Characterization of mechanical properties of adult chests during pre-hospital manual chest compressions through a simple viscoelastic model.

AIM: The purpose of this study was to develop a simple viscoelastic model to characterize the mechanical properties of chests during manual chest compressions in pre-hospital cardiopulmonary resuscitation (CPR). METHODS: Force and acceleration signals were extracted from CPR monitors used during pre-hospital resuscitation attempts on adult patients. Individual chest compressions were identified and segmented from the chest displacement computed using the force and acceleration. Each compression-recoil cycle was characterized by its elastic coefficient k (a measure of stiffness) and its compression and recoil damping coefficients, dc and dr, respectively (measures of viscosity). We compared the estimated and the calculated chest displacement to assess the goodness of fit of the model. We characterized the chest of patients at the beginning of CPR in relation to sex and age, and their variation as CPR progressed. RESULTS: A total of 1,156,608 chest compressions from 615 patients were analysed. Mean (95% CI) coefficient of determination R2 for the viscoelastic model was 97.9% (97.8-98.1). At the beginning of CPR, k was 104.9 N⋅cm-1 (102.0-107.8), dc was 2.868 N⋅s⋅cm-1 (2.751-2.984) and dr was 4.889 N⋅s⋅cm-1 (4.648-5.129). Damping during recoil was significantly higher than during compression. Stiffness was lower in women than in men. There were no differences in damping coefficients with sex but a higher dr with increasing age. All model coefficients decreased with compression count, with an overall decrease after 3,000 chest compressions of 34.6%, 48.8% and 37.2%, respectively. CONCLUSION: The model accurately described adult chest mechanical properties during CPR, highlighting differences between compression and recoil, sex and age, and a progressive reduction in chest stiffness and viscosity along resuscitation. Our findings may merit further investigation into whether patient-tailored and time-sensitive chest compression technique may be appropriate.

Assessment of the evolution of end-tidal carbon dioxide within chest compression pauses to detect restoration of spontaneous circulation.

BACKGROUND: Measurement of end-tidal CO2 (ETCO2) can help to monitor circulation during cardiopulmonary resuscitation (CPR). However, early detection of restoration of spontaneous circulation (ROSC) during CPR using waveform capnography remains a challenge. The aim of the study was to investigate if the assessment of ETCO2 variation during chest compression pauses could allow for ROSC detection. We hypothesized that a decay in ETCO2 during a compression pause indicates no ROSC while a constant or increasing ETCO2 indicates ROSC. METHODS: We conducted a retrospective analysis of adult out-of-hospital cardiac arrest (OHCA) episodes treated by the advanced life support (ALS). Continuous chest compressions and ventilations were provided manually. Segments of capnography signal during pauses in chest compressions were selected, including at least three ventilations and with durations less than 20 s. Segments were classified as ROSC or non-ROSC according to case chart annotation and examination of the ECG and transthoracic impedance signals. The percentage variation of ETCO2 between consecutive ventilations was computed and its average value, ΔETavg, was used as a single feature to discriminate between ROSC and non-ROSC segments. RESULTS: A total of 384 segments (130 ROSC, 254 non-ROSC) from 205 OHCA patients (30.7% female, median age 66) were analyzed. Median (IQR) duration was 16.3 (12.9,18.1) s. ΔETavg was 0.0 (-0.7, 0.9)% for ROSC segments and -11.0 (-14.1, -8.0)% for non-ROSC segments (p < 0.0001). Best performance for ROSC detection yielded a sensitivity of 95.4% (95% CI: 90.1%, 98.1%) and a specificity of 94.9% (91.4%, 97.1%) for all ventilations in the segment. For the first 2 ventilations, duration was 7.7 (6.0, 10.2) s, and sensitivity and specificity were 90.0% (83.5%, 94.2%) and 89.4 (84.9%, 92.6%), respectively. Our method allowed for ROSC detection during the first compression pause in 95.4% of the patients. CONCLUSION: Average percent variation of ETCO2 during pauses in chest compressions allowed for ROSC discrimination. This metric could help confirm ROSC during compression pauses in ALS settings.

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.

The impact of ventilation rate on end-tidal carbon dioxide level during manual cardiopulmonary resuscitation.

AIM: Ventilation rate is a confounding factor for interpretation of end-tidal carbon dioxide (ETCO2) during cardiopulmonary resuscitation (CPR). The aim of our study was to model the effect of ventilation rate on ETCO2 during manual CPR in adult out-of-hospital cardiac arrest (OHCA). METHODS: We conducted a retrospective analysis of OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG. We annotated pairs of capnogram segments presenting differences in average ventilation rate and average ETCO2 value but with other influencing factors (e.g. compression rate and depth) presenting similar values within the pair. ETCO2 variation as a function of ventilation rate was adjusted through curve fitting using non-linear least squares as a measure of goodness of fit. RESULTS: A total of 141 pairs of segments from 102 patients were annotated. Each pair provided a single data point for curve fitting. The best goodness of fit yielded a coefficient of determination R2 of 0.93. Our model described that ETCO2 decays exponentially with increasing ventilation rate. The model showed no differences attributable to the airway type (endotracheal tube or supraglottic King-LT-D). CONCLUSION: Capnogram interpretation during CPR is challenging since many factors influence ETCO2. For adequate interpretation, we need to know the effect of each factor on ETCO2. Our model allows quantifying the effect of ventilation rate on ETCO2 variation. Our findings could contribute to better interpretation of ETCO2 during CPR.

Chest compressions induce errors in end-tidal carbon dioxide measurement.

