(Circulation. 2001;103:1656.)
© 2001 American Heart Association, Inc.
Clinical Investigation and Reports |
Scaling Exponent Predicts Defibrillation Success for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest
From the Department of Emergency Medicine, University of Pittsburgh (Pa).
Correspondence to Dr Clifton W. Callaway, Department of Emergency Medicine, University of Pittsburgh, 230 McKee Pl, Suite 400, Pittsburgh, PA 151213. E-mail callawaycw{at}msx.upmc.edu
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Abstract |
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Background—Defibrillator shocks often fail to terminate ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA), and repeated failed shocks can worsen the subsequent response to therapy. Because the VF waveform changes with increasing duration of VF, it is possible that ECG analyses could estimate the preshock likelihood of defibrillation success. This study examined whether an amplitude-independent measure of preshock VF waveform morphology predicts outcome after defibrillation.
Methods and Results—Clinical data and ECG recordings from an automated external defibrillator were obtained for 75 subjects with OOHCA in a suburban community with police first responders and a paramedic-based emergency medical system. An estimate of the fractal self-similarity dimension, the scaling exponent, was calculated off-line for the VF waveform preceding shocks. Success of the first shock was determined from the recordings. Return of pulses and survival were determined by chart review. The first shock resulted in an organized rhythm in 43% of cases, and 17% of cases survived to hospital discharge. A lower mean value of the scaling exponent was observed for cases in which the first defibrillation resulted in an organized rhythm (P=0.004), for cases with return of pulses (P=0.049), and for cases surviving to hospital discharge (P<0.001). Receiver operator curves revealed the utility of the scaling exponent for predicting the probability of restoring an organized rhythm (area under the curve=0.70) and of survival (area under the curve=0.84).
Conclusions—The VF waveform in OOHCA can be quantified with the scaling exponent, which predicts the probability of first-shock defibrillation and survival to hospital discharge.
Key Words: fibrillation • heart arrest • defibrillation • electrocardiography • survival
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Ventricular fibrillation (VF) is the most common initial arrhythmia associated with sudden cardiac death. Electrical defibrillation remains the mainstay of therapy, but the efficacy of electrical defibrillation decreases with increasing duration of ischemia.1 2 3 Decreasing the delay to defibrillation by introduction of automated external defibrillators (AED) or other modifications improves survival from cardiac arrest in certain subsets of patients.4 5 However, overall recovery from cardiac arrest outside the hospital remains low, perhaps because many applications of AEDs still occur beyond the time during which defibrillation is likely to be successful.6 Investigators have suggested that brief periods of artificial circulation or other tailored therapy could improve outcome for selected patients in whom defibrillation is unlikely to succeed.6 However, identification of these subsets of patients remains problematic.
The waveform of VF is one possible predictor for the likelihood of successful defibrillation.7 8 9 10 VF waveform analysis is motivated by the obvious visual differences between the high-amplitude, lower-frequency waveform seen in early VF and the low-amplitude, higher-frequency waveform seen in later VF. However, both amplitude7 8 and frequency measures9 10 are difficult to apply in practice. For example, the amplitude is dependent on recording conditions and equipment, whereas frequency measures can have identical values both early and late during VF.11 In contrast, the fractal self-similarity dimension is a measure derived from nonlinear dynamics that provides a quantitative description of VF waveform morphology.12 13 A simple, straight-line waveform will have a fractal dimension approaching 1, whereas complex space-filling waveforms will have fractal dimensions approaching 2. The fractal dimension is independent of signal amplitude and increases with duration of VF.12 13
In this study, we examined whether the fractal dimension of the VF waveform recorded by AEDs predicted the success of defibrillation in a series of patients with out-of-hospital cardiac arrest (OOHCA). In particular, we calculated the scaling exponent as a measure of the self-similarity dimension in the VF waveform12 14 and assessed the ability of the scaling exponent to predict the success of defibrillation by the AED. As calculated, the scaling exponent is linearly related to the Hurst exponent but is easier to calculate.13 Because the likelihood of successful defibrillation decreases over time and scaling dimension increases over time, we hypothesized that a lower scaling dimension in the initial VF waveform would be correlated with successful defibrillation.
