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PsychopharmacologyFull Access

Point-of-Care EEG Device Could Ease Efforts to Detect Delirium

Published Online:https://doi.org/10.1176/appi.pn.2017.pp4b3

Abstract

A psychiatric face-to-face interview including the use of an instrument such as the Confusion Assessment Method is the current standard for diagnosing and screening delirium, but this type of screening can prove challenging due to the sheer volume of elderly patients admitted to hospitals daily.

Photo: Gen Shinozaki, M.D.
Gen Shinozaki, M.D., M.S.

Delirium is a dangerous state of confusion that affects millions of elderly people admitted to hospitals each year, increasing the risk of health complications and death (1-3).

Studies show that these patients are more likely to fall, aspirate, and hurt themselves and health care providers than others of the same age. Additionally, average hospital stays for delirious patients are 7.8 days longer than those who do not experience delirium (4). By some estimates, the extra days spent in the hospital can add up to more than $15,000, creating a significant burden for patients and hospitals (5).

Yet, delirium remains underdiagnosed, and thus undertreated.

Questionnaire-style instruments to assess delirium—including the Confusion Assessment Method (CAM), CAM-ICU, and Delirium Rating Scale (DRS)—have been shown to be sensitive at determining patients with delirium in research settings. However, the frequency with which these tools must be administered and their subjective nature makes them impractical for use in real-world hospital workflows. In fact, in busy hospital workflows such as those in an ICU, these tools have been shown to be ineffective with poor sensitivity (38 percent to 47 percent) (6, 7).

A psychiatric face-to-face interview including the use of an instrument such as CAM is the current standard for diagnosing and screening delirium, but this type of screening is also unrealistic for large volumes of patients admitted to hospitals.

Electroencephalography (EEG) can detect what is known as “diffuse slowing” brain wave signals that are characteristic of delirium (8, 9). But, this too, may be impractical for screening the high volume of elderly patients admitted into hospitals; EEG commonly requires an experienced technician to correctly place 20 electrodes upon the head of the patient, as well a neurologist to interpret the data.

What if there was a way to simplify an EEG screen for delirium?

Patients with delirium commonly display low frequency brain waves across multiple channels. If all channels are showing the same signals, is it possible measuring the output of a limited number of channels could prove equally effective at screening for delirium? Recent studies suggest the answer may be yes.

One study confirmed the excellent sensitivity and specificity of a limited number of EEG leads rather than the traditional 20 leads for confirming delirium (10). Our team recently showed that a two-channel portable bedside EEG device can differentiate between a delirious patient and normal patient, as well as delirious state versus recovered state among same patient.

We conducted a pilot study recruiting 142 subjects admitted to a hospital and measured brainwave signals using a simplified few lead EEG. The performance metrics were as follows: accuracy=87.5 percent, sensitivity=80.0 percent, specificity=87.7 percent. We will present more on these and other findings at the APA Annual Meeting in San Diego.

Reducing the number of lead placements on a patient’s head from 20 to two—for bipspectral EEG—would likely reduce the need for specialized neurologists and technicians to apply the device, and lead to more rapid screenings.

Bispectral brain wave monitoring is not new. For the last two decades, EEG signals obtained from a few leads attached to the foreheads of patients under anesthesia have been gaining popularity. Bispectral brain wave monitoring is almost standard care for anesthetized surgical patients (11-14), and electroconvulsive therapy (ECT) machines use two EEG leads to monitor seizure activity.

It is expected that EEG signals from a simplified handheld device can rapidly and accurately detect changes of EEG signals from normal to delirious conditions.

Our research team is hoping to begin using this approach to improve delirium care with mass screening and regular monitoring for elderly patients admitted to a hospital soon. ■

References

1. Fong TG, Tulebaev SR, Inouye SK. Delirium in Elderly Adults: Diagnosis, Prevention and Treatment. Nature Reviews Neurology. 2009;5(4):210-20.

2. Inouye SK, Westendorp RG, Saczynski JS. Delirium in Elderly People. Lancet. 2014;383(9920):911-22.

3. Inouye SK. Delirium in Older Persons. New England Journal of Medicine. 2006;354(11):1157-65.

4. McCusker J, Cole MG, Dendukuri N, Belzile E. Does Delirium Increase Hospital Stay? Journal of the American Geriatrics Society. 2003;51(11):1539-46.

5. Boustani M, Baker MS, Campbell N, Munger S, Hui SL, Castelluccio P, et al. Impact and Recognition of Cognitive Impairment Among Hospitalized Elders. Journal of Hospital Medicine. 2010;5(2):69-75.

6. van Eijk MM, van den Boogaard M, van Marum RJ, Benner P, Eikelenboom P, Honing ML, et al. Routine Use of the Confusion Assessment Method for the Intensive Care Unit: A Multicenter Study. American Journal of Respiratory and Critical Care Medicine. 2011;184(3):340-4.

7. Nishimura K, Yokoyama K, Yamauchi N, Koizumi M, Harasawa N, Yasuda T, et al. Sensitivity and Specificity of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) for Detecting Post-Cardiac Surgery Delirium: A Single-Center Study in Japan. Heart & Lung: The Journal of Critical Care. 2016;45(1):15-20.

8. Jacobson S, Jerrier H. EEG in Delirium. Seminars in Clinical Neuropsychiatry. 2000;5(2):86-92.

9. Jacobson SA, Leuchter AF, Walter DO. Conventional and Quantitative EEG in the Diagnosis of Delirium Among the Elderly. Journal of Neurology, Neurosurgery, and Psychiatry. 1993;56(2):153-8.

10. van der Kooi AW, Zaal IJ, Klijn FA, Koek HL, Meijer RC, Leijten FS, et al. Delirium Detection Using EEG: What and How to Measure. Chest. 2015;147(1):94-101.

11. Liu J, Singh H, White PF. Electroencephalographic Bispectral Index Correlates With Intraoperative Recall and Depth of Propofol-Induced Sedation. Anesthesia and Analgesia. 1997;84(1):185-9.

12. Doi M, Gajraj RJ, Mantzaridis H, Kenny GN. Effects of Cardiopulmonary Bypass and Hypothermia on Electroencephalographic Variables. Anaesthesia. 1997;52(11):1048-55.

13. Schmidlin D, Hager P, Schmid ER. Monitoring Level of Sedation With Bispectral EEG Analysis: Comparison Between Hypothermic and Normothermic Cardiopulmonary Bypass. British Journal of Anaesthesia. 2001;86(6):769-76.

14. Powers KS, Nazarian EB, Tapyrik SA, Kohli SM, Yin H, van der Jagt EW, et al. Bispectral Index as a Guide for Titration of Propofol During Procedural Sedation Among Children. Pediatrics. 2005;115(6):1666-74.

Gen Shinozaki, M.D., M.S., is an associate professor of psychiatry at the University of Iowa Carver College of Medicine. He is interested in developing a point-of-care EEG device suitable for mass screening of encephalopathy/delirium. Shinozaki has received research support from the National Institutes of Health, the National Science Foundation, and the University of Iowa Research Foundation. Shinozaki is president and co-founder of Predelix Medical, LLC.