Electroencephalography Monitoring in the Critically Ill

Abstract

Critically ill patients in the Intensive Care Unit (ICU) are often comatose and thoroughly monitored. Neurological complications occur in up to 20% of these patients. Therefore, monitoring of the brain, which can be performed using electroencephalography (EEG), has the potential to significantly impact the outcomes of patients at the ICU. Despite the potential of EEG as a noninvasive method for monitoring the neurological status of critically ill patients, the labor-intensive and complex nature of its application and assessment has hindered widespread implementation. Therefore, the aim of this thesis was to identify the logistical and technical prerequisites for efficient and effective neuromonitoring using EEG in the ICU.

Chapter 1 provides an overview of neuromonitoring techniques in the critically ill patient. It delves into the neurophysiological background of the EEG, the techniques used for applying the EEG electrodes, and the EEG assessment.

In Chapter 2 we present a qualitative study on the optimal conditions for EEG monitoring in the ICU. Through 12 individual and 2 focus group interviews with employees from different departments within and outside of the hospital, the current workflow regarding neuromonitoring in the ICU is identified. Additionally, we evaluate the barriers and facilitators for change in this monitoring process through the Consolidated Framework for Implementation Research (CFIR). Factors such as motivation and willingness to change serve as facilitators, while a lack of interdepartmental communication and the high workload for various healthcare professionals involved can be significant barriers.

The qualitative research reveals that the largest group monitored using EEG in the ICU consists of patients suffering from postanoxic encephalopathy, which can be a complication of a cardiac arrest. Therefore, in Chapter 3, we examine the technical requirements for optimal EEG monitoring. Specifically, we focus on the necessary number of EEG electrodes for reliable automatic classification of the EEG background pattern in postanoxic encephalopathy. By training an Random Forest (RF) classifier with input from 12, 10, 8, 6, and 4 EEG electrodes, we develop a model with a micro-averaged One-vs-Rest (OvR) Area Under the Curve - Receiver Operating Characteristics (AUC-ROC) value of 0.923, 0.924, 0.924, 0.925, and 0.923 (p-value: 0.279) for the different numbers of electrodes respectively. The constant performance of the model suggests that a reduced number of electrodes may be sufficient for monitoring this patient group, potentially reducing the workload for EEG technicians. Automatic assessment of the EEG can also contribute to a decreased workload for clinical neurophysiologists.

In Chapter 4 we provide the conclusions and future perspectives of this thesis. We have demonstrated the potential for change in the EEG monitoring workflow at the ICU of the Erasmus MC, indicating that there is an opportunity to work towards more effective and efficient neuromonitoring. Future research should focus on a broader range of logistical and technical prerequisites - including effective interdepartmental collaboration and which EEG equipment to use - thereby creating opportunities to improve treatment and outcomes of critically ill patients.