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Methods |
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This study was approved by the Institutional Review Board of the University of Pittsburgh. Police in 7 suburban municipalities with a total population of 145 000 were provided with Lifepak 300 semiautomated defibrillators (Physio-Control Inc). In each community, police arrive at medical emergencies before paramedics in
50% of cases. Data were collected between February 1, 1992, and
January 30, 1998. Police training and data collection methods were
detailed
previously.15 16 17 Subjects were adult patients (>18 years old) with nontraumatic cardiac arrest for whom an initial rhythm of VF was recorded by the AED. For each patient, the recording leads/defibrillation pads (FAST-PATCH, Physio-Control Inc) of the AED were applied to the chest. The ECG waveform was recorded on analog tapes along with an audio record. After each AED use, tapes and clinical information were collected by the investigators. All available ECG recordings were included.
Analysis of the ECG recording was conducted off-line. The ECG waveform was digitized at 400 samples per second from analog tape recordings by a PC-compatible computer and analog/digital converter (PowerLab, AD Instruments). The resulting digital records were analyzed with the Chart software package (AD Instruments) as well as customized software written in C.12 After selecting the epoch, all calculations were performed entirely by the computer.
Scaling exponents were calculated for 5.12-second epochs (2048 samples) of VF, beginning 10 seconds before the first shock. This sampling point was selected as an interval free of artifact, during which the AED instructed the first responders to stand clear. A sample size of 5.12 seconds or 2048 points provides a reliable estimate of the scaling exponent.12 The value of the scaling exponent during untreated VF is similar between consecutive epochs but increases over minutes.12 13
The scaling exponent is an estimate of the fractal
self-similarity dimension that characterizes the "roughness" or
"smoothness" of the ECG
waveform.14 Details of its
derivation have been reported
previously.12 Briefly, a
5.12-second epoch of the ECG waveform was expressed as a time series of
2048 voltage measurements, X(1), X(2), X(3)...X(i).... The sum, L, of
absolute potential differences between each ith measurement of the ECG
waveform, X(i) and the measurement k points later, X(i+k), was
calculated over the entire epoch. The separation or lag, k, was varied
from 1 (differences between each measurement) to 2000 (differences
between each 2000th measurement). The sums for each value of k were
normalized by a factor, [2048/k(2048-k)], to account for the
different numbers of intervals sampled for different values of k within
a finite epoch to describe a function, L(k).
 | (1) |
The resulting values of L(k) were fitted to an
exponential function describing the scaling relation between L(k) and the lag k.
 | (2) |
).
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The scaling exponent was compared between cases with
successful or failed shocks. Successful shocks were defined as cases in
which VF was converted to an organized rhythm that was sustained for
30 seconds with or without a pulse. Failed shocks either resulted in
continued VF or asystole (unorganized rhythms). Although asystole is
considered a successful defibrillation from an electrophysiological
standpoint,18 defining
success as restoration of an organized postshock rhythm is more
relevant to clinical
outcome.19 Preliminary
analyses indicated that the scaling measures did not differ between
subjects with postshock asystole and subjects with persistent VF. Other
outcome measures included the return of a spontaneous circulation
(ROSC) at any time during the resuscitation, survival to hospital
admission, and survival to hospital discharge.
The mean scaling exponent was compared between cases with
different clinical outcomes by use of the
t test. The proportions of
subjects with good clinical outcomes were compared for different ranges
of the scaling exponent by means of 
2
analysis. Associations between dichotomous categorical variables and
continuous variables were examined by means of the
t test. Multiple linear
regression was used to determine the association between the scaling
exponent and other continuous variables. Stepwise logistic regression
was performed to evaluate candidate variables (age, sex, time from call
received until shock, absence or presence of bystander CPR, whether
collapse was witnessed or unwitnessed, and value of the scaling
exponent) as predictors of the probability of successful defibrillation
by the first shock (SPSS-PC, SPSS Inc). Shock success was expressed as
a binary variable. At each step, the candidate predictor variable with
the highest association with the dependent variable was introduced into
the model, and variable entry was halted when the resulting improvement
in the fit of the model was not significant. Identical regression was
performed to identify contributing variables that predict the
likelihood of ROSC, admission to the hospital, or discharge from the
hospital. For all statistics, the criterion for significance was a
level of
P<0.05.
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Results |
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AED recordings were obtained from 75 cases of VF cardiac arrest in which defibrillation was attempted before other advanced life support. During the study interval, the community Emergency Medical Systems responded to 649 cases of cardiac arrest, and VF was the initial rhythm in 222 cases. Police first responders applied an AED and attempted defibrillation in 89 cases. The 75 cases used for this analysis represent all cases for which ECG recordings were successfully retrieved from the AED.
