Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics

Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest

Background

A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.

Methods

The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen's linear model, Weibull regression and joint models.

Conclusions

The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.

Ventilation during cardiopulmonary resuscitation with mechanical chest compressions: How often are two insufflations being given during the 3-second ventilation pauses?

BACKGROUND

Mechanical chest compression devices in 30:2 mode provide 3-second pauses to allow for two insufflations. We aimed to determine how often two insufflations are provided in these ventilation pauses, in order to assess if prehospital providers are able to ventilate out-of-hospital cardiac arrest (OHCA) patients successfully during mechanical chest compressions.

METHODS

Data from OHCA cases of the regional ambulance service of Utrecht, The Netherlands, were prospectively collected in the UTrecht studygroup for OPtimal registry of cardIAc arrest database (UTOPIA). Compression pauses and insufflations were visualized on thoracic impedance and waveform capnography signals recorded by manual defibrillators. Ventilation pauses were analyzed for number of insufflations, duration of the subintervals of the ventilation cycles, and ratio of successfully providing two insufflations over the course of the resuscitation. Generalized linear mixed effects models were used to accurately estimate proportions and means.

RESULTS

In 250 cases, 8473 ventilation pauses were identified, of which 4305 (51%) included two insufflations. When corrected for non-independence of the data across repeated measures within the same subjects with a mixed effects analysis, two insufflations were successfully provided in 45% of ventilation pauses (95% CI: 40-50%). In 19% (95% CI: 16-22%) none were given.

CONCLUSION

Providing two insufflations during pauses in mechanical chest compressions is mostly unsuccessful. We recommend developing strategies to improve giving insufflations when using mechanical chest compression devices. Increasing the pause duration might help to improve insufflation success.

The association of recent simulation training and clinical experience of team leaders with cardiopulmonary resuscitation quality during in-hospital cardiac arrest

OBJECTIVE

We aimed to investigate the association of recent team leader simulation training (<6 months) and years of clinical experience (≥4 years) with chest compression quality during in-hospital cardiac arrest (IHCA).

METHODS

This cohort study of IHCA in four Danish hospitals included cases with data on chest compression quality and team leader characteristics. We assessed the impact of recent simulation training and experienced team leaders on longest chest compression pause duration (primary outcome), chest compression fraction (CCF), and chest compression rates within guideline recommendations using mixed effects models.

RESULTS

Of 157 included resuscitation attempts, 45% had a team leader who recently participated in simulation training and 66% had an experienced team leader. The median team leader experience was 7 years [Q1; Q3: 4; 11]. The median duration of the longest chest compression pause was 16 s [10; 30]. Having a team leader with recent simulation training was associated with significantly shorter longest pause durations (difference: -7.11 s (95%-CI: -12.0; -2.2), p = 0.004), a higher CCF (difference: 3% (95%-CI: 2.0; 4.0%), p < 0.001) and with less guideline compliant chest compression rates (odds ratio: 0.4 (95%-CI: 0.19; 0.84), p = 0.02). Having an experienced team leader was not associated with longest pause duration (difference: -1.57 s (95%-CI: -5.34; 2.21), p = 0.42), CCF (difference: 0.7% (95%-CI: -0.3; 1.7), p = 0.17) or chest compression rates within guideline recommendations (odds ratio: 1.55 (95%-CI: 0.91; 2.66), p = 0.11).

CONCLUSION

Recent simulation training of team leaders, but not years of team leader experience, was associated with shorter chest compression pauses during IHCA.

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.

Prediction of Return of Spontaneous Circulation in a Pediatric Swine Model of Cardiac Arrest Using Low-Resolution Multimodal Physiological Waveforms

