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

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.

Arterial pressure, end-tidal carbon dioxide, and central venous oxygen saturation in reflecting compression depth

BACKGROUND

We sought to investigate the utility of arterial pressure, end-tidal carbon dioxide (ETCO2 ), and central venous oxygen saturation (SCVO2 ) to guide compression depth adjustment. Thus, in a pig model of cardiac arrest, we observed these parameters during cardiopulmonary resuscitation (CPR) with optimal and suboptimal compression depths.

METHODS

Sixteen pigs underwent three experimental sessions after induction of ventricular fibrillation. First, the animals received two 4-min CPR trials with either optimal (20% of the anteroposterior diameter) or suboptimal (70% of the optimal depth) compression depth. Second, the animals received two 5-min CPR trials with optimal compression depth, in which adrenaline (0.02 mg/kg) or saline placebo was administered. Third, the animals randomly received compression with either optimal or suboptimal depth during advanced cardiovascular life support.

RESULTS

The systolic arterial pressure reflected compression depth most accurately and immediately (area under the curve [AUC], 0.895-0.939 without adrenaline and 0.928-1.000 with adrenaline). Although the response of ETCO2 to the change in compression depth was 0.5 min slower than that of the systolic arterial pressure, the performance of ETCO2 was comparable with that of systolic arterial pressure. SCVO2 did not reflect compression depth. Adrenaline administration remarkably increased systolic arterial pressure, diastolic arterial pressure, and coronary perfusion pressure but did not affect the ETCO2 readings.

CONCLUSION

In a pig model of cardiac arrest, systolic arterial pressure reflected compression depth immediately and accurately. The performance of ETCO2 was comparable with that of systolic arterial pressure. SCVO2 did not reflect compression depth.

A focused investigation of expedited, stack of three shocks versus chest compressions first followed by single shocks for monitored ventricular fibrillation/ventricular tachycardia cardiopulmonary arrest in an in-hospital setting

BACKGROUND

In cases of in-hospital-witnessed ventricular fibrillation/ventricular tachycardia (VF/VT) arrest, it is unclear whether cardiopulmonary resuscitation prior to defibrillation attempt or expedited stacked defibrillation attempt is superior.

METHODS

Retrospective, observational study of all admitted patients with continuous cardiac monitoring who suffered VF/VT arrest between July 2005 and June 2013. In the stacked shock period (2005-2008), institutional protocols advocated early defibrillation with administration of 3 stacked shocks with brief pauses between each single defibrillation attempt to confirm sustained VF/VT. During the initial chest compression period (2008-2011), the protocol was modified to perform a 2-minute period of chest compressions prior to each defibrillation, including the initial. In the modified stack shock period (2011-2013), for a monitored arrest, defibrillation attempts were expedited with up to 3 successive shocks administered for persistent VF/VT. In unmonitored arrest, chest compressions and ventilations were initiated prior to defibrillation. The primary outcome measure was survival to hospital discharge.

RESULTS

Six hundred sixty-one cardiopulmonary arrests were recorded during the study period, with 106 patients (16%) representing primary VF/VT. The incidence of VF/VT arrest did not vary significantly between the study periods (P= 0.16) Survival to hospital discharge for all primary VF/VT arrest victims decreased, then increased significantly from the stacked shock period to initial chest compression period to modified stacked shock period (58%, 18%, 71%, respectively, P < 0.01). Specific group differences were significant between the initial chest compression versus the stacked and modified stacked shock groups (all P < 0.01).

CONCLUSION

Data suggest that monitored VF/VT should undergo expeditious defibrillation with use of stacked shocks.

Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests

Introduction

Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain.

Methods

A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients.

Results

Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered.

Conclusions

In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.

Towards cardiopulmonary resuscitation without vasoactive drugs

PURPOSE OF REVIEW

Whereas there is clear evidence for improved survival with cardiopulmonary resuscitation (CPR) and defibrillation during cardiac arrest management, there is today lacking evidence that any of the recommended and used drugs lead to any long-term benefit for the patients. In this review, we try to discuss our current view on why advanced life support (ALS) today can be performed without the use of drugs, and instead gain all focus on improving the tasks we know improve survival: CPR and defibrillation.

RECENT FINDINGS

Previous and recent cardiac arrest drug studies have been reviewed. These are mostly consisting of retrospective register data, some experimental data and a few new randomized trials. The alternative drug-free ALS concept is also discussed with relevant studies.

