Cardiac arrest was most commonly unwitnessed (n = 579 [52.0%]), nonshockable (n = 941 [84.6%]), and nonpublic (n = 999 [89.8%]). There were 198 patients (17.8%) with ROSC and 915 (82.2%) without ROSC. Median EtCO2 values between ROSC and non-ROSC cases were significantly different at 10 minutes (39.8 [IQR, 27.1-56.4] mm Hg vs 26.1 [IQR, 14.9-39.0] mm Hg; P < .001) and 5 minutes (43.0 [IQR, 28.1-55.8] mm Hg vs 25.0 [IQR, 13.3-37.4] mm Hg; P < .001) prior to end of resuscitation. In ROSC cases, median EtCO2 increased from 30.5 (IQR, 22.4-54.2) mm HG to 43.0 (IQR, 28.1-55.8) mm Hg (P for trend < .001). In non-ROSC cases, EtCO2 declined from 30.8 (IQR, 18.2-43.8) mm Hg to 22.5 (IQR, 12.8-35.4) mm Hg (P for trend < .001). Using adjusted multivariable logistic regression with slope of EtCO2, the temporal change in EtCO2 was associated with ROSC (odds ratio, 1.45 [95% CI, 1.31-1.61]). Conclusions and Relevance: In this secondary analysis of the PART trial, temporal increases in EtCO2 were associated with increased odds of ROSC. These results suggest value in leveraging continuous waveform capnography during OHCA resuscitation. Trial Registration: ClinicalTrials.gov Identifier: NCT02419573.

Bag-Valve-Mask Ventilation and Survival From Out-of-Hospital Cardiac Arrest: A Multicenter Study.

BACKGROUND: Few studies have measured ventilation during early cardiopulmonary resuscitation (CPR) before advanced airway placement. Resuscitation guidelines recommend pauses after every 30 chest compressions to deliver ventilations. The effectiveness of bag-valve-mask ventilation delivered during the pause in chest compressions is unknown. We sought to determine: (1) the incidence of lung inflation with bag-valve-mask ventilation during 30:2 CPR; and (2) the association of ventilation with outcomes after out-of-hospital cardiac arrest. METHODS: We studied patients with out-of-hospital cardiac arrest from 6 sites of the Resuscitation Outcomes Consortium CCC study (Trial of Continuous Compressions versus Standard CPR in Patients with Out-of-Hospital Cardiac Arrest). We analyzed patients assigned to the 30:2 CPR arm with ≥2 minutes of thoracic bioimpedance signal recorded with a cardiac defibrillator/monitor. Detectable ventilation waveforms were defined as having a bioimpedance amplitude ≥0.5 Ω (corresponding to ≥250 mL VT) and a duration ≥1 s. We defined a chest compression pause as a 3- to 15-s break in chest compressions. We compared the incidence of ventilation and outcomes in 2 groups: patients with ventilation waveforms in <50% of pauses (group 1) versus those with waveforms in ≥50% of pauses (group 2). RESULTS: Among 1976 patients, the mean age was 65 years; 66% were male. From the start of chest compressions until advanced airway placement, mean±SD duration of 30:2 CPR was 9.8±4.9 minutes. During this period, we identified 26 861 pauses in chest compressions; 60% of patients had ventilation waveforms in <50% of pauses (group 1, n=1177), and 40% had waveforms in ≥50% of pauses (group 2, n=799). Group 1 had a median of 12 pauses and 2 ventilations per patient versus group 2, which had 12 pauses and 12 ventilations per patient. Group 2 had higher rates of prehospital return of spontaneous circulation (40.7% versus 25.2%; P<0.0001), survival to hospital discharge (13.5% versus 4.1%; P<0.0001), and survival with favorable neurological outcome (10.6% versus 2.4%; P<0.0001). These associations persisted after adjustment for confounders. CONCLUSIONS: In this study, lung inflation occurred infrequently with bag-valve-mask ventilation during 30:2 CPR. Lung inflation in ≥50% of pauses was associated with improved return of spontaneous circulation, survival, and survival with favorable neurological outcome.

Impedance-Based Ventilation Detection and Signal Quality Control During Out-of-Hospital Cardiopulmonary Resuscitation.

Feedback on ventilation could help improve cardiopulmonary resuscitation quality and survival from out-of-hospital cardiac arrest (OHCA). However, current technology that monitors ventilation during OHCA is very limited. Thoracic impedance (TI) is sensitive to air volume changes in the lungs, allowing ventilations to be identified, but is affected by artifacts due to chest compressions and electrode motion. This study introduces a novel algorithm to identify ventilations in TI during continuous chest compressions in OHCA. Data from 367 OHCA patients were included, and 2551 one-minute TI segments were extracted. Concurrent capnography data were used to annotate 20724 ground truth ventilations for training and evaluation. A three-step procedure was applied to each TI segment: First, bidirectional static and adaptive filters were applied to remove compression artifacts. Then, fluctuations potentially due to ventilations were located and characterized. Finally, a recurrent neural network was used to discriminate ventilations from other spurious fluctuations. A quality control stage was also developed to anticipate segments where ventilation detection could be compromised. The algorithm was trained and tested using 5-fold cross-validation, and outperformed previous solutions in the literature on the study dataset. The median (interquartile range, IQR) per-segment and per-patient F 1-scores were 89.1 (70.8-99.6) and 84.1 (69.0-93.9), respectively. The quality control stage identified most low performance segments. For the 50% of segments with highest quality scores, the median per-segment and per-patient F 1-scores were 100.0 (90.9-100.0) and 94.3 (86.5-97.8). The proposed algorithm could allow reliable, quality-conditioned feedback on ventilation in the challenging scenario of continuous manual CPR in OHCA.

Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest.

