Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity.

INTRODUCTION: Pulseless electrical activity (PEA) is commonly observed in in-hospital cardiac arrest (IHCA). Universally available ECG characteristics such as QRS duration (QRSd) and heart rate (HR) may develop differently in patients who obtain ROSC or not. The aim of this study was to assess prospectively how QRSd and HR as biomarkers predict the immediate outcome of patients with PEA. METHOD: We investigated 327 episodes of IHCA in 298 patients at two US and one Norwegian hospital. We assessed the ECG in 559 segments of PEA nested within episodes, measuring QRSd and HR during pauses of compressions, and noted the clinical state that immediately followed PEA. We investigated the development of HR, QRSd, and transitions to ROSC or no-ROSC (VF/VT, asystole or death) in a joint longitudinal and competing risks statistical model. RESULTS: Higher HR, and a rising HR, reflect a higher transition intensity ("hazard") to ROSC (p < 0.001), but HR was not associated with the transition intensity to no-ROSC. A lower QRSd and a shrinking QRSd reflect an increased transition intensity to ROSC (p = 0.023) and a reduced transition intensity to no-ROSC (p = 0.002). CONCLUSION: HR and QRSd convey information of the immediateoutcome during resuscitation from PEA. These universally available and promising biomarkers may guide the emergency team in tailoring individual treatment.

Pulseless electrical activity in in-hospital cardiac arrest - A crossroad for decisions.

BACKGROUND: PEA is often seen during resuscitation, either as the presenting clinical state in cardiac arrest or as a secondary rhythm following transient return of spontaneous circulation (ROSC), ventricular fibrillation/tachycardia (VF/VT), or asystole (ASY). The aim of this study was to explore and quantify the evolution from primary/secondary PEA to ROSC in adults during in-hospital cardiac arrest (IHCA). METHODS: We analyzed 700 IHCA episodes at one Norwegian hospital and three U.S. hospitals at different time periods between 2002 and 2021. During resuscitation ECG, chest compressions, and ventilations were recorded by defibrillators. Each event was manually annotated using a graphical application. We quantified the transition intensities, i.e., the propensity to change from PEA to another clinical state using time-to-event statistical methods. RESULTS: Most patients experienced PEA at least once before achieving ROSC or being declared dead. Time average transition intensities to ROSC from primary PEA (n = 230) and secondary PEA after ASY (n = 72) were 0.1 per min, peaking at 4 and 7 minutes, respectively; thus, a patient in these types of PEA showed a 10% chance of achieving ROSC in one minute. Much higher transition intensities to ROSC, average of 0.15 per min, were observed for secondary PEA after VF/VT (n = 83) or after ROSC (n = 134). DISCUSSION: PEA is a crossroad in which the subsequent course is determined. The four distinct presentations of PEA behave differently on important characteristics. A transition to PEA during resuscitation should encourage the resuscitation team to continue resuscitative efforts.

Filtering mechanical chest compression artefacts from out-of-hospital cardiac arrest data.

AIM: Filtering techniques to remove manual compression artefacts from the ECG have not been incorporated to defibrillators to diagnose the rhythm during cardiopulmonary resuscitation. Mechanical and manual compression artefacts may be very different. The aim of this study is to characterize the compression artefact caused by the LUCAS 2 device and to evaluate whether filtering the LUCAS 2 artefact results in an accurate rhythm analysis. METHODS: A dataset of 1045 segments were obtained from 230 out-of-hospital cardiac arrest (OHCA) patients after LUCAS 2 activation. Rhythms were 201 shockable, 270 asystole and 574 organized. Segments during asystole were used to characterize the artefact in time and frequency domains. Three filtering methods, a comb filter and two adaptive filters, were used to remove the mechanical compression artefact. The filtered ECG was then diagnosed with a shock decision algorithm from a defibrillator. RESULTS: When compared to the manual compression artefact, the LUCAS 2 artefact presented a similar amplitude (1.2 mV, p-value 0.26), fixed frequency (101.7 min(-1)), more harmonic components, smaller spectral dispersion, and a more regular waveform (p-val <3 × 10(-7)). The sensitivity (SE) and specificity (SP) before filtering the LUCAS 2 artefact were 52.8% (90% low CI, 46.0%) and 81.5% (79.0%), respectively. For the best filter, SE and SP after filtering were 97.9% (95.7%) and 84.1% (82.0%), respectively. Optimal filters require more harmonics and smaller bandwidths than for manual compressions. CONCLUSION: Filtering resulted in a large increase in SE and small increase in SP. Despite differences in artefact characteristics between manual and mechanical compressions, filtering the LUCAS 2 compression artefact results in SE/SP values comparable to those obtained for manual compression artefacts. The SP is still below the 95% recommended by the American Heart Association.