The first shock by the AED successfully converted VF
into an organized rhythm in 31 cases (41.3%). Successful
defibrillation by the first AED shock was associated with ROSC during
the resuscitation
(Table,
2=4.05,
df=1,
P=0.044). Although more
of the subjects with successful AED defibrillation were admitted to the
hospital, this difference was not significant. The proportion of
subjects discharged alive from the hospital was higher after successful
AED defibrillation
(Table,
2=5.05,
df=1,
P=0.025). Age, sex ratio,
proportion in which collapse was witnessed, proportion with bystander
CPR, and delay from receiving call to delivery of shock did not differ
between groups with successful and failed shocks
(Table).
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Analysis of the VF waveform confirmed that a scaling relation existed between the lag, k, and the sum of potential changes, L(k). The region of scaling was consistently observed for values of log(k) between 0.6 and 1.2, corresponding to sampling frequencies between 25 and 100 Hz. The least-squares line fitted by computer to this scaling region had high correlation coefficients (range, 0.967 to 0.999). The value of the scaling exponent ranged from 1.11 to 2.00.
Mean values for the scaling exponent were lower for groups
with better clinical outcomes
(Figure 2). The scaling exponent differed between cases with
successful first shocks and those with failed first shocks (t=2.94,
df=73,
P=0.004), between cases with
ROSC and with no ROSC (t=2.79,
df=68.85,
P=0.049), and between cases
with survival to hospital discharge and with no survival (t=5.26,
df=60.97,
P<0.001). There was a trend
toward lower scaling exponents in cases with survival to hospital
admission compared with cases pronounced dead in the emergency
department (t=1.02, df=66,
P=0.059).
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Lower values of the scaling exponent were associated with an
increased probability of successful defibrillation
(Figure 3). The proportion of subjects successfully
defibrillated by the AED was highest (73%) when the initial scaling
exponent was in the range of 1.00 to 1.19
(2=11.31,
df=4,
P=0.023). Furthermore, the
value of the scaling exponent was related to the proportion of subjects
who had ROSC (2=11.16,
df=4,
P=0.025), who survived to
hospital admission (2=11.23,
df=4,
P=0.024), and who survived to
hospital discharge (2=17.57,
df=4,
P=0.0015).
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Stepwise logistic regression identified the scaling exponent
and age as the only variables associated with first-shock
defibrillation success. Including other variables listed in the
Table
did not significantly improve the resulting model. Coefficient
estimates (±SE) were 4.36±1.67 for scaling exponent and 0.046±0.022
for age. Logistic regression identified the scaling exponent as the
only variable associated with ROSC, admission to the hospital, and
survival to hospital discharge. To confirm the incremental value of VF
waveform analysis for predicting defibrillation success, a stepwise
logistic regression model was examined in which all other candidate
variables (age, sex, witnessed, bystander CPR, time from call receipt
to paramedic arrival, and time from call receipt to shock) were forced
into the model in the first step. The scaling exponent was then added
in the second step, resulting in a significant improvement in the -2
log likelihood of the model (improvement=6.10,
df=5,
P=0.0135). Because amplitude
and scaling exponent covaried, there was no incremental benefit of
scaling exponent when amplitude was included in the model.
Receiver operator curves (ROC) were constructed to
describe the utility of the scaling exponent by itself for predicting
defibrillation by the first shock, ROSC, or discharge from the hospital
(Figure 4). Areas under these curves were 0.70, 0.71, and
0.84, respectively. For comparison, the centroid frequency and
root-mean-square amplitude were calculated from the same segments of
VF. Centroid frequency was correlated with scaling exponent
(R2=0.204,
F[1,73]=18.7, P<0.001).
Areas under the ROC curve describing the utility of centroid frequency
for predicting defibrillation by the first shock, ROSC, or discharge
from the hospital were 0.53, 0.53, and 0.51, respectively.
Root-mean-square amplitude also was related to the scaling exponent. A
semilogarithmic relation between these variables provided the best fit
(R2=0.788,
F[1,73]=271.5, P<0.001).
Consistent with the close relation between amplitude and scaling
exponent, the areas under the ROC graphs indicated that amplitude could
predict defibrillation success, ROSC, and discharge from the hospital.
Areas under these curves were 0.75, 0.71, and 0.84,
respectively.
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Several factors may influence the value of the scaling exponent. Among continuous variables, the time from call receipt to shock delivered was positively correlated with the scaling exponent (R2=0.197, F[1,61]=15.01, P=0.001). Age was not associated with the value of the scaling exponent. The scaling exponent was lower in cases with witnessed arrest (1.32±0.18, n=54) than in cases with unwitnessed arrest (1.46±0.28, n=21) (t=2.10, df=26.83, P=0.045). The value of the scaling exponent did not differ between men and women or between cases with or those without bystander CPR.
