Delirium and the EEG Revisited: Blog Post by Medical Student Terrence Wong

Terrence Wong Med Student
Terrence Wong Med Student

I’ve got a sharp medical student, Terrence Wong, on my team who is participating in a new delirium detection study headed by one of my colleagues, Dr. Gen Shinozaki, MD working with surgeon Dr. John Cromwell, MD on using a simplified EEG procedure to detect delirium. It just might help move the clouds away from a path forward toward improving delirium prevention. Recall my post from over a couple of months ago in which I mentioned that I sent a message to the American Delirium Society blog page, asking for feedback about it.

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I have been expecting one or two medical students connected with the project to rotate through the general hospital psychiatry consultation service.  The leaders of the study want them to get an idea how psychiatric consultants usually assess delirium. The medical student has some background in marketing and has an interest in product development which would reduce waste and increase efficiency in health care.

He’s getting a look at the smokejumper’s view of the general hospital as we rush around putting out fires–getting singed in the process, I might add. I have some thoughts about delirium detection, which I posted back in January [1].

CAM Mini CogAnyway, I gave the medical student one of my cheat cards outlining the Confusion Assessment Method (CAM) and the Mini-Cog. So far there hasn’t been a good opportunity for me to demonstrate the usual way I detect delirium using those methods. It’s been pretty hectic and we’ve had to make educated guesses, get collateral history from family members about patients’ baseline behavior and cognition, dodge flailing arms and legs in the process…that kind of thing.

We’ve had an opportunity to actually recommend standard EEG to assess for delirium, which highlighted one of the challenges. How do you tell whether an abnormality would be a patient’s “normal” baseline if the EEG is abnormal? However, it also helped move the medical evaluation further along.

Anyway, here’s a blog post by Terrence on using EEG technology to improve health care for delirium. He’s right on target when he points out the limitations of antipsychotics for delirium [2].

Delirium silently creeps along during the hospitalization process, only presenting when the condition has erupted to clinical attention. Intervention at that moment is too late, yet currently, the best we can do. When you talk to healthcare providers at the front lines of delirium, you’ll hear the same response echoed over and over again, “Yes when they act crazy, just hit them with some Haldol!” But when you study under a seasoned psychiatrists such as Dr. James Amos, you’ll begin to understand why that perspective is detrimental to patient care. But it doesn’t have to be that way.

Efforts to detect delirium have relied upon two major methods, both which fall short of the practical needs of a modern hospital environment. Screening instruments, largely based upon chart review and patient interview, have been unsuccessful due to challenges implementing these into clinical workflows and in providing ongoing training for healthcare providers to use such instruments. In addition, they exhibit poor sensitivity in routine use.

We believe that there is an enormous opportunity to both improve the quality of care and decrease the cost of care through use of a noninvasive, point­-of­-care device that accurately predicts, screens, and monitors delirium, yet is simple enough to introduce into existing clinical workflows.

The specialized EEG will be an important and useful delirium screening tool. Our research currently suggests that this may also predict delirium prior to its clinical onset. The key concept here is early detection. The phrase, “an ounce of prevention is worth a pound of cure,” strongly resonates here. If you have ever seen a patient screaming, yelling, and buckling the bed despite restraints, you’ll vividly understand why it is important to prevent this condition.

Modern medicine demands the practice of cost-effective, high-quality healthcare. This clinical research data is currently being collected at University of Iowa Hospital & Clinics (UIHC). Our models predicts significant improvement in quality safety metrics and reduced financial expenditure. The world understands the importance of detecting hyperglycemia and its subsequent treatment. Our goal is to create the glucometer of delirium, establish the proof-of-concept at UIHC, and potentially implement it worldwide. 