Monitoring physiological waveforms, specifically hemodynamic variables (e.g., blood pressure waveforms) and end-tidal CO2 (EtCO2), during pediatric cardiopulmonary resuscitation (CPR) has been demonstrated to improve survival rates and outcomes when compared to standard depth-guided CPR. However, waveform guidance has largely been based on thresholds for single parameters and therefore does not leverage all the information contained in multimodal data. We hypothesize that the combination of multimodal physiological features improves the prediction of the return of spontaneous circulation (ROSC), the clinical indicator of short-term CPR success. We used machine learning algorithms to evaluate features extracted from eight low-resolution (4 samples per minute) physiological waveforms to predict ROSC. The waveforms were acquired from the 2nd to 10th minute of CPR in pediatric swine models of cardiac arrest (N = 89, 8-12 kg). The waveforms were divided into segments with increasing length (both forward and backward) for feature extraction, and machine learning algorithms were trained for ROSC prediction. For the full CPR period (2nd to 10th minute), the area under the receiver operating characteristics curve (AUC) was 0.93 (95% CI: 0.87-0.99) for the multivariate model, 0.70 (0.55-0.85) for EtCO2 and 0.80 (0.67-0.93) for coronary perfusion pressure. The best prediction performances were achieved when the period from the 6th to the 10th minute was included. Poor predictions were observed for some individual waveforms, e.g., right atrial pressure. In conclusion, multimodal waveform features carry relevant information for ROSC prediction. Using multimodal waveform features in CPR guidance has the potential to improve resuscitation success and reduce mortality.

Ventilation rates measured by capnography during out-of-hospital cardiac arrest resuscitations and their association with return of spontaneous circulation

BACKGROUND

Clinical guidelines for adult out-of-hospital cardiac arrest (OHCA) recommend a ventilation rate of 8-10 per minute yet acknowledge that few data exist to guide recommendations. The goal of this study was to evaluate the utility of continuous capnography to measure ventilation rates and the association with return of spontaneous circulation (ROSC).

METHODS

This was a retrospective observational cohort study. We included all OHCA during a two-year period and excluded traumatic and pediatric patients. Ventilations were recorded using non-invasive continuous capnography. Blinded medically trained team members manually annotated all ventilations. Four techniques were used to analyze ventilation rate. The primary outcome was sustained prehospital ROSC. Secondary outcomes were vital status at the end of prehospital care and survival to hospital admission. Univariable and multivariable logistic regression models were constructed.

RESULTS

A total of 790 OHCA were analyzed. Only 386 (49%) had useable capnography data. After applying inclusion and exclusion criteria, the final study cohort was 314 patients. The median ventilation rate per minute was 7 (IQR 5.4-8.5). Only 70 (22%) received a guideline-compliant ventilation rate of 8-10 per minute. Sixty-two (20%) achieved the primary outcome. No statistically significant associations were observed between any of the ventilation parameters and patient outcomes in both univariable and multivariable logistic regression models.

CONCLUSIONS

We failed to detect an association between intra-arrest ventilation rates measured by continuous capnography and proximal patient outcomes after OHCA. Capnography has poor reliability as a measure of ventilation rate. Achieving guideline-compliant ventilation rates remains challenging.

Manual chest compression pause duration for ventilations during prehospital advanced life support - An observational study to explore optimal ventilation pause duration for mechanical chest compression devices

AIM

Mechanical chest compression devices in the 30:2 mode generally provide a pause of three seconds to give two insufflations without evidence supporting this pause duration. We aimed to explore the optimal pause duration by measuring the time needed for two insufflations, during advanced life support with manual compressions.

METHODS

Prospectively collected data in the AmsteRdam REsuscitation STudies (ARREST) registry were analysed, including thoracic impedance signal and waveform capnography from manual defibrillators of the Amsterdam ambulance service. Compression pauses were analysed for number of insufflations, time interval from start of the compression pause to the end of the second insufflation, chest compression pause duration and ventilation subintervals.

RESULTS

During 132 out-of-hospital cardiac arrests, 1619 manual chest compression pauses to ventilate were identified. In 1364 (84%) pauses, two insufflations were given. In 28% of these pauses, giving two insufflations took more than three seconds. The second insufflation is completed within 3.8 seconds in 90% and within 5 seconds in 97.5% of these pauses. An increasing likelihood of achieving two insufflations is seen with increasing compression pause duration up to five seconds.

CONCLUSION

The optimal chest compression pause duration for mechanical chest compression devices in the 30:2 mode to provide two insufflations, appears to be five seconds, warranting further studies in the context of mechanical chest compression. A 5-second pause will allow providers to give two insufflations with a very high success rate. In addition, a 5-second pause can also be used for other interventions like rhythm checks and endotracheal intubation.

