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

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

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