Electrocardiogram characteristics prior to in-hospital cardiac arrest.

Survival after in-hospital cardiac arrest (I-HCA) remains < 30 %. There is very limited literature exploring the electrocardiogram changes prior to I-HCA. The purpose of the study was to determine demographics and electrocardiographic predictors prior to I-HCA. A retrospective study was conducted among 39 cardiovascular subjects who had cardiopulmonary resuscitation from I-HCA with initial rhythms of pulseless electrical activity (PEA) and asystole. Demographics including medical history, ejection fraction, laboratory values, and medications were examined. Electrocardiogram (ECG) parameters from telemetry were studied to identify changes in heart rate, QRS duration and morphology, and time of occurrence and location of ST segment changes prior to I-HCA. Increased age was significantly associated with failure to survive to discharge (p < 0.05). Significant change was observed in heart rate including a downtrend of heart rate within 15 min prior to I-HCA (p < 0.05). There was a significant difference in heart rate and QRS duration during the last hour prior to I-HCA compared to the previous hours (p < 0.05). Inferior ECG leads showed the most significant changes in QRS morphology and ST segments prior to I-HCA (p < 0.05). Subjects with an initial rhythm of asystole demonstrated significantly greater ECG changes including QRS morphology and ST segment changes compared to the subjects with initial rhythms of PEA (p < 0.05). Diagnostic ECG trends can be identified prior to I-HCA due to PEA and asystole and can be further utilized for training a predictive machine learning model for I-HCA.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning.

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.

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.

OBJECTIVE: The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. MATERIALS AND METHODS: Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. RESULTS: 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent > 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. CONCLUSION: At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model.