Fully automatic rhythm analysis during chest compression pauses.

AIM: Chest compression artefacts impede a reliable rhythm analysis during cardiopulmonary resuscitation (CPR). These artefacts are not present during ventilations in 30:2 CPR. The aim of this study is to prove that a fully automatic method for rhythm analysis during ventilation pauses in 30:2 CPR is reliable an accurate. METHODS: For this study 1414min of 30:2 CPR from 135 out-of-hospital cardiac arrest cases were analysed. The data contained 1942 pauses in compressions longer than 3.5s. An automatic pause detector identified the pauses using the transthoracic impedance, and a shock advice algorithm (SAA) diagnosed the rhythm during the detected pauses. The SAA analysed 3-s of the ECG during each pause for an accurate shock/no-shock decision. RESULTS: The sensitivity and PPV of the pause detector were 93.5% and 97.3%, respectively. The sensitivity and specificity of the SAA in the detected pauses were 93.8% (90% low CI, 90.0%) and 95.9% (90% low CI, 94.7%), respectively. Using the method, shocks would have been advanced in 97% of occasions. For patients in nonshockable rhythms, rhythm reassessment pauses would be avoided in 95.2% (95% CI, 91.6-98.8) of occasions, thus increasing the overall chest compression fraction (CCF). CONCLUSION: An automatic method could be used to safely analyse the rhythm during ventilation pauses. This would contribute to an early detection of refibrillation, and to increase CCF in patients with nonshockable rhythms.

A reliable method for rhythm analysis during cardiopulmonary resuscitation.

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Automatic detection of chest compressions for the assessment of CPR-quality parameters.

AIM: Accurate chest compression detection is key to evaluate cardiopulmonary resuscitation (CPR) quality. Two automatic compression detectors were developed, for the compression depth (CD), and for the thoracic impedance (TI). The objective was to evaluate their accuracy for compression detection and for CPR quality assessment. METHODS: Compressions were manually annotated using the force and ECG in 38 out-of-hospital resuscitation episodes, comprising 869 min and 67,402 compressions. Compressions were detected using a negative peak detector for the CD. For the TI, an adaptive peak detector based on the amplitude and duration of TI fluctuations was used. Chest compression rate (CC-rate) and chest compression fraction (CCF) were calculated for the episodes and for every minute within each episode. CC-rate for rescuer feedback was calculated every 8 consecutive compressions. RESULTS: The sensitivity and positive predictive value were 98.4% and 99.8% using CD, and 94.2% and 97.4% using TI. The mean CCF and CC-rate obtained from both detectors showed no significant differences with those obtained from the annotations (P>0.6). The Bland-Altman analysis showed acceptable 95% limits of agreement between the annotations and the detectors for the per-minute CCF, per-minute CC-rate, and CC-rate for feedback. For the detector based on TI, only 3.7% of CC-rate feedbacks had an error larger than 5%. CONCLUSION: Automatic compression detectors based on the CD and TI signals are very accurate. In most cases, episode review could safely rely on these detectors without resorting to manual review. Automatic feedback on rate can be accurately done using the impedance channel.

Reliable extraction of the circulation component in the thoracic impedance measured by defibrillation pads.

