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

Abstract

IntroductionQuantitative 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.MethodsA 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.ResultsAmong 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.ConclusionsIn 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.

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

“…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.…”

“…As shown in Table 3, the BER for the best entropy predictor (FuzzEn) was 2.4 points higher than that of the best classical predictor (MSI), which means a combined increase of around five points in SE and SP. There is currently contradictory evidence on the benefits of combining features for shock outcome prediction, with studies showing increases of up to six points in BER for combinations of 3-10 features [57,58] and studies showing no increase in accuracy [23,26]. In a recent study with a large cohort of 1617 patients and 3828 shocks, He et al [26] found no benefits in combining features and showed the strong correlation among features, such as MdS, AMSA, PPA or energy.…”

“…There is currently contradictory evidence on the benefits of combining features for shock outcome prediction, with studies showing increases of up to six points in BER for combinations of 3-10 features [57,58] and studies showing no increase in accuracy [23,26]. In a recent study with a large cohort of 1617 patients and 3828 shocks, He et al [26] found no benefits in combining features and showed the strong correlation among features, such as MdS, AMSA, PPA or energy. Those results confirmed the importance of VF amplitude in shock outcome prediction, since all of those features are proportional to VF amplitude or its square.…”

“…These thresholds are important should FuzzEn be used clinically in a prospective study, particularly to decide when to continue compressions instead of shocking VF, with the objective of avoiding futile shocks and longer pauses in chest compressions. Prospective studies in which shock outcome predictors, such as AMSA slope or the signal integral [12,60], are used to decide the most adequate therapy for VF are currently planned, thanks to the large body of evidence accumulated in retrospective studies of large patient cohorts [12,26]. Table 4.…”

“…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].…”