Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model

Electroencephalographic monitoring of brain activity during cardiac arrest: a narrative review

BACKGROUND

To date cardiac arrest (CA) remains a frequent cause of morbidity and mortality: despite advances in cardiopulmonary resuscitation (CPR), survival is still burdened by hypoxic-ischemic brain injury (HIBI), and poor neurological outcome, eventually leading to withdrawal of life sustaining treatment (WLST). The aim of CPR is cardiac pump support to preserve organ perfusion, until normal cardiac function is restored. However, clinical parameters of target organ end-perfusion during CPR, particularly brain perfusion, are still to be identified. In this context, electroencephalography (EEG) and its derivatives, such as processed EEG, could be used to assess brain function during CA.

OBJECTIVES

We aimed to review literature regarding the feasibility of EEG and processed or raw EEG monitoring during CPR.

METHODS

A review of the available literature was performed and consisted of mostly case reports and observational studies in both humans and animals, for a total number of 22 relevant studies.

RESULTS

The research strategy identified 22 unique articles. 4 observational studies were included and 6 animal testing studies in swine models. The remaining studies were case reports. Literature regarding this topic consists of conflicting results, containing studies where the feasibility of EEG during CPR was positive, and others where the authors reached opposite conclusions. Furthermore, the level of evidence, in general, remains low.

DISCUSSION

EEG may represent a useful tool to assess CPR effectiveness. A multimodal approach including other non-invasive tools such as, quantitative infrared pupillometry and transcranial Doppler, could help to optimize the quality of resuscitation maneuvers.

Enhancing cardiac arrest survival with extracorporeal cardiopulmonary resuscitation: insights into the process of death

Extracorporeal cardiopulmonary resuscitation (ECPR) is an emerging method of cardiopulmonary resuscitation to improve outcomes from cardiac arrest. This approach targets patients with out-of-hospital cardiac arrest previously unresponsive and refractory to standard treatment, combining approximately 1 h of standard CPR followed by venoarterial extracorporeal membrane oxygenation (VA-ECMO) and coronary artery revascularization. Despite its relatively new emergence for the treatment of cardiac arrest, the approach is grounded in a vast body of preclinical and clinical data that demonstrate significantly improved survival and neurological outcomes despite unprecedented, prolonged periods of CPR. In this review, we detail the principles behind VA-ECMO-facilitated resuscitation, contemporary clinical approaches with outcomes, and address the emerging new understanding of the process of death and capability for neurological recovery.

EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student’s t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.