A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR

AIM

We developed a method which continuously classifies the ECG rhythm during CPR in order to guide clinical care.

METHODS

We conducted a retrospective study of 432 patients treated following out-of-hospital cardiac arrest. Continuous ECG sequences from two-minute CPR cycles were extracted from defibrillator recordings and further divided into five-second clips. We developed an algorithm using wavelet analysis, hidden semi-Markov modeling, and random forest classification. The algorithm classifies individual clips as asystole, organized rhythm, ventricular fibrillation, or Inconclusive, while integrating information from previous clips. Classifications were compared to manual annotations to estimate accuracy in an independent validation dataset. Continuous sequences were classified as shockable, non-shockable, or Inconclusive; classifications were used to compute shock sensitivity and specificity.

RESULTS

Of 432 patient-cases, 290 were used for development and 142 for validation. In the 12,294 validation ECG clips during CPR, accuracies were 0.88 (95% CI 0.85-0.91) for asystole, 0.98 (95% CI 0.98-0.99) for organized rhythm, and 0.97 (95% CI 0.96-0.97) for ventricular fibrillation, with 43% classified as Inconclusive. Of 457 continuous sequences, shock sensitivity was 0.90 (95% CI 0.86, 0.93), shock specificity was 0.98 (95% CI 0.93, 0.99), and 7% were Inconclusive. Median delay to ventricular fibrillation recognition was 10 (IQR 5, 32) seconds.

CONCLUSION

An automated algorithm continuously classified the primary resuscitation rhythms-asystole, organized rhythms, and ventricular fibrillation-with 88-98% accuracy, enabling accurate shock advisory guidance during most two-minute CPR cycles. Additional investigation is required to understand how algorithm implementation could affect rescuer actions and clinical outcomes.

Prehospital Cardiac Arrest Airway Management: An NAEMSP Position Statement and Resource Document

Airway management is a critical component of out-of-hospital cardiac arrest (OHCA) resuscitation. Multiple cardiac arrest airway management techniques are available to EMS clinicians including bag-valve-mask (BVM) ventilation, supraglottic airways (SGAs), and endotracheal intubation (ETI). Important goals include achieving optimal oxygenation and ventilation while minimizing negative effects on physiology and interference with other resuscitation interventions. NAEMSP recommends:Based on the skill of the clinician and available resources, BVM, SGA, or ETI may be considered as airway management strategies in OHCA.Airway management should not interfere with other key resuscitation interventions such as high-quality chest compressions, rapid defibrillation, and treatment of reversible causes of the cardiac arrest.EMS clinicians should take measures to avoid hyperventilation during cardiac arrest resuscitation.Where available for clinician use, capnography should be used to guide ventilation and chest compressions, confirm and monitor advanced airway placement, identify return of spontaneous circulation (ROSC), and assist in the decision to terminate resuscitation.

Quality of Cardiopulmonary Resuscitation and 5-Year Survival Following in-Hospital Cardiac Arrest

Purpose

To improve cardiac arrest survival, international resuscitation guidelines emphasize measuring the quality of cardiopulmonary resuscitation (CPR). We aimed to investigate CPR quality during in-hospital cardiac arrest (IHCA) and study long-term survival outcomes.

Patients and Methods

This was a cohort study of IHCA from December 2011 until November 2014. Data were collected from the hospital switch board, patient records, and from defibrillators. Impedance data from defibrillators were analyzed manually at the level of single compressions. Long-term survival at 1-, 3-, and 5 years is reported.

Results

The study included 189 IHCAs; median (interquartile range (IQR)) time to first rhythm analysis was 116 (70-201) seconds and median (IQR) time to first defibrillation was 133 (82-264) seconds. Median (IQR) chest compression rate was 126 (119-131) per minute and chest compression fraction (CCF) was 78% (69-86). Thirty-day survival was 25%, while 1-year-, 3-year-, and 5-year survival were 21%, 14%, and 13%, respectively. There was no significant association between any survival outcomes and CCF, whereas chest compression rate was associated with survival to 30 days and 3 years. Overall, 5-year survival was associated with younger age (median 68 vs 74 years, p=0.003), less comorbidity (Charlson comorbidity index median 3 vs 5, p<0.001), and witnessed cardiac arrest (96% vs 77%, p=0.03).

