Predicting defibrillation success in sudden cardiac arrest patients

Firoozabadi, Reza; Nakagawa, M.; Helfenbein, Eric; Babaeizadeh, Saeed

doi:10.1016/j.jelectrocard.2013.06.007

Cited by 35 publications

(32 citation statements)

References 19 publications

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“…All classical predictors showed significantly different statistical distributions (p < 10 −10 ), and the boxplots visually show little overlap between the classes. Moreover, the values obtained for the features in both classes are in line with the values reported on other studies on shock outcome prediction [12,20,21,25,30]. The entropy measures of regularity also showed good class separation and significantly different distributions (p < 10 −10 ).…”

Section: Classical Versus Entropy-based Predictorssupporting

confidence: 89%

“…Special focus has been put on the thorough characterization and parametrization of the entropy measures. Four of the six analyzed features were conveniently adapted to encompass VF amplitude information and were shown to be at least as effective in shock outcome prediction as the most elaborate predictors described to date [23,25,26]. This indicates that quantitative entropy measures may be an adequate tool to characterize the non-linear dynamics of the VF waveform.…”

Section: Discussionmentioning

confidence: 92%

“…All patients were assigned an equal weight to compute ROC curves (repeated shocks on the same patient). Three measures were taken on the ROC curve including the area under the curve (AUC), maximum SP for SE > 90% and maximum SE for SP > 90% [25].…”

Section: Discussionmentioning

confidence: 99%

“…PPA measures VF coarseness by assessing its amplitude in short time intervals [25]. First, x(n) is subdivided into L non-overlapping sub-segments {x w (n)} w=1,...,L , and the peak-to-peak amplitude of each subsegment is computed.…”

Section: Classical Shock Outcome Predictorsmentioning

confidence: 99%

“…For instance, as time from collapse increases, VF amplitude decreases [19], and its dominant frequency and waveform roughness (the term roughness in VF waveform analysis was coined by Callaway, Sherman and colleagues in their contributions to the application of indices based on non-linear dynamics to predict defibrillation success [20,21]) reflect the different phases of VF [21,22]. Consequently, a plethora of VF-waveform features have been proposed to predict defibrillation success and to optimize treatment decisions [23][24][25][26]. These quantitative measures include the classical amplitude, slope or spectral analyses of VF [23,27], but also features derived from non-linear dynamics, such as: fractal dimension [28], Hurst exponents [28], scaling exponents [20], detrended fluctuation analysis [29] or Poincare-plot analysis [30].…”

Section: Introductionmentioning

confidence: 99%

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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 µV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.

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Section: Classical Versus Entropy-based Predictorssupporting

confidence: 89%

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Classical Shock Outcome Predictorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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Erweiterte Reanimationsmaßnahmen für Erwachsene („adult advanced life support“)

Soar

Nolan

Böttiger

et al. 2015

Notfall Rettungsmed

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Zuerst kardiopulmonale Reanimation oder zuerst Defibrillation beim außerklinischen Kreislaufstillstand? Es gibt Belege dafür, dass die Durchführung von Thoraxkompressionen während des Holens und Ladens des Defibrillators Kollabierter/kranker Patient Rufen Sie um Hilfe und berurteilen Sie den Patienten Lebenszeichen? NEIN JA Rufen Sie das Reanimationsteam CPR 30:2 Mit Sauersto und Atemwegshilfsmitteln Legen Sie die De pads an schließen sie den Monitor an De brillieren Sie, wenn indiziert Erweiterte lebenrettende Maßnahmen nach Eintre en des Reanimationsteams Beurteilen Sie nach ABCDE Erkennen und behandeln Sie: Sauersto , Monitoring, i.v.-Zugang Rufen Sie das Reanimationsteam, wenn indiziert Übergeben Sie an das Reanimationsteam

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