An integrative machine learning model using ECG and end-tidal carbon dioxide signals predicted defibrillation success with an ROC AUC of 93.8% and 83.3% accuracy, outperforming the standard AMSA technique.
Observational (n=57)
No
Does a machine learning model using non-linear dynamical signal characterization of VF waveforms improve the prediction of defibrillation success compared to the AMSA technique in out-of-hospital cardiac arrest patients?
A novel machine learning approach using non-linear dynamical analysis of VF waveforms, especially when combined with PetCO2 data, accurately predicts defibrillation success and outperforms traditional AMSA techniques.
BACKGROUND: Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique. METHODS: A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6-10 features for each test fold. RESULTS: The integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively. CONCLUSION: We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.
Shandilya et al. (Mon,) conducted a observational in Out-of-hospital cardiac arrest with ventricular fibrillation (n=57). Machine learning model using ECG and PetCO2 signals vs. Amplitude Spectral Area (AMSA) technique was evaluated on Prediction of defibrillation success (Accuracy and ROC AUC). An integrative machine learning model using ECG and end-tidal carbon dioxide signals predicted defibrillation success with an ROC AUC of 93.8% and 83.3% accuracy, outperforming the standard AMSA technique.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: