• Meike van Sleuwen, Haoqi Sun, Christine Eckhardt, Anudeepthi Neelagiri, Ryan A Tesh, Mike Westmeijer, Luis Paixao, Subapriya Rajan, Parimala Velpula Krishnamurthy, Pooja Sikka, Michael J Leone, Ezhil Panneerselvam, Syed A Quadri, Oluwaseun Akeju, Eyal Y Kimchi, and M Brandon Westover.
• Department of Neurology, Massachusetts General Hospital, Boston, MA.
• Crit. Care Med. 2022 Jan 1; 50 (1): e11e19e11-e19.

ObjectivesDelirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).DesignRetrospective cohort study.SettingSingle-center tertiary academic medical center.PatientsThree-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.InterventionsNone.Measurements And Main ResultsWe developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.ConclusionsThe E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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