Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

Background: Pulseless electrical activity (PEA) is the most common rhythm during in-hospital cardiac arrest (IHCA) with a prevalence around 50%. Knowing the prognosis of PEA evolution towards return of spontaneous circulation (ROSC) could help optimizing both resuscitation maneuvers and pharmacological therapy. The aim of this study was to develop an automatic method to predict the evolution of PEA during resuscitation based on the ECG-waveform. Materials and Methods: The dataset consists of 164 IHCA cases recorded by St. Olav University Hospital (Norway), Hospital of the University of Pennsylvania (USA) and Penn Presbyterian Medical Center (USA). ROSC was verified in 108 cases of the patients by physicians and bioengineers based on episode waveforms and clinical data. PEA segments of 5 sec were extracted from the last 10 min before ROSC or the end of resuscitation therapy. Three machine learning models were designed for segment binary classification based on an SVM (Gaussian) model using: 1) ECG-waveform features (9); 2) QRS-features (8); and 3) both ECG-waveform and QRS-features (17). Ten-fold cross validation was applied to train and test the models, and the performance was given in terms of area under the curve (AUC), sensitivity (Se) to correctly detect cases evolving to ROSC, specificity (Sp) and balanced accuracy (BAC). Results: A total of 780 segments were extracted (472 with ROSC). The median (IQR) for the models with the best feature combination are shown in the Table. The most important ECG-waveform features are associated to the ECG spectral distribution. QRS features that showed relevant information about the evolution of PEA were heart rate median and standard deviation, and QRS width, slope and amplitude. Conclusions: ROSC/no ROSC prediction of PEA segments is feasible using ECG signal information. The combination of ECG-waveform and QRS features enhances performance of predictive model.

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Background: Re-arrest occurs when a cardiac arrest patient being treated by the emergency medical services experiences another cardiac arrest after return of spontaneous circulation (ROSC).The incidence of re-arrest is high, close to 40% in out-of-hospital cardiac arrest (OHCA), and it is associated with lower survival. Prediction of re-arrest could improve prehospital care. The aim of this study was to develop a re-arrest prediction model based on heart rate variability (HRV) features. Materials and methods: OHCA cases treated by Dallas-FortWorth Center of Resuscitation Research were analyzed. Patients with at least two minutes of ROSC were included. Re-arrest was ascertained by the presence of life-threatening ECG and/or presence of chest compressions within 12 minutes after ROSC. Eighteen HRV characteristics for 1 min and 2 min intervals after ROSC were computed. Features were fed into a Random Forest (RF) classifier with 100 trees to predict re-arrest cases. Ten-fold cross-validation with 30 repetitions was applied to train the model and assess the performance in terms of area under the curve (AUC). Results: Inclusion criteria were met by 98 patients, 41 of which suffered re-arrest. The median time (interquartile range) to re-arrest from ROSC onset was 5 (3-7) min. The re-arrest prediction model showed a median AUC of 0.71 and 0.75 for 1 and 2 min post ROSC intervals, respectively. The most important HRV features in the RF predictor were the SD1/SD2 ratio (where SD1 and SD2 are the dispersions of points both perpendicular and parallel to the line-of-identity in the Poincaré plot), SD2, the interquartile range of the RR intervals, peak frequency in the high frequency band (0.15-0.4 Hz) and coefficient of variation of RR intervals (the ratio between the mean and standard deviation of RR intervals). Conclusions: HRV metrics predict re-arrest in OHCA. Further studies with larger datasets are needed to better understand re-arrest dynamics and confirm conclusions.