1.van der Kooi, A. W., et al. (2015). “Delirium detection using eeg: What and how to measure.” Chest 147(1): 94-101.
BACKGROUND:  Despite its frequency and impact, delirium is poorly recognized in postoperative and critically ill patients. EEG is highly sensitive to delirium but, as currently used, it is not diagnostic. To develop an EEG-based tool for delirium detection with a limited number of electrodes, we determined the optimal electrode derivation and EEG characteristic to discriminate delirium from nondelirium.METHODS:  Standard EEGs were recorded in 28 patients with delirium and 28 age- and sex-matched patients who had undergone cardiothoracic surgery and were not delirious, as classified by experts using Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. The first minute of artifact-free EEG data with eyes closed as well as with eyes open was selected. For each derivation, six EEG parameters were evaluated. Using Mann-Whitney U tests, all combinations of derivations and parameters were compared between patients with delirium and those without. Corresponding P values, corrected for multiple testing, were ranked.RESULTS:  The largest difference between patients with and without delirium and highest area under the receiver operating curve (0.99; 95% CI, 0.97-1.00) was found during the eyes-closed periods of the EEG, using electrode derivation F8-Pz (frontal-parietal) and relative δ power (median [interquartile range (IQR)] for delirium, 0.59 [IQR, 0.47-0.71] and for nondelirium, 0.20 [IQR, 0.17-0.26]; P = .0000000000018). With a cutoff value of 0.37, it resulted in a sensitivity of 100% (95% CI, 100%-100%) and specificity of 96% (95% CI, 88%-100%).CONCLUSIONS:  In a homogenous population of nonsedated patients who had undergone cardiothoracic surgery, we observed that relative δ power from an eyes-closed EEG recording with only two electrodes in a frontal-parietal derivation can distinguish among patients who have delirium and those who do not.

2.Neufeld, K. J., et al. (2016). “Antipsychotic Medication for Prevention and Treatment of Delirium in Hospitalized Adults: A Systematic Review and Meta-Analysis.” J Am Geriatr Soc: n/a-n/a.
Objectives To evaluate the effectiveness of antipsychotic medications in preventing and treating delirium. Design Systematic review and meta-analysis. Setting PubMed, EMBASE, CINAHL, and databases were searched from January 1, 1988, to November 26, 2013. Participants Adult surgical and medical inpatients. Intervention Antipsychotic administration for delirium prevention or treatment in randomized controlled trials or cohort studies. Measurements Two authors independently reviewed all citations, extracted relevant data, and assessed studies for potential bias. Heterogeneity was considered as chi-square P < .1 or I2 > 50%. Using a random-effects model (I2 > 50%) or a fixed-effects model (I2 < 50%), odds ratios (ORs) were calculated for dichotomous outcomes (delirium incidence and mortality), and mean or standardized mean difference for continuous outcomes (delirium duration, severity, hospital and intensive care unit (ICU) length of stay (LOS)). Sensitivity analyses included postoperative prevention studies only, exclusion of studies with high risk of bias, and typical versus atypical antipsychotics. Results Screening of 10,877 eligible records identified 19 studies. In seven studies comparing antipsychotics with placebo or no treatment for delirium prevention after surgery, there was no significant effect on delirium incidence (OR = 0.56, 95% confidence interval (CI) = 0.23–1.34, I2 = 93%). Using data reported from all 19 studies, antipsychotic use was not associated with change in delirium duration, severity, or hospital or ICU LOS, with high heterogeneity among studies. No association with mortality was detected (OR = 0.90, 95% CI = 0.62–1.29, I2 = 0%). Conclusion Current evidence does not support the use of antipsychotics for prevention or treatment of delirium. Additional methodologically rigorous studies using standardized outcome measures are needed.

4 thoughts on “Delirium and the EEG Revisited: Blog Post by Medical Student Terrence Wong

  1. Jim,

    The EEG is a very good delirium detector. There has been some literature on two patterns – one fast and one slow. But even independent of eyes closed epochs, a standard EEG with delta waves qualifies for me. I have seen many patients who appear to be stunned on standard psychiatric medications like lamotrigine and valproate who have this abnormality along with various kinds of medical delirium. I also like the idea of a few electrodes. I think that you could probably use one electrode with the correct data analysis. The EEG is an underutilized test in medical psychiatry.

    Good luck.

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