The scaling exponent increased with duration of
untreated VF
(Figure 5). In witnessed arrests (n=45), in which the time of
emergency call receipt is a valid surrogate for the time of onset of
VF, the interval from call receipt until first shock was positively
correlated with scaling exponent
(R2=0.157,
F[1,43]=8.01, P=0.007).
Estimates of the time of first shock were not available for 9 cases. In
the subset of subjects with witnessed cardiac arrests and no bystander
CPR (n=26), the association between interval from call receipt to first
shock was associated more strongly with the increase in scaling
exponent
(R2=0.383,
F[1,24]=14.91, P=0.0007).
Furthermore, the calculated rate of increase in scaling exponent for
subjects with witnessed arrests and bystander CPR (slope=0.026; 95%
CI, -0.023, 0.074) exhibited a trend toward being lower than the rate
for subjects with witnessed arrests and no bystander CPR (slope=0.047;
95% CI, 0.022, 0.073).
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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These data illustrate the use of a measure derived from fractal geometry for quantifying the VF waveform and predicting response to shocks. This measure, the scaling exponent, estimates the self-similarity dimension of the waveform.14 The scaling exponent provides a measure that is independent of the absolute amplitude of the recorded signal. Thus, the scaling exponent can be compared between recordings made with different equipment and different electrodes. For example, the present data confirm that the scaling exponent can be measured in human VF during OOHCA and that it has similar magnitude and behavior in humans and swine.12 13
Significant improvements are needed for treatment of OOHCA. In one series, shocks failed to terminate VF or resulted in asystole 75% of the time.20 Many previous studies have noted that repeated unsuccessful defibrillation attempts can increase myocardial damage and dysfunction21 22 as well as increase energy requirements for subsequent defibrillation.23 Furthermore, postshock ST-segment displacement increases with increasing energy delivered and is believed to increase with multiple shocks.24 These data emphasize the need to improve the likelihood of successful defibrillation by the first delivered shock in OOHCA.
Although decreasing the interval from collapse to shock can increase resuscitation success,5 alternative strategies may improve outcomes for the remainder of patients with prolonged VF. Studies in animals have provided evidence that reperfusion and reoxygenation of the fibrillating heart increase the probability of achieving organized electrical activity after shock.19 25 26 Likewise, introducing a 90-second period of CPR before attempting defibrillation improves survival in humans.6 This beneficial effect of antecedent CPR affects predominantly those cases in which the delay before arrival of therapy exceeded 4 minutes.
Development of a tool for stratifying patients according to
their probability of responding to standard treatment could allow
application of these alternative algorithms without delaying
defibrillation for patients in whom it is likely to succeed. In
particular, the scaling exponent described here may be a correlate of
the elapsed ischemic interval before attempting defibrillation
(Figure 5). Although the sensitivity of this measure for
identifying cases for which defibrillation will succeed may not justify
withholding shocks altogether, prior knowledge of the probability of
shock success could help guide the timing or energy of electrical
therapy. For example, subjects with prolonged VF and high scaling
exponents may benefit from brief periods of artificial perfusion before
electrical
therapy.6
Prior studies used quantitative measures of the VF waveform morphology to estimate the duration of VF and its likelihood of successful defibrillation.7 8 9 10 For example, centroid frequency, a measure based on spectral analysis, varies with the duration of VF.11 Use of centroid frequency is limited by its multiphasic change with time. Because of this multiphasic profile, a particular value of the centroid frequency is not uniquely associated with a particular duration of VF. Other studies have found that the amplitude of the VF waveform can predict successful defibrillation.7 8 However, amplitude measurements can be affected by electrode configuration, placement, body habitus, impedance, and recording equipment.
Nonlinear dynamic measures, such as the fractal
dimension, are more appropriate for quantifying nonperiodic waveforms
such as VF. Complex dynamic behavior has been described in several
models of
VF.27 28
Furthermore, measurements based on nonlinear dynamics have previously
been developed for the VF waveform in stabilized animal
preparations.29 In humans
and in unperfused animals, the VF waveform does not exhibit true
fractal structure because it has scaling relations and self-similarity
only over a limited range of time scales
(Figure 1)30 and
because it is not stationary over
time.12 Nevertheless,
measures of fractal dimension over limited ranges have proven useful
for characterizing many natural
phenomena.31 Although
amplitude and scaling exponent are correlated in this data set, the
calculated value of the scaling exponent does not depend on the
absolute amplitude of the
waveform.12 13
Thus, this measure can be compared between different recording
conditions and even between different studies.
