Search

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a form of profound cardiac shock defined as measurable cardiac activity without clinically detectable pulses. p-PEA has a distinct physiology and etiology from VF and true-PEA, and may constitute up to 40% of reported cases of cardiac arrest. Electrical impedance tomography (EIT) uses cutaneous electrodes to generate images based on cross sectional resistance. We utilized EIT to predict the number of interventions required to achieve ROSC from p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential cutaneous thoracic and abdominal electrode arrays. A deep learning model was utilized to detect features within the EIT video clips of the p-PEA disease state to predict the number of treatments required to achieve ROSC. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 1630 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Data were labeled based on the number of interventions required to achieve ROSC (100% O 2 , 100% O 2 + CPR, 100% O 2 + CPR + Epi, ROSC not achieved). Results: This approach yielded receiver operator characteristic curves - area under the curve (ROC-AUC, Figure 1) values of 0.75 for micro (weighted) AUC and 0.78 for macro (unweighted) AUC on a 4 class prediction model. Conclusion: EIT combined with machine learning may differentiate the required treatments needed to achieve ROSC in p-PEA.

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. Patients in pseudo-PEA carry different prognoses than those in true PEA and may require different therapies. Noninvasive technologies capable of detecting return of spontaneous circulation (ROSC) are needed to guide transition therapy from ACLS to critical care. Electrical impedance (EI) measurements across the thorax and abdomen may reflect the state of the cardiovascular system and change with ROSC. Hypothesis: EI measured across the chest wall or abdomen can detect ROSC in p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. EI was measured with cutaneous belts around the chest and abdomen (Swisstom). p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic pressure < 40 mmHg. Experiments in which ROSC occurred within 20 seconds of starting 100% FiO 2 were studied. EI waveforms were analyzed with fast fourier transforms, and the dominant frequencies of p-PEA and ROSC signals were subtracted to create a difference distribution. Results: With onset of ROSC, there was a characteristic frequency increase in EI measurable across both chest and abdominal bands. Fourier transform analysis demonstrated a significant difference in dominant frequencies between resuscitation and p-PEA. The median difference between resuscitation and p-PEA dominant frequencies were 5.49 in the thorax [95% CI 3.48 - 7.50] and 6.65 in the abdomen [95% CI 3.63 - 9.67]. Conclusions: There appear to be characteristic EI frequency patterns and changes in p-PEA and ROSC. If confirmed clinically, this indicates that EI may be a promising technology for non-invasive monitoring in cardiac arrest.