Conclusion

We established a systematic collection of IHCA CPR quality data to measure and improve CPR quality and long-term survival outcomes. Median time to first rhythm check/defibrillation was <3 minutes, but median chest compression rate was too fast and median CCF slightly below 80%. More than half of 30-day survivors were still alive at 5 years.

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.

Tidal volume measurements via transthoracic impedance waveform characteristics: The effect of age, body mass index and gender. A single centre interventional study

BACKGROUND AND AIM

Measuring tidal volumes (TV) during bag-valve ventilation is challenging in the clinical setting. The ventilation waveform amplitude of the transthoracic impedance (TTI-amplitude) correlates well with TV for an individual, but poorer between patients. We hypothesized that TV to TTI-amplitude relations could be improved when adjusted for morphometric variables like body mass index (BMI), gender or age, and that TTI-amplitude cut-offs for ventilations with adequate TV (>400ml) could be established.

MATERIALS AND METHODS

Twenty-one consenting adults (9 female, and 9 overall overweight) during positive pressure ventilation in anaesthesia before scheduled surgery were included. Seventeen ventilator modes were used (⩾ five breaths per mode) to adjust different TVs (150-800 ml), ventilation frequencies (10-30 min^-1) and insufflation times (0.5-3.5 s). TTI from the defibrillation pads was filtered to obtain ventilation TTI-amplitudes. Linear regression models were fitted between target and explanatory variables, and compared (coefficient of determination, R²).

RESULTS

The TV to TTI-amplitude slope was 1.39 Ω/l (R²=0.52), with significant differences (p<0.05) between male/female (1.04 Ω/l vs 1.84 Ω/l) and normal/overweight subjects (1.65 Ω/l vs 1.04 Ω/l). The median (interquartile range) TTI-amplitude cut-off for adequate TV was 0.51 Ω(0.14-1.20) with significant differences between males and females (0.58 Ω/0.39 Ω), and normal and overweight subjects (0.52 Ω/0.46 Ω). The TV to TTI-amplitude model improved (R²=0.66) when BMI, age and gender were included.

CONCLUSIONS

TTI-amplitude to TV relations were established and cut-offs for ventilations with adequate TV determined. Patient morphometric variables related to gender, age and BMI explain part of the variability in the measurements.

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 ± 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.

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 identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

Background

A transthoracic impedance (TTI) signal is an important indicator of the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). We proposed an automatic detection algorithm including the wavelet decomposition, fuzzy c-means (FCM) clustering, and deep belief network (DBN) to identify the compression and ventilation waveforms for evaluating the quality of CPR.

Methods

TTI signals were collected from a cardiac arrest model that electrically induced cardiac arrest in pigs. All signals were denoised using the wavelet and morphology method. The potential compression and ventilation waveforms were marked using an algorithm with a multi-resolution window. The compressions and ventilations in these waveforms were identified and classified using the FCM clustering and DBN methods.

Results

Using the FCM clustering method, the positive predictive values (PPVs) for compressions and ventilations were 99.7% and 95.7%, respectively. The sensitivities of recognition were 99.8% for compressions and 95.1% for ventilations. The DBN approach exhibited similar PPV and sensitivity results to the FCM clustering method. The time cost was satisfactory using either of these techniques.

Conclusions

Our findings suggest that FCM clustering and DBN can be utilized to effectively and accurately evaluate CPR quality, and provide information for improving the success rate of CPR. Our real-time algorithms using FCM clustering and DBN eliminated most distortions and noises effectively, and correctly identified the compression and ventilation waveforms with a low time cost.

Monitoring chest compression rate in automated external defibrillators using the autocorrelation of the transthoracic impedance

Aim

High-quality chest compressions is challenging for bystanders and first responders to out-of-hospital cardiac arrest (OHCA). Long compression pauses and compression rates higher than recommended are common and detrimental to survival. Our aim was to design a simple and low computational cost algorithm for feedback on compression rate using the transthoracic impedance (TI) acquired by automated external defibrillators (AEDs).