The present data suggest that the structure of the human VF
waveform evolves over time
(Figure 5). A similar increase in the scaling exponent over
time has been reported in
swine.12 Conceptually, the
scaling exponent distinguishes coarse VF (low scaling exponent) from
fine VF (high scaling exponent). An alternative interpretation is that
low values of the scaling exponent reflect more large-scale structure
in the VF waveform, whereas high values of the scaling exponent reflect
less large-scale structure. Previous investigations in dogs suggest
that stronger correlations between the time of depolarization in
spatially separated parts of the myocardium are associated with a
greater likelihood of successful
defibrillation.32
Furthermore, the correlation between electrical depolarization in
spatially separated pieces of myocardium decreases over
time.27 It is possible that
the scaling exponent is a macroscopic measure of the spatial and
temporal correlation of myocardial depolarization. Consequently, lower
scaling exponents are associated with a greater likelihood of
successful defibrillation
(Figures 2 and 3). Taken together, these data suggest that
analyses of VF waveforms provide some insight into the physiological
state of the subject, which is perhaps related to the total duration of
ischemia.
Summary
Human VF exhibits a time-dependent structure that can
be quantified by the amplitude-independent fractal self-similarity
dimension. This scaling exponent is an estimate of the fractal
dimension. Over time, the scaling exponent increases, reflecting a loss
of structure in the VF waveform. Moreover, the absolute value of the
scaling exponent predicts the likelihood of successful defibrillation
and ultimate survival. Even with consideration of other potential
factors that may influence resuscitation, this tool remains predictive
and may have utility in guiding future therapeutic
trials.
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Acknowledgments |
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This study was supported by a grant from the Emergency Medicine Foundation (Dallas, Tex).
Received September 26, 2000; revision received December 1, 2000; accepted December 13, 2000.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Eisenberg M, Bergner L, Hallstrom A. Paramedic programs and out-of-hospital cardiac arrest, I: factors associated with successful resuscitation. Am J Public Health. 1979;69:30–38.
2. Eisenberg M, Copass M, Hallstrom A. Treatment of out-of-hospital cardiac arrest with rapid defibrillation by emergency medical technicians. N Engl J Med. 1980;302:1379–1383.[Abstract]
3. Roth R, Stewart R, Rogers K. Out-of-hospital cardiac arrest: factors associated with survival. Ann Emerg Med. 1984;13:237–243.[Medline] [Order article via Infotrieve]
4. Auble T, Menegazzi J, Paris P. Effect of out-of-hospital defibrillation by basic life support providers on cardiac arrest mortality: a metaanalysis. Ann Emerg Med. 1995;25:642–647.[Medline] [Order article via Infotrieve]
5.
Stiell I, Wells G,
Field B, et al. Improved out-of-hospital cardiac arrest survival
through the inexpensive optimization of an existing defibrillation
program: OPALS study phase II.
JAMA. 1999;281:1175–1181.
6.
Cobb L, Fahrenbruch
C, Walsh T, et al. Influence of cardiopulmonary resuscitation prior to
defibrillation in patients with out-of-hospital ventricular
fibrillation. JAMA. 1999;281:1182–1188.
7. Callaham M, Braun O, Valentine W, et al. Prehospital cardiac arrest treated by urban first-responders: profile of patient response and prediction of outcome by ventricular fibrillation waveform. Ann Emerg Med. 1993;22:1664–1677.[Medline] [Order article via Infotrieve]
8. Weaver W, Cobb L, Dennis D, et al. Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest. Ann Intern Med. 1985;102:53–55.
9. Brown C, Dzworczyk R. Signal analysis of the human electrocardiogram during ventricular fibrillation: frequency and amplitude parameters as predictors of successful countershock. Ann Emerg Med. 1996;27:184–188.[Medline] [Order article via Infotrieve]
10.
Strohmenger H,
Linder K, Lurie K, et al. Frequency of ventricular fibrillation as a
predictor of defibrillation success during cardiac surgery.
Anesth Analg. 1994;79:434–438.
