VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes

Ryan A. Tesh; Haoqi Sun; Jin Jing; Mike Westmeijer; Anudeepthi Neelagiri; Subapriya Rajan; Parimala V. Krishnamurthy; Pooja Sikka; Syed A. Quadri; Michael J. Leone; Luis Paixao; Ezhil Panneerselvam; Christine Eckhardt; Aaron F. Struck; Peter W. Kaplan; Oluwaseun Akeju; Daniel Jones; Eyal Y. Kimchi; M. Brandon Westover

doi:10.1097/CCE.0000000000000611

VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes

Ryan A. Tesh, Haoqi Sun, Jin Jing, Mike Westmeijer, Anudeepthi Neelagiri, Subapriya Rajan, Parimala V. Krishnamurthy, Pooja Sikka, Syed A. Quadri, Michael J. Leone, Luis Paixao, Ezhil Panneerselvam, Christine Eckhardt, Aaron F. Struck, Peter W. Kaplan, Oluwaseun Akeju, Daniel Jones, Eyal Y. Kimchi, M. Brandon Westover^*

^*Corresponding author for this work

Neurology

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

OBJECTIVES: To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.

Original language	English (US)
Pages (from-to)	E0611
Journal	Critical Care Explorations
Volume	4
Issue number	1
DOIs	https://doi.org/10.1097/CCE.0000000000000611
State	Published - Jan 18 2022

Funding

Dr. Kimchi was supported by the National Institutes of Health (NIH) (K08MH116135). Dr. Westover is a co-founder of Beacon Biosignals. Dr. Westover was supported by the Glenn Foundation for Medical Research and American Federation for Aging Research (Breakthroughs in Gerontology Grant); American Academy of Sleep Medicine (Foundation Strategic Research Award); Football Players Health Study at Harvard University; Department of Defense through a subcontract from Moberg ICU Solutions; and NIH (R01NS102190, R01NS102574, R01NS107291, RF1AG064312, R01AG062989). The remaining authors have disclosed that they do not have any potential conflicts of interest.

Keywords

EEG
delirium
delirium severity
encephalopathy
prognosis

ASJC Scopus subject areas

Critical Care and Intensive Care Medicine

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10.1097/CCE.0000000000000611

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Tesh, R. A., Sun, H., Jing, J., Westmeijer, M., Neelagiri, A., Rajan, S., Krishnamurthy, P. V., Sikka, P., Quadri, S. A., Leone, M. J., Paixao, L., Panneerselvam, E., Eckhardt, C., Struck, A. F., Kaplan, P. W., Akeju, O., Jones, D., Kimchi, E. Y., & Westover, M. B. (2022). VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes. Critical Care Explorations, 4(1), E0611. https://doi.org/10.1097/CCE.0000000000000611

@article{62673f00619c4bfca32704c644ab2be4,

title = "VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes",

abstract = "OBJECTIVES: To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.",

keywords = "EEG, delirium, delirium severity, encephalopathy, prognosis",

author = "Tesh, {Ryan A.} and Haoqi Sun and Jin Jing and Mike Westmeijer and Anudeepthi Neelagiri and Subapriya Rajan and Krishnamurthy, {Parimala V.} and Pooja Sikka and Quadri, {Syed A.} and Leone, {Michael J.} and Luis Paixao and Ezhil Panneerselvam and Christine Eckhardt and Struck, {Aaron F.} and Kaplan, {Peter W.} and Oluwaseun Akeju and Daniel Jones and Kimchi, {Eyal Y.} and Westover, {M. Brandon}",

note = "Publisher Copyright: {\textcopyright} 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.",

year = "2022",

month = jan,

day = "18",

doi = "10.1097/CCE.0000000000000611",

language = "English (US)",

volume = "4",

pages = "E0611",

journal = "Critical Care Explorations",

issn = "2639-8028",

publisher = "Lippincott Williams and Wilkins",

number = "1",

}

Tesh, RA, Sun, H, Jing, J, Westmeijer, M, Neelagiri, A, Rajan, S, Krishnamurthy, PV, Sikka, P, Quadri, SA, Leone, MJ, Paixao, L, Panneerselvam, E, Eckhardt, C, Struck, AF, Kaplan, PW, Akeju, O, Jones, D, Kimchi, EY & Westover, MB 2022, 'VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes', Critical Care Explorations, vol. 4, no. 1, pp. E0611. https://doi.org/10.1097/CCE.0000000000000611

TY - JOUR

T1 - VE-CAM-S

T2 - Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes

AU - Tesh, Ryan A.

AU - Sun, Haoqi

AU - Jing, Jin

AU - Westmeijer, Mike

AU - Neelagiri, Anudeepthi

AU - Rajan, Subapriya

AU - Krishnamurthy, Parimala V.

AU - Sikka, Pooja

AU - Quadri, Syed A.

AU - Leone, Michael J.

AU - Paixao, Luis

AU - Panneerselvam, Ezhil

AU - Eckhardt, Christine

AU - Struck, Aaron F.

AU - Kaplan, Peter W.

AU - Akeju, Oluwaseun

AU - Jones, Daniel

AU - Kimchi, Eyal Y.

AU - Westover, M. Brandon

PY - 2022/1/18

Y1 - 2022/1/18

N2 - OBJECTIVES: To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.

AB - OBJECTIVES: To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.

KW - EEG

KW - delirium

KW - delirium severity

KW - encephalopathy

KW - prognosis

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VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes

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