Methods

ECG and TI signals from AED recordings of 242 OHCA patients treated by basic life support (BLS) ambulances were retrospectively analyzed. Beginning and end of chest compression series and each individual compression were annotated. The algorithm computed a biased estimate of the autocorrelation of the TI signal in consecutive non-overlapping 2-s analysis windows to detect the presence of chest compressions and estimate compression rate.

Results

A total of 237 episodes were included in the study, with a median (IQR) duration of 10 (6–16) min. The algorithm performed with a global sensitivity in the detection of chest compressions of 98.7%, positive predictive value of 98.7%, specificity of 97.1%, and negative predictive value of 97.1% (validation subset including 207 episodes). The unsigned error in the estimation of compression rate was 1.7 (1.3–2.9) compressions per minute.

Conclusion

Our algorithm is accurate and robust for real-time guidance on chest compression rate using AEDs. The algorithm is simple and easy to implement with minimal software modifications. Deployment of AEDs with this capability could potentially contribute to enhancing the quality of chest compressions in the first minutes from collapse.

A review of ventilation in adult out-of-hospital cardiac arrest

Out-of-hospital cardiac arrest continues to be a devastating condition despite advances in resuscitation care. Ensuring effective gas exchange must be weighed against the negative impact hyperventilation can have on cardiac physiology and survival. The goals of this narrative review are to evaluate the available evidence regarding the role of ventilation in out-of-hospital cardiac arrest resuscitation and to provide recommendations for future directions. Ensuring successful airway patency is fundamental for effective ventilation. The airway management approach should be based on professional skill level and the situation faced by rescuers. Evidence has explored the influence of different ventilation rates, tidal volumes, and strategies during out-of-hospital cardiac arrest; however, other modifiable factors affecting out-of-hospital cardiac arrest ventilation have limited supporting data. Researchers have begun to explore the impact of ventilation in adult out-of-hospital cardiac arrest outcomes, further stressing its importance in cardiac arrest resuscitation management. Capnography and thoracic impedance signals are used to measure ventilation rate, although these strategies have limitations. Existing technology fails to reliably measure real-time clinical ventilation data, thereby limiting the ability to investigate optimal ventilation management. An essential step in advancing cardiac arrest care will be to develop techniques to accurately and reliably measure ventilation parameters. These devices should allow for immediate feedback for out-of-hospital practitioners, in a similar way to chest compression feedback. Once developed, new strategies can be established to guide out-of-hospital personnel on optimal ventilation practices.

A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance

OBJECTIVE

Interruptions in chest compressions during treatment of out-of-hospital cardiac arrest are associated with lower likelihood of successful resuscitation. Real-time automated detection of chest compressions may improve CPR administration during resuscitation, and could facilitate application of next-generation ECG algorithms that employ different parameters depending on compression state. In contrast to accelerometer sensors, transthoracic impedance (TTI) is commonly acquired by defibrillators. We sought to develop and evaluate the performance of a TTI-based algorithm to automatically detect chest compressions.

METHODS

Five-second TTI segments were collected from patients with out-of-hospital cardiac arrest treated by one of four defibrillator models. Segments with and without chest compressions were collected prior to each of the first four defibrillation shocks (when available) from each case. Patients were divided randomly into 40% training and 60% validation groups. From the training segments, we identified spectral and time-domain features of the TTI associated with compressions. We used logistic regression to predict compression state from these features. Performance was measured by sensitivity and specificity in the validation set. The relationship between performance and TTI segment length was also evaluated.

RESULTS

The algorithm was trained using 1859 segments from 460 training patients. Validation sensitivity and specificity were >98% using 2727 segments from 691 validation patients. Validation performance was significantly reduced using segments shorter than 3.2 s.

CONCLUSIONS

A novel method can reliably detect the presence of chest compressions using TTI. These results suggest potential to provide real-time feedback in order to improve CPR performance or facilitate next-generation ECG rhythm algorithms during resuscitation.

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.