11. Brown C, Dzwonczyk R, Werman H, et al. Estimating the duration of ventricular fibrillation. Ann Emerg Med. 1989;18:1181–1185.[Medline] [Order article via Infotrieve]
12. Callaway C, Sherman L, Scheatzle M, et al. Scaling structure of the electrocardiographic waveform during prolonged ventricular fibrillation in swine. Pacing Clin Electrophysiol. 2000;23:180–191.[Medline] [Order article via Infotrieve]
13. Sherman L, Callaway C, Menegazzi J. Ventricular fibrillation exhibits dynamical properties and self-similarity. Resuscitation. 2000;47:163–173.[Medline] [Order article via Infotrieve]
14. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D. 1988;31:277–283.
15. Davis E, Mosesso V. Performance of police first responders in utilizing automated external defibrillation on victims of sudden cardiac arrest. Prehosp Emerg Care. 1998;2:101–107.[Medline] [Order article via Infotrieve]
16. Davis E, McCrorry J, Mosesso V. Institution of a police automated external defibrillation program: concepts and practice. Prehosp Emerg Care. 1999;3:60–65.[Medline] [Order article via Infotrieve]
17. Mosesso V, Davis E, Auble T, et al. Use of automated external defibrillators by police officers for treatment of out-of-hospital cardiac arrest. Ann Emerg Med. 1998;32:200–207.[Medline] [Order article via Infotrieve]
18. Poole J, White R, Kanz K, et al. Low-energy impedance-compensating biphasic waveforms terminate ventricular fibrillation at high rates in victims of out-of-hospital cardiac arrest. J Cardiovasc Electrophysiol. 1997;8:1373–1385.[Medline] [Order article via Infotrieve]
19.
Niemann J, Cairns
C, Sharma J, et al. Treatment of prolonged ventricular fibrillation:
immediate countershock versus high-dose epinephrine and CPR preceding
countershock. Circulation. 1992;85:281–287.
20. Hargarten K, Stueven H, Waite E, et al. Prehospital experience with defibrillation of coarse ventricular fibrillation: a ten-year review. Ann Emerg Med. 1990;19:157–162.[Medline] [Order article via Infotrieve]
21. Gaba D, Maxwell M, Merlone S, et al. Internal countershock produces myocardial damage and lactate production without myocardial ischemia in anesthetized dogs. Anesthesiology. 1987;66:477–482.[Medline] [Order article via Infotrieve]
22. Ehsani A, Ewy G, Sobel B. Effects of electrical countershock on serum creatine phosphokinase (CPK) isoenzyme activity. Am J Cardiol. 1976;37:12–18.[Medline] [Order article via Infotrieve]
23. Bardy G, Ivey T, Johnson G, et al. Prospective evaluation of initially ineffective defibrillation pulses on defibrillation success during ventricular fibrillation in survivors of cardiac arrest. Am J Cardiol. 1988;62:718–722.[Medline] [Order article via Infotrieve]
24.
Bardy G,
Marchlinski F, Sharma A, et al. Arrhythmias/pacing/heart block:
multicenter comparison of truncated biphasic shocks and standard damped
sine wave monophasic shocks for transthoracic ventricular
defibrillation. Circulation. 1996;94:2507–2514.
25. Barton C, Manning J, Batson N. Effect of selective aortic arch perfusion on median frequency and peak amplitude of ventricular fibrillation in a canine model. Ann Emerg Med. 1996;27:610–616.[Medline] [Order article via Infotrieve]
26. Strohmenger H, Linder K, Prengel A, et al. Effects of epinephrine and vasopressin on median fibrillation frequency and defibrillation success in a porcine model of cardiopulmonary resuscitation. Resuscitation. 1996;31:65–73.[Medline] [Order article via Infotrieve]
27. Witkowski F, Leon L, Penkoske P, et al. Spatiotemporal evolution of ventricular fibrillation. Nature. 1998;392:78–82.[Medline] [Order article via Infotrieve]
28. Gray R, Pertsov A, Jalife J. Spatial and temporal organization during cardiac fibrillation. Nature. 1998;392:75–78.[Medline] [Order article via Infotrieve]
29. Hastings H, Evans S, Quan W, et al. Nonlinear dynamics in ventricular fibrillation. Proc Natl Acad Sci U S A. 1996;93:10496–10499.
30.
Kaplan D, Cohen
R. Is fibrillation chaos? Circ
Res. 1990;67:886–892.
31.
Avnir D, Biham O,
Lidar D, et al. Is the geometry of nature fractal?
Science. 1998;279:39–40.
32. Hsia P-W, Fendelander L, Harrington G, et al. Defibrillation success is associated with myocardial organization: spatial coherence as a new method of quantifying the electrical organization of the heart. J Electrocardiol. 1996;29:189–197. [Medline] [Order article via Infotrieve]
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