BACKGROUND: Prompt defibrillation is key to successful resuscitation from ventricular fibrillation out-of-hospital cardiac arrest (VF-OHCA). Preliminary evidence suggests that the timing of shock relative to the amplitude of the VF ECG waveform may affect the likelihood of resuscitation. We investigated whether the VF waveform amplitude at the time of shock (instantaneous amplitude) predicts outcome independent of other validated waveform measures. METHODS: We conducted a retrospective study of VF-OHCA patients ≥18 old. We evaluated three VF waveform measures for each shock: instantaneous amplitude at the time of shock, and maximum amplitude and amplitude spectrum area (AMSA) over a 3-s window preceding the shock. Linear mixed-effects modeling was used to determine whether instantaneous amplitude was associated with shock-specific return of organized rhythm (ROR) or return of spontaneous circulation (ROSC) independent of maximum amplitude or AMSA. RESULTS: The 566 eligible patients received 1513 shocks, resulting in ROR of 62.0% (938/1513) and ROSC of 22.3% (337/1513). In unadjusted regression, an interquartile increase in instantaneous amplitude was associated with ROR (Odds ratio [OR] [95% confidence interval] = 1.27 [1.11-1.45]) and ROSC (OR = 1.27 [1.14-1.42]). However, instantaneous amplitude was not associated with ROR (OR = 1.13 [0.97-1.30]) after accounting for maximum amplitude, nor with ROR (OR = 1.00 [0.87-1.15]) or ROSC (OR = 1.05 [0.93-1.18]) after accounting for AMSA. By contrast, AMSA and maximum amplitude remained independently associated with ROR and ROSC. CONCLUSIONS: We did not observe an independent association between instantaneous amplitude and shock-specific outcomes. Efforts to time shock to the maximal amplitude of the VF waveform are unlikely to affect resuscitation outcome.

Methods for calculating ventilation rates during resuscitation from out-of-hospital cardiac arrest.

OBJECTIVE: Ventilation control is important during resuscitation from out-of-hospital cardiac arrest (OHCA). We compared different methods for calculating ventilation rates (VR) during OHCA. METHODS: 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 sec). We defined hypo- and hyperventilation as VR <6 and >12 breaths/min, respectively. We assessed differences in estimated hypo- and hyperventilation with each VR measurement technique. RESULTS: Of 3,004 patients, data were available for 1,010. With the counted method, total hypoventilation increased with longer time epochs ([10-s epoch: 75 sec hypoventilation] to [60-s epoch: 97 sec hypoventilation]). However, with the measured method, total hypoventilation decreased with longer time epochs ([10-s epoch: 223 sec hypoventilation] to [60-s epoch: 150 sec hypoventilation]). With the counted method, the total duration of hyperventilation decreased with longer time epochs ([10-s epochs: 35 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). With the measured method, total hyperventilation decreased with longer time epochs ([10-s epoch: 78 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). Differences between the measured and counted estimates were smallest with a 60-s time epoch. CONCLUSIONS: Quantifications of hypo- and hyperventilation vary with the applied measurement methods. Measurement methods are important when characterizing ventilation rates in OHCA.

Airway strategy and ventilation rates in the pragmatic airway resuscitation trial.

BACKGROUND: We sought to describe ventilation rates during out-of-hospital cardiac arrest (OHCA) resuscitation and their associations with airway management strategy and outcomes. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Novel application of thoracic impedance to characterize ventilations during cardiopulmonary resuscitation in the pragmatic airway resuscitation trial.

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 0.71 Ω, Ae 0.70 Ω vs LT: Ai 0.46 Ω, Ae 0.45 Ω; p < 0.01 respectively) and mechanical chest compressions (ETI: Ai 1.22 Ω, Ae 1.14 Ω VS LT: Ai 0.74 Ω, Ae 0.68 Ω; p < 0.01 respectively). Ventilations per minute, duration of TI amplitude insufflation and exhalation did not differ among groups. 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.

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets.

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.

Airway strategy and chest compression quality in the Pragmatic Airway Resuscitation Trial.

BACKGROUND: 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). METHODS: 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. RESULTS: 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 ±â€¯10.8 min vs. ETI 25.3 ±â€¯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. CONCLUSION: 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.

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

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.

Impedance Based Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Monitoring ventilation rate is key to improve the quality of cardiopulmonary resuscitation (CPR) and increase the probability of survival in the event of an out-of-hospital cardiac arrest (OHCA). Ventilations produce discernible fluctuations in the thoracic impedance signal recorded by defibrillators. Impedance-based detection of ventilations during CPR is challenging due to chest compression artifacts. This study presents a method for an accurate detection of ventilations when chest compressions are delivered using a piston-driven mechanical device. Data from 223 OHCA patients were analyzed and 399 analysis segments totaling 3101 minutes of mechanical CPR were extracted. A total of 18327 ventilations were annotated using concurrent capnogram recordings. An adaptive least mean squares filter was used to remove compression artifacts. Potential ventilations were detected using a greedy peak detector, and the ventilation waveform was characterized using 8 waveform features. These features were used in a logistic regression classifier to discriminate true ventilations from false positives produced by the greedy peak detector. The classifier was trained and tested using patient wise 10-fold cross validation (CV), and 100 random CV partitions were created to statistically characterize the performance metrics. The peak detector presented a sensitivity (Se) of 99.30%, but a positive predictive value (PPV) of 54.43%. The best classifier configuration used 6 features and improved the mean (sd) Se and PPV of the detector to 93.20% (0.06) and 94.43% (0.04), respectively. When used to measure per minute ventilation rates for feedback to the rescuer, the mean (sd) absolute error in ventilation rate was 0.61 (1.64) min-1. The first impedance-based method to accurately detect ventilations and give feedback on ventilation rate during mechanical CPR has been demonstrated.