Delirium detection by a novel bispectral electroencephalography device in general hospital

Gen Shinozaki; Aubrey C Chan; Nicholas A Sparr; Kasra Zarei; Lindsey N Gaul; Jonathan T Heinzman; Julian Robles; Kumi Yuki; Theodosis J Chronis; Timothy Ando; Terrence Wong; Sayeh Sabbagh; Michelle T Weckmann; Sangil Lee; Thoru Yamada; Matthew D Karam; Nicolas O Noiseux; Eri Shinozaki; John W Cromwell

doi:10.1111/pcn.12783

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Delirium detection by a novel bispectral electroencephalography device in general hospital

Journal article

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Delirium detection by a novel bispectral electroencephalography device in general hospital

Gen Shinozaki, Aubrey C Chan, Nicholas A Sparr, Kasra Zarei, Lindsey N Gaul, Jonathan T Heinzman, Julian Robles, Kumi Yuki, Theodosis J Chronis, Timothy Ando, …

Psychiatry and clinical neurosciences, Vol.72(12), pp.856-863

12/2018

DOI: 10.1111/pcn.12783

PMCID: PMC6317342

PMID: 30246448

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Abstract

Delirium is common and dangerous among elderly inpatients; yet, it is underdiagnosed and thus undertreated. This study aimed to test the diagnostic characteristics of a noninvasive point-of-care device with two-channel (bispectral) electroencephalography (EEG) for the screening of delirium in the hospital. Patients admitted to the University of Iowa Hospitals and Clinics were assessed for the presence of delirium with a clinical assessment, the Confusion Assessment Method for Intensive Care Unit and Delirium Rating Scale. Subsequently, we obtained a 10-min bispectral EEG (BSEEG) recording from a hand-held electroencephalogram device during hospitalization. We performed power spectral density analysis to differentiate between those patients with and without delirium. Initially 45 subjects were used as a test dataset to establish a cut-off. The BSEEG index was determined to be a significant indicator of delirium, with sensitivity 80% and specificity 87.7%. An additional independent validation dataset with 24 patients confirmed the validity of the approach, with a sensitivity of 83.3% and specificity of 83.3%. In this pilot study, the BSEEG method was able to distinguish delirious patients from non-delirious patients. Our data showed the feasibility of this technology for mass screening of delirium in the hospital.

point of care

power spectral analysis

delirium

electroencephalogram

Details

Title: Subtitle: Delirium detection by a novel bispectral electroencephalography device in general hospital
Creators: Gen Shinozaki - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Aubrey C Chan - Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
Nicholas A Sparr - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Kasra Zarei - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Lindsey N Gaul - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Jonathan T Heinzman - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Julian Robles - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Kumi Yuki - Department of Family Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
Theodosis J Chronis - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Timothy Ando - Department of Psychiatry, Stanford University School of Medicine, Stanford, USA
Terrence Wong - Department of Internal Medicine, Highland Hospital, Alameda Health System, Oakland, USA
Sayeh Sabbagh - Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, USA
Michelle T Weckmann - Department of Family Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
Sangil Lee - Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
Thoru Yamada - Department of Neurology, University of Iowa Carver College of Medicine, Iowa City, USA
Matthew D Karam - Department of Orthopedics and Rehabilitation, University of Iowa Carver College of Medicine, Iowa City, USA
Nicolas O Noiseux - Department of Orthopedics and Rehabilitation, University of Iowa Carver College of Medicine, Iowa City, USA
Eri Shinozaki - Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
John W Cromwell - Department of Surgery, University of Iowa Carver College of Medicine, Iowa City, USA
Resource Type: Journal article
Publication Details: Psychiatry and clinical neurosciences, Vol.72(12), pp.856-863
DOI: 10.1111/pcn.12783
PMID: 30246448
PMCID: PMC6317342
NLM abbreviation: Psychiatry Clin Neurosci
ISSN: 1323-1316
eISSN: 1440-1819
Publisher: Australia
Grant note: MH107654 / National Institutes of Health 1664364 / National Science Foundation T35 HL007485 / NHLBI NIH HHS University of Iowa Research Foundation GAP
Language: English
Date published: 12/2018
Academic Unit: Neurology; Psychiatry; Emergency Medicine; Orthopedics and Rehabilitation; Surgery; Family and Community Medicine; Anesthesia; Injury Prevention Research Center; Neurosurgery; Internal Medicine
Record Identifier: 9984003488402771

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