Scaling Exponent Predicts Defibrillation Success for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest

Abstract

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 (Pϭ0.004), for cases with return of pulses (Pϭ0.049), and for cases surviving to hospital discharge (PϽ0.001). Receiver operator curves revealed the utility of the scaling exponent for predicting the probability of restoring an organized rhythm (area under the curveϭ0.70) and of survival (area under the curveϭ0.84). 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.

Influence of the skeletal muscle activity on time and frequency domain properties of the body surface ECG during evolving ventricular fibrillation in the pig

“…This measure shows how the signal under consideration differs from the random one, which is characterized by ScE≈2 [6]. We estimated ScE as described previously [1,3]. Briefly, we calculated function L (k) for lag k = 1−1024.…”

“…It should be noted that the value of ScE may depend on fitting interval of k . This interval varied in previous studies [1,3,17]. We used two different fitting intervals (1 to 8 and 5 to 20 points) for calculation of ScE and designated them as ScE 1−8 and ScE 5−20 , respectively.…”

“…ScE was recently introduced as a measure of ECG organization during VF [1][2][3][4][5][6][7][8][9]17]. It was postulated that ScE distinguishes "coarse" VF (low value of ScE close to 1) from "fine" VF (high value of ScE close to 2) and can provide a means to estimate VF duration [2] and to predict the outcome of rescue shocks [1].…”

“…Many attempts have been made to use the measures based on the ECG waveform in frequency and time domain as a means of assessing VF duration for optimization of the resuscitation strategies [1][2][3][4][5][6][7][8][9][10][11]. Accumulated clinical and laboratory data [1][2][3][4][5][6][7][8][9][10][11] have shown that these ECG waveform-based measures, or combinations of these measures, can help to predict the likelihood of the successful countershock therapy in cardiac arrest patients. However, analysis of ECG during VF can be confounded by extra-cardiac phenomena.…”

The effect of ischemia on ventricular fibrillation as measured by fractal dimension and frequency measures

“…[7,9] The three frequency based measures have been studied extensively and include median frequency, angular velocity, and frequency ratio [4][5][6][7][8]12,16]. Measures of the fractal dimension have also been developed to analyze VF and include the scaling exponent (ScE) [9,[13][14][15] and the logarithm of the absolute correlations (LAC) [17]. Another measure of fractal dimension is the Hurst exponent [7,[18][19][20].…”