AIM: To analyze the feasibility of extracting the circulation component from the thoracic impedance acquired by defibrillation pads. The impedance circulation component (ICC) would permit detection of pulse-generating rhythms (PRs) during the analysis intervals of an automated external defibrillator when a non-shockable rhythm with QRS complexes is detected. METHODS: A dataset of 399 segments, 165 associated with PR and 234 with pulseless electrical activity (PEA) rhythms, was extracted from out-of-hospital cardiac arrest episodes by applying a conservative criterion. Records consisted of the electrocardiogram and the thoracic impedance signals free of artifacts due to thoracic compressions and ventilations. The impedance was processed using an adaptive scheme based on a least mean square algorithm to extract the ICC. Waveform features of the ICC signal and its first derivative were used to discriminate PR from PEA rhythms. RESULTS: The segments were split into development (83 PR and 117 PEA rhythms) and testing (82 PR and 117 PEA rhythms) subsets with a mean duration of 10.6s. Three waveform features, peak-to-peak amplitude, mean power, and mean area were defined for the ICC signal and its first derivative. The discriminative power in terms of area under the curve with the testing dataset was 0.968, 0.971, and 0.969, respectively, when applied to the ICC signal, and 0.974, 0.988 and 0.988, respectively, with its first derivative. CONCLUSION: A reliable method to extract the ICC of the thoracic impedance is feasible. Waveform features of the ICC or its first derivative show a high discriminative power to differentiate PR from PEA rhythms (area under the curve higher than 0.96 for any feature).

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation.

AIM: To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR). METHODS: We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed. RESULTS: We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses. CONCLUSION: Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.

Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance.

AIM: To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. METHODS: A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. RESULTS: The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. CONCLUSIONS: The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.

The predictive value of ventricular fibrillation electrocardiogram signal frequency and amplitude variables in patients with out-of-hospital cardiac arrest.

UNLABELLED: We evaluated ventricular fibrillation frequency and amplitude variables to predict successful countershock, defined as pulse-generating electrical activity. We also elucidated whether bystander cardiopulmonary resuscitation (CPR) influences these electrocardiogram (ECG) variables. In 89 patients with out-of-hospital cardiac arrest, ECG recordings of 594 countershock attempts were collected and analyzed retrospectively. By using fast Fourier transformation analysis of the ventricular fibrillation ECG signal in the frequency range 0.333-15 Hz (median [range]), median frequency, dominant frequency, spectral edge frequency, and amplitude were as follows: 4.4 (2.4-7.5) Hz, 4.0 (0.7-7.0) Hz, 7.7 (3.7-13.7) Hz, and 0.94 (0.24-1.95) mV, respectively, before successful countershock (n = 59). These values were 3.8 (0.8-7.7) Hz (P = 0.0002), 3.0 (0.3-9.7) Hz (P < 0.0001), 7.3 (2.0-14.0) Hz (P < 0.05), and 0.53 (0.03-3.03) mV (P < 0.0001), respectively, before unsuccessful countershock (n = 535). In patients in whom bystander CPR was performed (n = 51), ventricular fibrillation frequency and amplitude before the first defibrillation attempt were higher than in patients without bystander CPR (n = 38) (median frequency, 4.4 [2.4-7.5] vs 3.7 [1.8-5.3] Hz, P < 0.0001; dominant frequency, 3.8 [0.9-7.7] vs 2.6 [0.8-5.9] Hz, P < 0.0001; spectral edge frequency, 8.4 [4.8-12.9] vs 7.2 [3.9-12.1] Hz, P < 0.05; amplitude, 0.79 [0.06-4.72] vs 0.67 [0.16-2.29] mV, P = 0.0647). Receiver operating characteristic curves demonstrate that successful countershocks will be best discriminated from unsuccessful countershocks by ventricular fibrillation amplitude (3000-ms epoch). At 73% sensitivity, a specificity of 67% was obtained with this variable. IMPLICATIONS: In patients with out-of-hospital cardiac arrest, successful countershocks will be best discriminated from unsuccessful countershocks by ventricular fibrillation amplitude (3000-ms epoch). At 73% sensitivity, a specificity of 67% was obtained with this variable.

"Probability of successful defibrillation" as a monitor during CPR in out-of-hospital cardiac arrested patients.

The frequency spectrum of the ECG in ventricular fibrillation (VF) correlates with myocardial perfusion and might predict defibrillation success defined as return of spontaneous circulation (ROSC). The predictive power increases when more spectral variables are combined, but the complex information can be difficult to handle during the intensity of CPR. We therefore developed a method for expressing this multidimensional information in a single reproducible variable reflecting the probability of defibrillation success. This is based on the highest performing predictor for ROSC after 883 shocks given to 156 patients with VF. This was a combination of two decorrelated spectral features based on a principal component analysis of an original feature set with information on centroid frequency, peak power frequency, spectral flatness and energy. The function "Probability of defibrillation success" (P(ROSC)(v)) was developed by a 2-dimensional histogram technique. P(ROSC)(v) discriminated between shocks followed by ROSC and No-ROSC (P<0.0001). The present methodology indicates a possible way to develop a CPR monitor.

