Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study

Hunter, Andrew; Crouch, Barry; Webster, Nigel R.; Platt, Bettina

doi:10.3934/neuroscience.2020001

Cited by 20 publications

(38 citation statements)

References 33 publications

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“…Median EEG duration was 0.5 hours (0.3-30). The majority of patients (64%) were under continuous sedation during EEG, with propofol and/or midazolam; their median duration of mechanical ventilation was 8 days (4-14) and the median ICU stay was 11 days (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Resultsmentioning

confidence: 99%

“…EEG is part of standard diagnostic procedures of acute alteration of consciousness in the ICU, and may therefore prove useful to contribute to delirium diagnosis (7)(8)(9). Yet, clinical data on EEG in ICU delirium is limited (10,11), and to our knowledge, a comprehensive analysis of EEG characteristics in mechanically ventilated critically ill patients at high delirium risk has not been explored so far, especially in adults without known acute brain injury.…”

Section: Introductionmentioning

confidence: 99%

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EEG characteristics of mechanically ventilated critically ill patients at high risk of delirium

Eskioglou

Iaquaniello

Alvarez

et al. 2020

Preprint

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Background Neurophysiological exploration of ICU delirium is limited. Here, we examined EEG characteristics of medical-surgical critically ill patients with new onset altered consciousness state at high risk for ICU delirium. Methods Pre-planned analysis of non-neurological mechanically ventilated medical-surgical ICU subjects, who underwent a prospective multicenter randomized, controlled EEG study (NCT03129438, April 2017-November 2018). EEG characteristics, according to the 2012 ACNS nomenclature, included background activity, rhythmic periodic patterns/epileptic activity, amplitude, frequency, stimulus-induced discharges, triphasic waves, reactivity and NREM sleep. We explored EEG findings in delirious vs. non-delirious patients, specifically focusing on presence of burst-suppression and rhythmic periodic patterns (ictal-interictal continuum), and epileptiform activity (ictal EEG). Results We analyzed 91 patients (median age, 66 years) who underwent EEG because of new onset altered consciousness state at a median 5 days from admission; 42 patients developed delirium (46%). Burst-suppression (10 vs. 0%, p = 0.02), rhythmic/periodic patterns (43% vs. 22%, p = 0.03) and epileptiform activity (7 vs. 0%, p = 0.05) were more frequent in delirious vs. non-delirious patients. The presence of at least one of these abnormal EEG findings (32/91 patients; 35%) was associated with a significant increase in the likelihood of delirium (42 vs. 15%, p = 0.006). Cumulative dose of sedatives and analgesics, as well as all other EEG characteristics, did not differ significantly between the two groups. Conclusion In mechanically ventilated non-neurological critically ill patients with new onset alteration of consciousness, EEG showing burst-suppression and/or ictal-interictal continuum findings indicates a higher risk of ICU delirium, independently of sedation and analgesia.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG characteristics of mechanically ventilated critically ill patients at high risk of delirium

Eskioglou

Iaquaniello

Alvarez

et al. 2020

Preprint

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“…With regard to confounding factors, eight of 33 studies (24.2%) excluded patients with dementia [35,43,49,52,59,64,65]; six (18.2%) included subjects with dementia and studied differences in EEG between patients with delirium, with dementia, and with both conditions [41, 60-62, 66, 71]; four (12.1%) included subjects with dementia but did not take this into account in their analyses [42,50,56,57]; 15 (45.5%) did not mention whether subjects with dementia were included [44, 46-48, 51, 53-55, 58, 63, 67-70, 72]. Furthermore, six studies (18.2%) did not include patients with alcohol/substance abuse [41,49,59,62,67,71]; three (9.1%) included patients with alcohol/substance abuse and took this into account in their analysis [46,58,66]; six (18.2%) included patients with alcohol/substance abuse but did not consider this in the [50,[54][55][56][57]69]; 18 (54.5%) did not mention whether patients with alcohol/substance abuse were included [35, 42-45, 47, 48, 51-53, 60, 61, 63-65, 68, 70, 72].…”

Section: Study and Patient Characteristicsmentioning

confidence: 99%

“…Others averaged electrodes of anatomical regions to search for differences. Six studies [35,42,50,62,65,67] used two-or three-electrode derivations. More speci cally, Hunter et al [65], van der Kooi et al [35], and Fleischmann et al [42] examined different derivations among all included electrodes, while Koponen et al [62], Matsushima et al [67], and Numan et al [50] just analyzed the T6-O2 or T5-O1 derivation, the C3-O1 derivation, or Fp2-Pz and T8-Pz derivations, respectively.…”

Section: Quantitativementioning

confidence: 99%

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Electroencephalography in Delirium Assessment. A Scoping Review

Wiegand

Rémi²,

Dimitriadis³

2021

Preprint

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BackgroundDelirium is a common disorder affecting up to 82% of patients in the intensive care unit (ICU). Delirium assessment scores such as the Confusion Assessment Method (CAM) are time-consuming, they cannot differentiate between different types of delirium and their etiologies, and they may have low sensitivities in the clinical setting. While today, electroencephalography (EEG) is increasingly being applied to delirious patients in the ICU, a lack of clear cut EEG signs, leads to inconsistent assessments. MethodsWe therefore conducted a scoping review on EEG findings in delirium. 1236 articles identified through database search on PubMed and Embase were reviewed. Finally, 33 original articles were included in the synthesis. ResultsEEG seems to offer manifold possibilities in diagnosing delirium. All 33 studies showed a certain degree of qualitative or quantitative EEG alterations in delirium. Thus, normal routine (rEEG) and continuous EEG (cEEG) make presence of delirium very unlikely. All 33 studies used different research protocols to at least some extent. These include differences in time points, duration, conditions, and recording methods of EEG, as well as different patient populations, and diagnostic methods for delirium. Thus, a quantitative synthesis and common recommendations are so far elusive. ConclusionFuture studies should compare the different methods of EEG recording and evaluation to identify robust parameters for everyday use. Evidence for quantitative bi-electrode delirium detection based on increased relative delta power and decreased beta power is growing and should be further pursued. Additionally, EEG studies on the evolution of a delirium including patient outcomes are needed.

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Delirium detection using GAMMA wave and machine learning: A pilot study

Mulkey

Albanese

Kim

et al. 2022

Research in Nursing & Health

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Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long‐term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof‐of‐concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.

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Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study

Cited by 20 publications

References 33 publications

EEG characteristics of mechanically ventilated critically ill patients at high risk of delirium

EEG characteristics of mechanically ventilated critically ill patients at high risk of delirium

Electroencephalography in Delirium Assessment. A Scoping Review

Delirium detection using GAMMA wave and machine learning: A pilot study

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