Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).
Crit Care Med
; 50(1): e11-e19, 2022 01 01.
Article
em En
| MEDLINE
| ID: mdl-34582420
ABSTRACT
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 MAINRESULTS:
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Confusão
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Delírio
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Eletroencefalografia
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Aprendizado de Máquina
Tipo de estudo:
Observational_studies
/
Prognostic_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article