Recent evidence indicates that cardiopulmonary resuscitation (CPR) during both in- and out-of-hospital cardiac arrest is characterised by frequent and long interruptions in chest compressions [1, 2]. This reduces vital organ perfusion [3], and in animal experiments, increased length of chest compression pause before shock correlates with reduced rates of return of spontaneous circulation (ROSC) and survival [4–6]. Edelson et al. [7] reported that successful defibrillation, defined as removal of ventricular fibrillation (VF) for at least 5 seconds, was associated with shorter pre-shock pauses in man. Eilevstjønn et al. [8] reported a similar association for shocks with ROSC outcome but only reported the median length of pre-shock pauses for ROSC and no-ROSC shocks. Identifying in detail how pausing in chest compressions affects the vitality of the myocardium, and thereby the probability of ROSC (P _ROSC) after defibrillation, is important because it affects treatment priorities before a defibrillation, a vital stage of the resuscitation effort.

During VF and ventricular tachycardia (VT) one can calculate ROSC-predictors from the electrocardiogram (ECG) reflecting the P _ROSC associated with defibrillation [9–12]. In general terms, we can say that ROSC-predictors reflect the coarseness of the ECG or the vitality of the myocardium. ROSC-predictors have been affected positively by compression sequences both in animals [13] and man [14] and negatively by periods with no chest compressions both in animals [15] and man [16]. Unfortunately, we have now realised that the statistical analysis performed in the last article [16] was flawed. This is explained in Appendix 1. In the current work therefore, we reinvestigate the effect of interruptions of chest compressions on P _ROSC calculated from the ECG. Our hypothesis was that P _ROSC decreases during such interruptions and that the size of this effect may depend on the absolute value of P _ROSC. We use a ROSC-predictor that represents the most accurate, currently available estimate of P _ROSC [11], and a statistical methodology that properly handles short time variations of the P _ROSC estimate and the fact that the P _ROSC level varies from interval to interval [17]. We handle the problem not solved in Eftestol et al. [16] by applying an adequate regression to the data, representing the underlying trends in P _ROSC development, and use this to derive our results. Further, we compare the results with relevant data from investigations of animal and human sudden cardiac arrest data.

Data were collected by the respective emergency medical services in an observational prospective study of out-of-hospital cardiac arrest patients in Akershus (Norway), Stockholm (Sweden) and London (UK) in the period March 2002 to September 2004 [1, 18]. The appropriate ethical boards at each site approved the study, and the need for informed consent from each patient was waived as decided by these boards in accordance with paragraph 26 of the Helsinki declaration for human medical research. The study is registered as a clinical trial at http://www.clinicaltrials.gov/, (NCT00138996). Continuous ECG, transthoracic impedance, and chest compression depth measurements were collected using a modified Heartstart 4000 (Phillips Medical Systems, Andover, MA, USA) (Heartstart 4000SP (Laerdal Medical, Stavanger, Norway)) and patient records registered according to the Utstein template [19]. The ECG was obtained through the defibrillator's self-adhesive defibrillation pads positioned in lead II equivalent positions and the same types of electrodes were used throughout the data collection. The signal was digitally recorded with a sampling rate of 500 samples per second and 16 bits resolution. Before digital sampling the analogue ECG signal was filtered with a second order bandpass filter with passband of 0.9–50 Hz and before analysis a 48 tap lowpass digital filter with an upper passband edge of 30 Hz was applied to the digitised ECG to remove any 50 Hz power line noise. Regarding the mapping-dataset (described below) ROSC was indicated either by a clinically detected pulse or by changes in the transthoracic impedance >50 mΩ coincident with QRS complexes [1]. Compression depth measurements were used to identify the presence and absence of chest compressions. Further details about registrations and methodology have been given elsewhere [1].

