Abstract 370: Electrical Features Of Pulseless Electrical Activity Associated With Cardiac Arrest Outcomes
Introduction: The cardiac arrest rhythm of pulseless electrical activity (PEA) poses various diagnostic and therapeutic challenges. PEA may represent a spectrum of arrest conditions with variable responses to resuscitation care. Aim: We analyzed PEA rhythms to identify diagnostic patterns associated with survival in cardiac arrest. Methods: In this retrospective cohort study, we utilized the Portland Resuscitation Outcomes Consortium database of out-of-hospital cardiac arrests compiled by the Tualatin Valley Fire and Rescue from 2006-2016. Recordings from defibrillation pads included compression waveforms, electrocardiogram, and transthoracic impedance signals. For each patient, we analyzed the first two pauses in chest compressions, characterized by flat compression and impedance signals. Features extracted from raw ECG signals included contraction frequency and variability. Signal Fourier transformation and 0-100 Hz band pass filtering yielded signals’ distribution across a frequency spectrum from which signal power was extracted. Extraction of the three most prominent frequencies was performed from the Gaussian filtered frequency spectrum. Non-parametric tests (Mann-Whitney, Fisher) and logistic regression methods were used for analysis. Results: Fifty-nine ECG recordings were analyzed corresponding to 7 (11.9%) survivors and 52 (88.1%) non-survivors. Median age was 72 (IQR 20), and 28.8% (17/59) were female. No significant differences were noted in sex or median age between survivors and non-survivors. Analysis of the first ECG pause showed a higher first peak median frequency among survivors (2.15 vs 0.06 Hz, p=0.049). We did not find a significant association between the second peak median frequency of the first ECG segment (6.46 vs 1.49 Hz, p=0.882) or the signal power of the second ECG segment (108.04 vs 100.77 Hz, p=0.647) with survival. Regression analysis did not provide reliable outcome prediction models for survival in this preliminary cohort. Conclusion: Computerized analysis of PEA ECG waveforms offers alternate approaches to bedside signal interpretation that may correlate with survival. Our preliminary work offers a potential approach to PEA analysis that will require application to a larger PEA arrest cohort.