TY - JOUR
T1 - Physiological Assessment of Delirium Severity
T2 - The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)
AU - Van Sleuwen, Meike
AU - Sun, Haoqi
AU - Eckhardt, Christine
AU - Neelagiri, Anudeepthi
AU - Tesh, Ryan A.
AU - Westmeijer, Mike
AU - Paixao, Luis
AU - Rajan, Subapriya
AU - Krishnamurthy, Parimala Velpula
AU - Sikka, Pooja
AU - Leone, Michael J.
AU - Panneerselvam, Ezhil
AU - Quadri, Syed A.
AU - Akeju, Oluwaseun
AU - Kimchi, Eyal Y.
AU - Westover, M. Brandon
N1 - Funding Information:
Drs. Kimchi’s and Westover’s institutions received funding from the National Institutes of Health (NIH). Drs. Kimchi and Westover received support for article research from the NIH. Dr. Westover received funding from Beacon Biosignals. The remaining authors have disclosed that they do not have any potential conflicts of interest.
Publisher Copyright:
Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - OBJECTIVES: Delirium 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). DESIGN: Retrospective cohort study. SETTING: Single-center tertiary academic medical center. PATIENTS: Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We 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. CONCLUSIONS: The 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.
AB - OBJECTIVES: Delirium 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). DESIGN: Retrospective cohort study. SETTING: Single-center tertiary academic medical center. PATIENTS: Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We 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. CONCLUSIONS: The 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.
KW - Clinical outcomes
KW - Delirium severity
KW - Electroencephalography
KW - Machine learning
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UR - http://www.scopus.com/inward/citedby.url?scp=85122296157&partnerID=8YFLogxK
U2 - 10.1097/CCM.0000000000005224
DO - 10.1097/CCM.0000000000005224
M3 - Article
C2 - 34582420
AN - SCOPUS:85122296157
VL - 50
SP - E11-E19
JO - Critical Care Medicine
JF - Critical Care Medicine
SN - 0090-3493
IS - 1
ER -