Describing logslope development

Using the statistical software S-plus (Insightful Corporation, Seattle, WA, USA), we fitted a linear mixed-effects model to the interval-dataset [17]. The linear mixed-effects model takes into account the fact that the general level of logslope varies between intervals by allowing individual variation in the regression parameters from interval to interval. The logslope values are the response variable in the model, and the development of this described with a polynomial in t, where t is time (seconds) after chest compressions stopped minus 10 seconds (t = t _org-10). The subtraction is performed in order to decorrelate the covariates of the model (t, t ², etc). If i identifies the interval (cluster), the model for the development of logslope with time is given by:

$logslop{e}_i\left(t\right)={\displaystyle \sum _{k=0}^K{\beta }_k\cdot t^k}+{\displaystyle \sum _{k=0}^M{U}_{ik}\cdot t^k}+{\epsilon }_{it}$

(3)

K is the polynomial degree for the fixed-effects part of the model and M the degree for the random-effects part (accounting for variations between intervals) and K ≥ M. Gaussian model residuals ε _it are assumed and also a multivariate Gaussian distribution of the random terms U _ik , k ∈ 1, ..., M

(4)

The S-plus function used to fit the model, lme, has several different possible ways of modelling correlation between residuals within each interval (cluster), and possible heteroschedasticity in the data [17]. We chose the optimal correlation model, variance model (for heteroschedastic data) and the necessary polynomial degree (K and M), in that order, by comparing Akaike information criterion (AIC) values. Polynomial degrees up to four were tested and among possible models with similar AIC values the model of lowest polynomial degrees was chosen. We did not experiment with other variance covariates than the fitted-model value (software default). For mathematical details about the modelling of correlation between residuals and heteroschedastic data we refer to Pinheiro and Bates [17]. After choosing our final model it was validated by plotting normal probability plots for the model residuals and the random terms, the distribution of residuals against time and fitted-model value and the response against fitted values.

Data from 530 defibrillation attempts given to 86 patients were included in the mapping-dataset and used to estimate the logslope to P _ROSC mapping. In the mapping-dataset logslope has an area under the ROSC curve of 0.876 when used to predict which defibrillations will result in ROSC. A histogram of these data is plotted along with the estimated mapping in Figure 1.

Logslope values from 911 intervals without chest compressions were included in the interval-dataset and the median interval without chest compressions had five analysis windows. Out of 6229 analysis windows, 17.6% were excluded because of noise in the ECG, leaving 5138. From the complete records of 363 patients, 134 patients had intervals that were included. By comparing AIC values of different candidate models we identified a linear mixed-effects model with an exponential spatial correlation model with nugget effect, polynomial degrees K = 1 and M = 1, and an exponential variance function. Given that a linear model in t (polynomials of degree one) was chosen shifting of the time covariate is not necessary, but in order to simplify calculations a time shift of -3 seconds (t = t _org -3) was used when estimating the final model.

The data in the interval-dataset and how the linear mixed-effects model represents these are illustrated by Figure 2. The figure shows the logslope values and fitted-model values of three intervals. We observe that the intervals are of different length, have logslope values at different levels, and that the fitted lines are allowed to have different intercepts and slopes. The fitted values of Standard deviation(U ₀), Standard deviation(U ₁) and correlation(U ₀, U ₁) given in Table 1 describe how the intercepts and slopes are distributed in our data.

Using Equation (8) in Appendix 2 we computed a set of expected developments of P _ROSC given different P _ROSC values 3 seconds into an interval, shown in Figure 3. According to the β ₀ parameter, the logslope value 3 seconds into an interval has a Gaussian distribution with mean -5.224 and standard deviation 0.594 (Std(U ₀)) in our dataset, and we characterise each development by the quantile the initial logslope value (corresponding to the chosen initial P _ROSC value) is at in this distribution. About 60% of the intervals have initial P _ROSC below 0.1.

Using the approach described in Appendix 2 we estimate that if all the intervals without chest compressions were shocked with 3 seconds pre-shock pause the mean P _ROSC would be 0.126, while with 27 seconds pause it would be 0.0971. The estimated relative decrease in mean P _ROSC, 1-(0.0971/0.1255) ≈ 0.23, (95% CI: 0.17–0.29) is comparable with that in each of the developments in Figure 3, meaning that independent of the absolute P _ROSC level about 23% of the chance of ROSC will be lost with increasing the pre-shock pause in chest compressions from 3 to 27 seconds.