Transthoracic Impedance Measured with Defibrillator Pads-New Interpretations of Signal Change Induced by Ventilations

Compressions during the insufflation phase of ventilations may cause severe pulmonary injury during cardiopulmonary resuscitation (CPR). Transthoracic impedance (TTI) could be used to evaluate how chest compressions are aligned with ventilations if the insufflation phase could be identified in the TTI waveform without chest compression artifacts. Therefore, the aim of this study was to determine whether and how the insufflation phase could be precisely identified during TTI. We synchronously measured TTI and airway pressure (Paw) in 21 consenting anaesthetised patients, TTI through the defibrillator pads and Paw by connecting the monitor-defibrillator’s pressure-line to the endotracheal tube filter. Volume control mode with seventeen different settings were used (5–10 ventilations/setting): Six volumes (150–800 mL) with 12 min⁻¹ frequency, four frequencies (10, 12, 22 and 30 min⁻¹) with 400 mL volume, and seven inspiratory times (0.5–3.5 s) with 400 mL/10 min⁻¹ volume/frequency. Median time differences (quartile range) between timing of expiration onset in the Paw-line (Paw_EO) and the TTI peak and TTI maximum downslope were measured. TTI peak and Paw_EO time difference was 579 (432–723) ms for 12 min⁻¹, independent of volume, with a negative relation to frequency, and it increased linearly with inspiratory time (slope 0.47, R2 = 0.72). Paw_EO and TTI maximum downslope time difference was between −69 and 84 ms for any ventilation setting (time aligned). It was independent (R2 < 0.01) of volume, frequency and inspiratory time, with global median values of −47 (−153–65) ms, −40 (−168–68) ms and 20 (−93–128) ms, for varying volume, frequency and inspiratory time, respectively. The TTI peak is not aligned with the start of exhalation, but the TTI maximum downslope is. This knowledge could help with identifying the ideal ventilation pattern during CPR.

Association of ventilation with outcomes from out-of-hospital cardiac arrest

AIM OF STUDY

To determine the association between bioimpedence-detected ventilation and out-of-hospital cardiac arrest (OHCA) outcomes.

METHODS

This is a retrospective, observational study of 560 OHCA patients from the Dallas-Fort Worth site enrolled in the Resuscitation Outcomes Consortium Trial of Continuous or Interrupted Chest Compressions During CPR from 4/2012 to 7/2015. We measured bioimpedance ventilation (lung inflation) waveforms in the pause between chest compression segments (Physio-Control LIFEPAK 12 and 15, Redmond, WA) recorded through defibrillation pads. We included cases ≥18 years with presumed cardiac cause of arrest assigned to interrupted 30:2 chest compressions with bag-valve-mask ventilation and ≥2 min of recorded cardiopulmonary resuscitation. We compared outcomes in two a priori pre-specified groups: patients with ventilation waveforms in <50% of pauses (Group 1) versus those with waveforms in ≥50% of pauses (Group 2).

RESULTS

Mean duration of 30:2 CPR was 13 ± 7 min with a total of 7762 pauses in chest compressions. Group 1 (N = 424) had a median 11 pauses and 3 ventilations per patient vs. Group 2 (N = 136) with a median 12 pauses and 8 ventilations per patient, which was associated with improved return of spontaneous circulation (ROSC) at any time (35% vs. 23%, p < 0.005), prehospital ROSC (19.8% vs. 8.7%, p < 0.0009), emergency department ROSC (33% vs. 21%, p < 0.005), and survival to hospital discharge (10.3% vs. 4.0%, p = 0.008).

CONCLUSIONS

This novel study shows that ventilation with lung inflation occurs infrequently during 30:2 CPR. Ventilation in ≥50% of pauses was associated with significantly improved rates of ROSC and survival.

Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest

BACKGROUND

Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO₂ for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC.

MATERIALS AND METHODS

A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO₂ values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO₂ value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC.

RESULTS

EtCO₂ values increased significantly from 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO₂ was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p < 0.05). Adding EtCO₂ to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO₂ had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively.

CONCLUSION

Adding EtCO₂ improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC.

