Predisposition for delirium and EEG characteristics

Montfort, Simone J.T. van; Dellen, Edwin van; Wattel, Luna L.; Kant, Ilse; Numan, Tianne; Stam, Cornelis J.; Slooter, Arjen J. C.

doi:10.1016/j.clinph.2020.01.023

Cited by 18 publications

(7 citation statements)

References 60 publications

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“…No baseline differences in functional brain networks were found between patients who developed delirium postoperatively and those who remained delirium-free, which is in line with previous studies showing that functional brain network characteristics that are altered during delirium do not necessarily reflect for the syndrome 14,15,83,84 . The onset of delirium seems therefore to reflect new functional network impairments.…”

Section: Discussionsupporting

confidence: 89%

Delirium as a disorder of brain network disintegration

Montfort¹,

Theodora²

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General introduction Chapter 1 Delirium Delirium is derived from the Latin word 'delirare', which literally means 'a disturbed state of mind' and has been known since ancient times. Hippocrates (460-370 BC) may have been the first to describe a syndrome characterized by confusion and restlessness, which fluctuated over time and which was associated with physical illness. He called this condition 'phrenitis' 1,2. In the following eras, many other names have been used to describe the syndrome, including brain failure, acute change of mental status, delirium, acute brain dysfunction, intensive care psychosis and encephalopathy. Of these, the term delirium became the official name. In the 90's, Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria were developed for the syndrome and delirium became an official diagnosis. Currently, delirium is defined as a neuropsychiatric syndrome, characterized by an acute change in attention and awareness. It is always a direct consequence of an underlying medical condition 3. It is a common syndrome, affecting 10-50% of the hospitalized elderly, and is a burden for patients and their relatives 4. Delirium has several clinical manifestations, which are commonly divided into hypoactive, hyperactive and mixed subtypes. Hypoactive delirium is characterized by lethargy and lack of psychomotor behavior and speech. In contrast, the hyperactive subtype is defined by restlessness, hyper vigilance and agitation 3. The mixed type manifest both hypoactive and hyperactive elements 5. All types are associated with negative outcomes, such as prolonged hospital stay, long-term cognitive impairment and dementia 4 .

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Section: Discussionsupporting

confidence: 89%

Delirium as a disorder of brain network disintegration

Montfort¹,

Theodora²

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“…While most prior research on EEG as a predictor of delirium has predominantly relied on analysis of EEG data collected either intra-operatively ( Kratzer et al, 2020 ; Sun et al, 2020 ) or pre-operatively ( van Montfort et al, 2020 ), here we compared the pre-operative and intra-operative EEG signals. We observed relative changes in EEG complexity from before to during anesthesia/surgery, which may reflect brain-health vulnerability.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction

Acker

Zhou

et al. 2021

Front. Syst. Neurosci.

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Physiologic signals such as the electroencephalogram (EEG) demonstrate irregular behaviors due to the interaction of multiple control processes operating over different time scales. The complexity of this behavior can be quantified using multi-scale entropy (MSE). High physiologic complexity denotes health, and a loss of complexity can predict adverse outcomes. Since postoperative delirium is particularly hard to predict, we investigated whether the complexity of preoperative and intraoperative frontal EEG signals could predict postoperative delirium and its endophenotype, inattention. To calculate MSE, the sample entropy of EEG recordings was computed at different time scales, then plotted against scale; complexity is the total area under the curve. MSE of frontal EEG recordings was computed in 50 patients ≥ age 60 before and during surgery. Average MSE was higher intra-operatively than pre-operatively (p = 0.0003). However, intraoperative EEG MSE was lower than preoperative MSE at smaller scales, but higher at larger scales (interaction p < 0.001), creating a crossover point where, by definition, preoperative, and intraoperative MSE curves met. Overall, EEG complexity was not associated with delirium or attention. In 42/50 patients with single crossover points, the scale at which the intraoperative and preoperative entropy curves crossed showed an inverse relationship with delirium-severity score change (Spearman ρ = −0.31, p = 0.054). Thus, average EEG complexity increases intra-operatively in older adults, but is scale dependent. The scale at which preoperative and intraoperative complexity is equal (i.e., the crossover point) may predict delirium. Future studies should assess whether the crossover point represents changes in neural control mechanisms that predispose patients to postoperative delirium.

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“… 83 With the exception of functional impairment, other risk factors for delirium including age, cognitive impairment, depression and alcohol misuse were not associated with delirium-related EEG characteristics in older outpatients awaiting elective surgery. 84 Therefore, EEG slowing (characterized by increases in delta and theta power and triphasic waves) and reduced functional connectivity can distinguish patients with and without delirium. 85 EEG also has diagnostic utility particularly to identify mental alterations due to non-convulsive status epilepticus which may present as delirium.…”

Section: Screening and Diagnosismentioning

confidence: 99%