Delirium is an acute disturbance of cognition and attention which fluctuates during the day. The incidence of delirium is high, especially in critically ill and elderly patients. However, the pathophysiology is not fully understood and the diagnosis can be challenging. In clinical practice it can be difficult to distinguish hypoactive delirium (i.e., being drowsy, inactive, withdrawn) from the effects of sedation. The aim of this thesis was to improve our knowledge of reduced levels of consciousness by studying the brain activity and to improve delirium detection in clinical practice. In part I of this thesis, we used 21-channel electroencephalography (EEG) functional connectivity and network analysis to compare patients with hypoactive delirium and patients recovering from anaesthesia. Reduced functional connectivity and disturbed directed connectivity were found to be associated with the reduced levels of consciousness in both hypoactive delirium and recovery from anaesthesia. However, the topology was different. Hypoactive delirium showed a less intergraded, and therefore efficient network, whereas the recovery from anaesthesia showed a more integrated network. Furthermore, the effects of two different sedatives (midazolam and propofol) were studied. Both showed reduced slowing of the EEG, reduced functional connectivity and loss of the directed connectivity compared to the awake state. No differences were found in the topology. Propofol had a significantly greater reduction of the alpha frequency band functional connectivity and delta band directed connectivity compared to midazolam. Part II describes of the validation of a single channel EEG-based delirium monitor. First, the current clinical practise regarding delirium detection was evaluated. Delirium experts evaluated video-recorded cognitive assessments of postsurgical elderly patients. There was a considerable amount of disagreement (21%) between the experts, which indicate the difficulty of delirium detection. Secondly, the sensitivity of the delirium screening tools for routine daily practice by nurses was assessed. In only 50% of the patients the result of a screening tool was documented, and within these cases, the sensitivity was 43%. This indicates the need for an objective tool to detect delirium. Therefore, an EEG-based delirium monitor was developed and validated in postoperative patients. In our validation study we showed that it is possible to distinguish delirious from non-delirious patients using the relative delta power (1-4 Hz). Finally, we showed that it is possible to predict delirium the next day using this simple single-channel EEG recording. However, this final result should be confirmed in a larger study population. We showed that it is possible to detect delirium based on a simple quantitative EEG recording, and early prediction of delirium seems feasible. Since delirium is always a consequence of a physical cause, this EEG-based monitor may allow early detection of this underlying cause, which should be treated. Improved algorithms should be developed to detect delirium distinct from conditions influencing the EEG, such as the effects of sedatives.
|Qualification||Doctor of Philosophy|
|Award date||21 Sep 2017|
|Publication status||Published - 2017|