SUMMARY

There is currently no evidence to support any specific drugs during cardiac arrest. Good-quality CPR, early defibrillation and goal-directed postresuscitation care is more important. Healthcare systems should not prioritize implementation of unproven drugs before good quality of care can be documented. More drug studies are indeed required, and future research needs to incorporate better diagnostic tools to test more specific and tailored therapies that account for underlying causes and individual responsiveness.

Beyond ventricular fibrillation analysis: comprehensive waveform analysis for all cardiac rhythms occurring during resuscitation

AIM

To propose a method which analyses the electrocardiogram (ECG) waveform of any cardiac rhythm occurring during resuscitation and computes the probability of that rhythm converting into another with better prognosis (Pdes).

METHODS

Rhythm transitions occurring spontaneously or due to defibrillation were analyzed. For each possible rhythm, ventricular fibrillation/ventricular tachycardia (VF/VT), pulseless electrical activity (PEA), pulse-generating rhythm (PR) and asystole (AS), the desired and undesired transitions were defined. ECG segments corresponding to the last 3s of rhythms prior to transition were used to extract waveform features. For each rhythm type, waveform features were combined into a logistic regression model to develop a rhythm specific classifier of desired transitions. This model was the monitoring function for the Pdes. The capacity of each rhythm specific classifier to discriminate between desired and undesired transitions was evaluated in terms of area under the curve (AUC). Pdes was integrated into a state sequence representation, which structures the information of cardiac arrest episodes, to analyze the effect of therapy on patient. As a case study, the effect of optimal/suboptimal cardiopulmonary resuscitation (CPR) on Pdes was analyzed. The mean Pdes was computed for the pre- and post-CPR intervals which presented the same underlying rhythm. The relationship between the optimal/suboptimal CPR and increase/decrease of Pdes was analyzed.

RESULTS

The AUC was 0.80, 0.79, 0.73 and 0.61 for VF/VT, PEA, PR and AS respectively. The Pdes quantified the probability of every rhythm of the episode developing to a better state, and the evolution of Pdes was coherent with the provided therapy. The case study indicated, for most rhythms, that positive trends in the dynamic behaviour could be associated with optimal CPR, whereas the opposite seemed true for negative trends.

CONCLUSION

A method for continuous ECG waveform analysis covering all cardiac rhythms during resuscitation has been proposed. This methodology can be further developed to be used in retrospective studies of CPR techniques, and, in the future, for potentially monitoring in real time the probability of survival of patients being resuscitated.

Advanced life support therapy on out-of-hospital cardiac arrest patients: an engineering perspective

In the USA alone, several hundred thousand people die of sudden cardiac arrests each year. Basic life support, defined as chest compressions and ventilations, and early defibrillation are the only factors proven to increase the survival of patients with out-of-hospital cardiac arrest and are key elements in the chain of survival defined by the American Heart Association. The current cardiopulmonary resuscitation guidelines treat all patients the same but studies show a need for more individualization of treatment. This review focusses on ideas on how to strengthen the weak parts of the chain of survival including the ability to measure the effects of therapy, improve time efficiency and optimize the sequence and quality of the various components of cardiopulmonary resuscitation.

Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest

PURPOSE

Ventricular fibrillation (VF) is a common cardiac arrest rhythm that can be terminated by electrical defibrillation. During cardiopulmonary resuscitation, there is a strong need for a prompt and reliable predictor of successful defibrillation because myocardial damage can result from repeated futile defibrillation attempts. Continuous wavelet transform (CWT) provides excellent time and frequency resolution of signals. The purpose of this study was to evaluate whether features based on CWT could predict successful defibrillation.

METHODS

VF electrocardiogram (ECG) waveforms stored in ambulance-located defibrillators were collected. Predefibrillation waveforms were divided into 1.0- or 5.12-s VF waveforms. Indices in frequency domain or nonlinear analysis were calculated on the 5.12-s waveform. Simultaneously, CWT was performed on the 1.0-s waveform, and total low-band (1-3 Hz), mid-band (3-10 Hz), and high-band (10-32 Hz) energy were calculated.