Reducing CPR artefacts in ventricular fibrillation in vitro.

CPR creates artefacts on the ECG, and a pause in CPR is therefore mandatory during rhythm analysis. This hands-off interval is harmful to the already marginally circulated tissues during CPR, and if the artefacts could be removed by filtering, the rhythm could be analyzed during ongoing CPR. Fixed coefficient filters used in animals cannot solve this problem in humans, due to overlapping frequency spectra for artefacts and VF signals. In the present study, we established a method for mixing CPR-artefacts (noise) from a pig with human VF (signal) at various signal-to-noise ratios (SNR) from -10 dB to +10 dB. We then developed a new methodology for removing CPR artefacts by applying a digital adaptive filter, and compared the results with this filter to that of a fixed coefficient filter. The results with the adaptive filter clearly outperformed the fixed coefficient filter for all SNR levels. At an original SNR of 0 dB, the restored SNRs were 9.0+/-0.7 dB versus 0.9+/-0.7 dB respectively (P<0.0001).

CPR artifact removal from human ECG using optimal multichannel filtering.

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals we used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.

Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest.

BACKGROUND: In 156 patients with out-of-hospital cardiac arrest of cardiac cause, we analyzed the ability of 4 spectral features of ventricular fibrillation before a total of 868 shocks to discriminate or not between segments that correspond to return of spontaneous circulation (ROSC). METHODS AND RESULTS: Centroid frequency, peak power frequency, spectral flatness, and energy were studied. A second decorrelated feature set was generated with the coefficients of the principal component analysis transformation of the original feature set. Each feature set was split into training and testing sets for improved reliability in the evaluation of nonparametric classifiers for each possible feature combination. The combination of centroid frequency and peak power frequency achieved a mean+/-SD sensitivity of 92+/-2% and specificity of 27+/-2% in testing. The highest performing classifier corresponded to the combination of the 2 dominant decorrelated spectral features with sensitivity and specificity equal to 92+/-2% and 42+/-1% in testing or a positive predictive value of 0.15 and a negative predictive value of 0.98. Using the highest performing classifier, 328 of 781 shocks not leading to ROSC would have been avoided, whereas 7 of 87 shocks leading to ROSC would not have been administered. CONCLUSIONS: The ECG contained information predictive of shock therapy. This could reduce the delivery of unsuccessful shocks and thereby the duration of unnecessary "hands-off" intervals during cardiopulmonary resuscitation. The low specificity and positive predictive value indicate that other features should be added to improve performance.

Quality assessment of defribrillation and advanced life support using data from the medical control module of the defibrillator.

What actually occurred during the two last links in the 'chain of survival': defibrillation and advanced life support (ALS), was studied in 156 patients with cardiac arrest of cardiac aetiology using the computer recording of the defibrillator and the Utstein-style data record. Ten patients (6%) survived. The ECG artefacts caused by chest compressions enabled a detailed analysis of compression rates (median 108 min(-1)) and duration of important compression free periods. The time from initiation of monitoring during asystole until chest compressions were initiated was median 29 s, significantly shorter than during electromechanical dissociation (EMD, 109 s; P < 0.001). These times were both significantly longer than the median time from initiation of monitoring until the first shock was given in cases with VF (19 s; P < 0.001). A total of 883 shocks (median six shocks) were administered to 110 patients with a significant difference in number of shocks between survivors and non-survivors, one versus seven, respectively. The success rate for the first shock and all shocks defined as non-VT/VF 5 s after the shock, was 75 and 63%, respectively. However, just 10% of all shocks resulted in a rhythm with a pulse and only 4% resulted in sustained return of spontaneous circulation (ROSC). An isoelectric period followed 38% of the shocks, and in 27% this lasted more than 20 s, with five patients obtaining electrical activity with a pulse after more than 30 s of isoelectric ECG. Thoracic impedance did not affect the shock efficacy. The method of analysing resuscitation we describe may be useful for quality improvement.