(PO-110) (PO-110) Masimo Cerebral State Monitoring and Detection of Delirium in Hospitalized Patients

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Background:
Delirium is often undetected among hospitalized patients, resulting in significant morbidity. Current screening tools such as the Confusion Assessment Method (CAM) have limited validity in busy clinical settings. FDA-approved cerebral state monitors (CSMs) that record limited channel processed EEG have the potential to serve as an objective and accessible screening tool for delirium. We have previously shown that visual data from CSMs can detect delirium in hospitalized patients, but did not analyze raw EEG data due to device restrictions (Luo et al., 2020). In this ongoing prospective cohort study, we are testing if raw EEG data obtained from a Masimo CSM could improve upon CAM in detecting delirium in hospitalized patients.

Methods:
After IRB approval, 22 participants (15 non-delirious and 7 delirious) were recruited from medically hospitalized patients receiving psychiatric consultation. 3D-CAM was obtained before monitoring with a Masimo CSM for 10 minutes with eyes closed. Four-channel frontotemporal raw EEG data was used to generate frequency spectrograms with a MATLAB-based program, Brainstorm. Power values were extracted for low/high alpha, beta, theta, and delta frequency bands in each channel; mean frequency band power and ratios (e.g., low alpha/low theta) were calculated. Using Mann Whitney U tests, EEG variables were compared between the two groups to assess for significant association with delirium. 3D-CAM accuracy was assessed with Fisher’s Exact test.

Results:
High delta, low alpha/high delta, and low theta/high delta were found to be significantly associated with delirium (p=.048, p=.013, p=.013, respectively). 3D-CAM did not differentiate between delirious and non-delirious participants (Fisher’s Exact Test, p=.077).

Discussion:
As delirium develops, the EEG reflects diminution of alpha power and increases in theta and delta power. High delta (2-4 Hz) power appears crucial for identifying delirium, as it may be the least susceptible to artifact. Our results indicate incomplete overlap in EEG variables between the current study and previous results, which may represent an early confirmation of our original findings. Alternatively, our small sample may be skewed by outlying data, and is still underpowered to survive multiple comparisons. Our current analytic methods represent a significant improvement in precision over our previous analysis of color density spectral arrays, which imposed unquantified but likely significant variability. Future steps involve enrolling more participants to validate these early findings.

Conclusion
Once validated, a Masimo CSM could improve upon the detection of delirium in hospitalized patients and serve as a widespread objective screening tool.

Reference:

Luo, A., Muraida, S., Pinchotti, D., Richardson, E., Ye, E., Hollingsworth, B., Win, A., Myers, O., Langsjoen, J., Valles, E., Zolyomi, A., & Quinn, D. K. (2020). Bispectral Index Monitoring With Density Spectral Array for Delirium Detection. Psychosomatics, S0033-3182(20)30242-5. Advance online publication.

Learning Objectives:
Recognize the limitations of current screening tools for delirium and how a Masimo Cerebral State Monitor could serve as an accessible and objective screening tool.
Understand how power spectral density analysis of raw electroencephalography data obtained from a Masimo Cerebral State Monitor could improve upon detection of delirium in hospitalized patients.
Differentiate which power frequency bands and ratios may be associated with delirium in hospitalized patients.

Presenting Author(s)

Alice Luo, MD
– Consultation-Liaison Psychiatry Fellow, Memorial Sloan Kettering Cancer Center, New York, NY, United States
Co-Author(s)

Samuel MacDonald
– Medical Student, University of New Mexico, Albuquerque, NM, United States

Davin Quinn, MD, FACLP
– Vice Chair, Adult Clinical Services, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States