RESULTS

In 152 patients with out-of-hospital cardiac arrest, a total of 233 ECG predefibrillation recordings, consisting of 164 unsuccessful and 69 successful episodes, were analyzed. Indices of frequency domain analysis (peak frequency, centroid frequency, and amplitude spectral area), nonlinear analysis (approximate entropy and Hurst exponent, detrended fluctuation analysis), and CWT analysis (mid-band and high-band energy) were significantly different between unsuccessful and successful episodes (P < 0.01 for all). However, logistic regression analysis showed that centroid frequency and total mid-band energy were effective predictors (P < 0.01 for both).

CONCLUSIONS

Energy spectrum analysis based on CWT as short as a 1.0-s VF ECG waveform enables prompt and reliable prediction of successful defibrillation.

Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

Background

Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis during ongoing chest compression could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.

Methods

For the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the coherent line removal algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.

Results

For non-optimized coherent line removal based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, n = 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio var(VF):var(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.

Discussion

The localized coherent line removal algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.

Conclusions

The localized coherent line removal algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.

CPR artifact removal in ventricular fibrillation ECG signals using Gabor multipliers

BACKGROUND AND OBJECTIVE

We present an algorithm for discarding cardiopulmonary resuscitation (CPR) components from ventricular fibrillation ECG (VF ECG) signals and establish a method for comparing CPR attenuation on a common dataset. Removing motion artifacts in ECG allows for uninterrupted rhythm analysis and reduces "hands-off" time during resuscitation.

METHODS AND RESULTS

The current approach assumes a multichannel setting where the information of the corrupted ECG is combined with an additional pressure signal in order to estimate the motion artifacts. The underlying algorithm relies on a localized time-frequency transformation, the Gabor transform, that reveals the perturbation components, which, in turn, can be attenuated. The performance of the method is evaluated on a small set of test signals in the form of error analysis and compared to two well-established CPR removal algorithms that use an adaptive filtering system and a state-space model, respectively.

CONCLUSION

We primarily point out the potential of the algorithm for successful artifact removal; however, on account of the limited set of human VF and animal asystole CPR signals, we refrain from a statistical analysis of the efficiency of CPR attenuation. The results encourage further investigations in both the theoretical and the clinical setup.

Development of the probability of return of spontaneous circulation in intervals without chest compressions during out-of-hospital cardiac arrest: an observational study

BACKGROUND

One of the factors that limits survival from out-of-hospital cardiac arrest is the interruption of chest compressions. During ventricular fibrillation and tachycardia the electrocardiogram reflects the probability of return of spontaneous circulation associated with defibrillation. We have used this in the current study to quantify in detail the effects of interrupting chest compressions.

METHODS

From an electrocardiogram database we identified all intervals without chest compressions that followed an interval with compressions, and where the patients had ventricular fibrillation or tachycardia. By calculating the mean-slope (a predictor of the return of spontaneous circulation) of the electrocardiogram for each 2-second window, and using a linear mixed-effects statistical model, we quantified the decline of mean-slope with time. Further, a mapping from mean-slope to probability of return of spontaneous circulation was obtained from a second dataset and using this we were able to estimate the expected development of the probability of return of spontaneous circulation for cases at different levels.

RESULTS

From 911 intervals without chest compressions, 5138 analysis windows were identified. The results show that cases with the probability of return of spontaneous circulation values 0.35, 0.1 and 0.05, 3 seconds into an interval in the mean will have probability of return of spontaneous circulation values 0.26 (0.24-0.29), 0.077 (0.070-0.085) and 0.040(0.036-0.045), respectively, 27 seconds into the interval (95% confidence intervals in parenthesis).

CONCLUSION

During pre-shock pauses in chest compressions mean probability of return of spontaneous circulation decreases in a steady manner for cases at all initial levels. Regardless of initial level there is a relative decrease in the probability of return of spontaneous circulation of about 23% from 3 to 27 seconds into such a pause.

Monitoring the quality of cardiopulmonary resuscitation

PURPOSE OF REVIEW

Numerous recent reports have described limitations in the quality of cardiopulmonary resuscitation. Thus, there has been increasing interest in the techniques available to monitor quality. This review focuses on the major publications since the review published by the International Liaison Committee on Resuscitation in 2005. Some key articles published prior to this time period have also been included.

RECENT FINDINGS

A number of devices can monitor various components of the quality of cardiopulmonary resuscitation. End-tidal CO2 measurement assists in confirming placement of endotracheal tubes, correlates with cardiac output and detects the return of spontaneous circulation. Turbine flow-meters monitor respiratory rate and tidal volume. Transthoracic impedance monitoring measures respiratory rate, and may assist in confirmation of endotracheal tube placement. A new mechanical device (CPREzy) and a new defibrillator/monitor allow estimation of depth (and rate) of compressions. Ventricular-fibrillation waveform analysis may facilitate better timing of defibrillation. Echocardiography detects conditions that may impair the quality of cardiopulmonary resuscitation.