BACKGROUND: Real-time measurement of end-tidal carbon dioxide (ETCO2) is used as a non-invasive estimate of cardiac output and perfusion during cardiopulmonary resuscitation (CPR). However, capnograms are often distorted by chest compressions (CCs) and this may affect ETCO2 measurement. The aim of the study was to quantify the effect of CC-artefact on the accuracy of ETCO2 measurements obtained during out-of-hospital manual CPR. METHODS: We retrospectively analysed monitor-defibrillator recordings collected by two advanced life support agencies during out-of-hospital cardiac arrest. These two agencies, represented as A and B used different side-stream capnometers and monitor-defibrillators. One-minute capnogram segments were reviewed. Each ventilation within each segment was identified using the transthoracic impedance signal and the capnogram. ETCO2 values per ventilation were manually annotated and compared to the corresponding capnometry values stored in the monitor-defibrillator. Ventilations were classified as distorted or non-distorted by CC-artefact. RESULTS: A total of 407 1-min capnogram segments from 65 patients were analysed. Overall, 4095 ventilations were annotated, 2170 (32.4% distorted) and 1925 (31.8% distorted) for agency A and B, respectively. Median (IQR) unsigned error in ETCO2 measurement increased from 1.5 (0.6-3.1)% for non-distorted to 5.5 (1.8-14.1)% for distorted ventilations; from 0.7 (0.3-1.2)% to 3.7 (1.0-9.9)% in agency A and from 2.3 (1.2-3.9)% to 8.3 (3.9-19.5)% in agency B (p < 0.001). Errors were higher than 10 mmHg in 9% and higher than 15 mmHg in 5% of the distorted ventilations. CONCLUSION: CC-artefact causes ETCO2 measurement errors in the two studied devices. This suggests that capnometer algorithms may need to be adapted to reliably perform in the presence of CC-artefact during CPR.

Modeling the impact of ventilations on the capnogram in out-of-hospital cardiac arrest.

AIM: Current resuscitation guidelines recommend waveform capnography as an indirect indicator of perfusion during cardiopulmonary resuscitation (CPR). Chest compressions (CCs) and ventilations during CPR have opposing effects on the exhaled carbon dioxide (CO2) concentration, which need to be better characterized. The purpose of this study was to model the impact of ventilations in the exhaled CO2 measured from capnograms collected during out-of-hospital cardiac arrest (OHCA) resuscitation. METHODS: We retrospectively analyzed OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG signals. Segments with CC pauses, two or more ventilations, and with no pulse-generating rhythm were selected. Thus, only ventilations should have caused the decrease in CO2 concentration. The variation in the exhaled CO2 concentration with each ventilation was modeled with an exponential decay function using non-linear-least-squares curve fitting. RESULTS: Out of the original 1002 OHCA dataset (one per patient), 377 episodes had the required signals, and 196 segments from 96 patients met the inclusion criteria. Airway type was endotracheal tube in 64.8% of the segments, supraglottic King LT-D™ in 30.1%, and unknown in 5.1%. Median (IQR) decay factor of the exhaled CO2 concentration was 10.0% (7.8 - 12.9) with R2 = 0.98(0.95 - 0.99). Differences in decay factor with airway type were not statistically significant (p = 0.17). From these results, we propose a model for estimating the contribution of CCs to the end-tidal CO2 level between consecutive ventilations and for estimating the end-tidal CO2 variation as a function of ventilation rate. CONCLUSION: We have modeled the decrease in exhaled CO2 concentration with ventilations during chest compression pauses in CPR. This finding allowed us to hypothesize a mathematical model for explaining the effect of chest compressions on ETCO2 compensating for the influence of ventilation rate during CPR. However, further work is required to confirm the validity of this model during ongoing chest compressions.

Association of chest compression and recoil velocities with depth and rate in manual cardiopulmonary resuscitation.

AIM: Maximum velocity during chest recoil has been proposed as a metric for chest compression quality during cardiopulmonary resuscitation (CPR). This study investigated the relationship of the maximum velocities during compression and recoil phases with compression depth and rate in manual CPR. METHODS: We measured compression instances in out-of-hospital cardiac arrest recordings using custom Matlab programs. Each compression cycle was characterized by depth and rate, maximum compression and recoil velocities (CV and RV), and compression and recoil durations (total and effective). Mean compression and recoil velocities were computed as depth divided by compression and recoil durations, respectively. We correlated CV and RV with their corresponding mean velocities (total and effective), characterized by Pearson's correlation coefficient. RESULTS: CV/RV were strongly correlated with their corresponding mean velocities, with a median r of 0.83 (0.77-0.88)/0.82 (0.76-0.87) in per patient analysis, 0.86/0.88 for all the population. Correlation with mean effective velocities had a median r of 0.91 (0.87-0.94)/0.92 (0.89-0.94) in per-patient, 0.92/0.94 globally (p < 0.001). Total and effective compression and recoil durations were inversely proportional to compression rate. We observed similar RV values among compressions regardless of whether they were compliant with recommended depth and rate. Conversely, we observed different RV values among compressions having the same depth and rate, but presenting very distinct compression waveforms. CONCLUSION: CV and RV were highly correlated with compression depth and compression and recoil times, respectively. Better understanding of the relationship between novel and current quality metrics could help with the interpretation of CPR quality studies.

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform.

BACKGROUND: During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection. MATERIALS AND METHODS: We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms. RESULTS: Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms. CONCLUSIONS: Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation.

Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-of-concept of a single accelerometer-based feedback algorithm.

BACKGROUND: The use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. MATERIALS AND METHODS: The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. RESULTS: The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. CONCLUSIONS: The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.