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

Metrics save lives: value and hurdles faced

PURPOSE OF REVIEW

Affirmation of the importance of precision in fundamentals of resuscitation practices with improving neurologically intact survival from sudden cardiac arrest, correlated with both measurements of resuscitation metrics generically and recently further refined metric parameters specifically.

RECENT FINDINGS

Quality of baseline cardiopulmonary resuscitation (CPR) in historic intervention trials may not be 'high quality' as once assumed. Optimal chest compression rates are within the narrow spectrum of 106-108/min for adults. Optimal ventilation rates remain within the 8-10/min range.

SUMMARY

Although traditional CPR teaching of 'hard and fast' chest compressions has promoted a relatively easy to remember directive, the reality is that laypersons and medical professionals alike may unwittingly provide markedly suboptimal chest compression depths and rates. Prior resuscitation studies that focused upon airway adjuncts, defibrillation strategies, and/or pharmaceutical interventions that did not simultaneously gauge the underlying CPR chest compression rates, chest compression fraction of time, and ventilation rates should be cautiously interpreted in light of discovery that assumption of 'high-quality CPR' without measurement of the metrics of such is likely a faulty assumption.

The past, present, and future of ventilation during cardiopulmonary resuscitation

PURPOSE OF REVIEW

To evaluate the past and present literature on ventilation during out of hospital cardiac arrest, highlighting research that has informed current guidelines.

RECENT FINDINGS

Previous studies have studied what are optimal compression-to-ventilation ratios, ventilation rates, and methods of ventilation. Continuous chest compression cardiopulmonary resuscitation (CPR) has not shown to provide a significant survival benefit over the traditional 30 : 2 CPR. The optimal ventilation rate is recommended at 8 to 10 breaths per minute. Methods such as capnography and thoracic impedance are being used to evaluate ventilation in research studies.

SUMMARY

Future out of hospital cardiac arrest studies are still exploring how to optimize the delivery of ventilation during the initial stages of resuscitation. More prospective studies focusing on ventilation are needed to inform guidelines.

ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis

OBJECTIVE

There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms.

METHODS

The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied.

RESULTS

The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively.

CONCLUSIONS

The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.

SIGNIFICANCE

We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.

Feedback on the Rate and Depth of Chest Compressions during Cardiopulmonary Resuscitation Using Only Accelerometers

Background

Quality of cardiopulmonary resuscitation (CPR) is key to increase survival from cardiac arrest. Providing chest compressions with adequate rate and depth is difficult even for well-trained rescuers. The use of real-time feedback devices is intended to contribute to enhance chest compression quality. These devices are typically based on the double integration of the acceleration to obtain the chest displacement during compressions. The integration process is inherently unstable and leads to important errors unless boundary conditions are applied for each compression cycle. Commercial solutions use additional reference signals to establish these conditions, requiring additional sensors. Our aim was to study the accuracy of three methods based solely on the acceleration signal to provide feedback on the compression rate and depth.

Materials and Methods

We simulated a CPR scenario with several volunteers grouped in couples providing chest compressions on a resuscitation manikin. Different target rates (80, 100, 120, and 140 compressions per minute) and a target depth of at least 50 mm were indicated. The manikin was equipped with a displacement sensor. The accelerometer was placed between the rescuer’s hands and the manikin’s chest. We designed three alternatives to direct integration based on different principles (linear filtering, analysis of velocity, and spectral analysis of acceleration). We evaluated their accuracy by comparing the estimated depth and rate with the values obtained from the reference displacement sensor.

Results

The median (IQR) percent error was 5.9% (2.8–10.3), 6.3% (2.9–11.3), and 2.5% (1.2–4.4) for depth and 1.7% (0.0–2.3), 0.0% (0.0–2.0), and 0.9% (0.4–1.6) for rate, respectively. Depth accuracy depended on the target rate (p < 0.001) and on the rescuer couple (p < 0.001) within each method.

Conclusions

Accurate feedback on chest compression depth and rate during CPR is possible using exclusively the chest acceleration signal. The algorithm based on spectral analysis showed the best performance. Despite these encouraging results, further research should be conducted to asses the performance of these algorithms with clinical data.