SUMMARY

Many options are available to monitor the quality of cardiopulmonary resuscitation. Some have significant limitations, and others are only readily available in hospital. The use of the information from this more intensive monitoring promises to improve outcomes of cardiopulmonary resuscitation.

Outcome prediction for guidance of initial resuscitation protocol: Shock first or CPR first

BACKGROUND

Ventricular fibrillation (VF) is treated optimally with a defibrillation shock shortly after patient collapse, but may benefit from initial cardiopulmonary resuscitation (CPR) if the shock is delayed. An objective measure of potential responsiveness to defibrillation could help decide optimal initial therapy.

METHODS AND RESULTS

a new electrocardiogram (ECG) analysis algorithm was compared with response interval (call-to-shock) for prediction of patient outcome in a population of 87 VF patients in the Rochester, Minnesota area. In a retrospective analysis, both call-to-shock interval (p = 0.009) and ECG analysis (p < 0.001) predicted neurologically intact survival, with ECG analysis the stronger predictor (p = 0.034). When applied to advising initial patient treatment, ECG analysis compared favorably with the call-to-shock interval. Using a 7 min call-to-shock time criterion, 69% of patients would receive shocks first treatment using ECG analysis versus 67% using the call-to-shock interval (p = NS), 94% of survivors would retain successful shocks first treatment versus 85% (p = NS), and 48% of non-survivors receive alternate CPR-first treatment versus 45% (p = NS). Similarly, no significant differences were observed between ECG analysis and call-to-shock interval using an 8 min criterion.

CONCLUSIONS

Both call-to-shock interval and a real-time ECG analysis are predictive of patient outcome. The ECG analysis is more predictive of neurologically intact survival. Moreover, the ECG analysis is dependent only upon the patient's condition at the time of treatment, with no need for knowledge of the response interval, which may be difficult to estimate at the time of treatment.

Independent evaluation of a defibrillation outcome predictor for out-of-hospital cardiac arrested patients

We evaluated the ability of a previously derived outcome predictor to discriminate between ECG segments corresponding to return of spontaneous circulation (ROSC) or not in validation data from 136 patients with cardiac arrest. The new data used for validation were totally independent from the predictor derivation data used in the original study. Features corresponding to those used in the development of the original outcome predictor, centroid frequency, peak power frequency, spectral flatness and energy, were computed following which a second decorrelated feature set was generated. The outcome predictor was applied to the new data with good correspondence in performance (testing) to what was expected (training) with receiver operator characteristics (ROC) areas of 0.80 and 0.79, respectively. Outcome predictor performance was reproducible. As in the present study, future testing should be performed on totally independent data not included in the design of the outcome predictor to get a reliable impression of expected performance.

Waveform analysis of ventricular fibrillation to predict defibrillation

PURPOSE OF REVIEW

Ventricular fibrillation occurs during many cases of cardiac arrest and is treated with rescue shocks. Coarse ventricular fibrillation occurs earlier after the onset of cardiac arrest and is more likely to be converted to an organized rhythm with pulses by rescue shocks. Less organized or fine ventricular fibrillation occurs later, has less power concentrated within narrow frequency bands and lower amplitude, and is less likely to be converted to an organized rhythm by rescue shocks. Quantitative analysis of the ventricular fibrillation waveform may distinguish coarse ventricular fibrillation from fine ventricular fibrillation, allowing more appropriate delivery of rescue shocks.

RECENT FINDINGS

A variety of studies in animals and humans indicate that there is underlying structure within the ventricular fibrillation waveform. Highly organized or coarse ventricular fibrillation is characterized by large power contributions from a few component frequencies and higher amplitude. Amplitude, decomposition into power spectra, or probability-based, nonlinear measures all can quantify the organization of human ventricular fibrillation waveforms. Clinical data have accumulated that these quantitative measures, or combinations of these measures, can predict the likelihood of rescue shock success, restoration of circulation, and survival to hospital discharge.

SUMMARY

Many quantitative ventricular fibrillation measures could be implemented in current generations of monitors/defibrillators to assist the timing of rescue shocks during clinical care. Emerging data suggest that a period of chest compressions or reperfusion can increase the likelihood of successful defibrillation. Therefore, waveform-based prediction of defibrillation success could reduce the delivery of failed rescue shocks.