Our analysis shows that P _ROSC, as determined from ECG analysis, decreases in a steady manner with time in VF/VT intervals without chest compressions for all initial values of P _ROSC. This shows that limiting interruption in chest compressions is important for patients in all states and that every second without perfusion has a negative effect on P _ROSC. The current results therefore give support for a strong focus on improving CPR quality.

Unlike the results of Eftestol et al. [16], the current results show that P _ROSC does not decrease sharply during the first 5 seconds of an interruption. Therefore, a pre-shock pause in compressions of a couple of seconds to ensure the safety of EMS personnel, or to perform rhythm analysis on artefact-free ECG, may be acceptable. Concerning safety, pre-shock pauses of 1 to 2 seconds might be sufficient as it has been argued that the risks of accidental defibrillation of resuscitation providers have been over emphasised [22]. Further, the accuracy of rhythm analysis during ongoing compressions might in the future be improved as a result of either improved artefact removal algorithms or of using dedicated ECG recording electrodes generating fewer artefacts than combined recording and defibrillation electrodes [23]. This will also reduce the need for pre-shock pauses in compressions. Although the current results show that pre-shock interruptions in compressions should be minimised, they do not indicate that it is critical, although favourable, for the outcome of resuscitation to compress during defibrillation. This is because there is no indication of a sharp decrease in P _ROSC during the first few seconds of an interruption.

The effect of pre-shock interruptions in chest compressions on resuscitation outcome or ROSC has also been studied in animals [4–6]. In agreement with the current results all three studies found a clear negative effect of longer pre-shock interruptions. Edelson et al. [7] found strong negative correlation between duration of pre-shock interruptions on first shock success in humans, defined as removal of VF for at least 5 seconds following defibrillation, but no significant effect of pre-shock pause duration on ROSC or survival to hospital discharge. It was stated that one possible reason for this was the limited dataset with lower incidence of ROSC and survival than of shock success with the possibility of a statistical type II error. Eilevstjønn et al. [8] found that the median length of pre-shock interruption of chest compressions for ROSC was 15 seconds versus 18 seconds for no-ROSC shocks (P = 0.008), but did not quantify further the negative effect of increasing length of interruptions.

A limitation of our approach is that the effect was studied indirectly by using the established fact that the P _ROSC can be estimated from the ECG waveform [9–12], instead of by direct analysis of the relation between pre-shock pause length and rate of ROSC. The latter could however, only have identified how the mean probability of ROSC for the population would be influenced by increasing pre-shock pauses in CPR, and could not have identified possible differences in the development for cases with different probabilities of ROSC at the start of the pre-shock interval without chest compressions. Using ECG analysis we can estimate P _ROSC continuously for every available interval without chest compressions that follow an interval with chest compressions and, therefore, estimate the expected development of P _ROSC for cases at different starting levels. A further limitation is that we excluded from the analysis all segments where the ECG apparently was affected by a pacemaker and one group of patients was therefore excluded from the analysis. Performing a re-analysis including also the data with noise did however only produce minor changes in the results. At last, the effects of right ventricular dilation and left ventricular contraction occurring during interruptions in compressions, described by Chamberlain et al. [24], may not be reflected in the ECG waveform. The current results may in this respect possibly underestimate the detrimental effect of interrupting compressions.

We have shown that the probability of ROSC estimated from the ECG decreases in a steady manner with increasing pre-shock pauses in chest compressions. Regardless of initial level, there is a relative decrease in the estimated probability of ROSC of about 23% from 3 to 27 seconds, or, in other words, a 1% relative decrease for every second into such a pause.

By choosing different values for x we can calculate the expected P _ROSC development for cases with different starting values. For a given starting value of P _ROSC, the corresponding logslope value x is found using Equation (2). If we make no specific choice of logslope at t = 0, but modify Equation (8) to integrate out the random intercept U_h0 as well as the other random terms, we can obtain an estimate for how the mean P _ROSC develops with time, pooling together cases at all P _ROSC levels.