Deep Learning and Electrical Impedance Tomography in the Prediction of Interventions Needed to Resuscitate From Hypoxic Pseudo-Pulseless Electrical Activity

Title:

Deep Learning and Electrical Impedance Tomography in the Prediction of Interventions Needed to Resuscitate From Hypoxic Pseudo-Pulseless Electrical Activity

Link:

https://www.ahajournals.org/doi/abs/10.1161/circ.142.suppl_4.295

Abstract:

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% O2, 100% O2 + CPR, 100% O2 + 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.

Citation:

Alexander L. Lindqwister, Weiyi Wu, Samuel Klein, Ethan K. Murphy, Karen L. Moodie, Saeed Hassanpour, Alexandra P. Hamlin, Joseph M. Minichiello, Justin E. Anderson, Norman A. Paradis, “Deep Learning and Electrical Impedance Tomography in the Prediction of Interventions Needed to Resuscitate From Hypoxic Pseudo-Pulseless Electrical Activity”, American Heart Association Scientific Sessions (AHA), Circulation, 142:A295, 2020.