Effects of Cardiopulmonary Resuscitation on Predictors of Ventricular Fibrillation Defibrillation Success During Out-of-Hospital Cardiac Arrest

Background— Early defibrillation is considered the most important factor for restoring spontaneous circulation in cardiac arrest patients with ventricular fibrillation. Recent studies have shown that, after prolonged ventricular fibrillation, the rates of return of spontaneous circulation (ROSC) and survival are improved if defibrillation is delayed so that CPR can be given first. To examine whether CPR improves myocardial readiness for defibrillation, we analyzed whether CPR causes changes in predictors of defibrillation success calculated from the ventricular fibrillation waveform.

Methods and Results— ECG recordings were retrieved for 105 patients from an original study of 200 patients receiving CPR or defibrillation first. Altogether, 267 CPR sequences from 77 patients were identified on which the effect of CPR could be evaluated. Five predictors of ROSC (spectral flatness measure, energy, centroid frequency, amplitude spectrum relationship, and estimated probability of ROSC) were determined from a spectral analysis of the ventricular fibrillation waveform immediately before and immediately after each of the 267 sequences. CPR increased spectral flatness measure, centroid frequency, and amplitude spectrum relationship ( P <0.05, P <0.001, P <0.01). In an analysis of the effect of the duration of CPR, the probability of ROSC and amplitude spectrum relationship showed a positive change for CPR sequences lasting >3 minutes ( P <0.001, P <0.05).

Conclusions— During resuscitation from ventricular fibrillation, changes in the predictors calculated from the ventricular fibrillation waveform indicated a positive effect of CPR on the myocardium.

Analysing the ventricular fibrillation waveform

The surface electrocardiogram associated with ventricular fibrillation has been of interest to researchers for some time. Over the last few decades, techniques have been developed to analyse this signal in an attempt to obtain more information about the state of the myocardium and the chances of successful defibrillation. This review looks at the implications of analysing the VF waveform and discusses the various techniques that have been used, including fast Fourier transform analysis, wavelet transform analysis and mathematical techniques such as chaos theory.

Effects of interrupting precordial compressions on the calculated probability of defibrillation success during out-of-hospital cardiac arrest

BACKGROUND

Cardiopulmonary resuscitation (CPR) creates artifacts on the ECG and, with automated defibrillators, a pause in CPR is mandatory during rhythm analysis. The rate of return of spontaneous circulation (ROSC) is reduced with increased duration of this hands-off interval in rats. We analyzed whether similar hands-off intervals in humans with ventricular fibrillation causes changes in the ECG predicting a lower probability of ROSC.

METHODS AND RESULTS

The probability of ROSC after a shock was continually determined from ECG signal characteristics for up to 20 seconds of 634 such hands-off intervals in patients with ventricular fibrillation. In hands-off intervals with an initially high (40% to 100%) or median (25% to 40%) probability for ROSC, the probability was gradually reduced with time to a median of 8% to 11% after 20 seconds (P<0.001). In episodes with a low initial probability (0% to 25%; median, 5%), there was no further reduction with time.

CONCLUSIONS

The interval between discontinuation of chest compressions and delivery of a shock should be kept as short as possible.

Bench to bedside: resuscitation from prolonged ventricular fibrillation

Ventricular fibrillation (VF) remains the most common cardiac arrest heart rhythm. Defibrillation is the primary treatment and is very effective if delivered early within a few minutes of onset of VF. However, successful treatment of VF becomes increasingly more difficult when the duration of VF exceeds 4 minutes. Classically, successful cardiac arrest resuscitation has been thought of as simply achieving restoration of spontaneous circulation (ROSC). However, this traditional approach fails to consider the high early post-cardiac arrest mortality and morbidity and ignores the reperfusion injuries, which are manifest in the heart and brain. More recently, resuscitation from cardiac arrest has been divided into two phases; phase I, achieving ROSC, and phase II, treatment of reperfusion injury. The focus in both phases of resuscitation remains the heart and brain, as prolonged VF remains primarily a two-organ disease. These two organs are most sensitive to oxygen and substrate deprivation and account for the vast majority of early post-resuscitation mortality and morbidity. This review focuses first on the initial resuscitation (achieving ROSC) and then on the reperfusion issues affecting the heart and brain.