Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)
| Autor: | Oluwaseun Akeju, Anudeepthi Neelagiri, Luis Paixao, Mike Westmeijer, Ezhil Panneerselvam, Parimala Velpula Krishnamurthy, Meike van Sleuwen, Ryan A. Tesh, Michael J. Leone, Syed A. Quadri, Christine Eckhardt, M. Brandon Westover, Subapriya Rajan, Eyal Y. Kimchi, Pooja Sikka, Haoqi Sun |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Adult
Male medicine.medical_specialty Psychological intervention Comorbidity Electroencephalography Critical Care and Intensive Care Medicine Severity of Illness Index Article Machine Learning Altered Mental Status Internal medicine medicine Image Processing Computer-Assisted Humans Hospital Mortality Confusion Aged Retrospective Studies Academic Medical Centers medicine.diagnostic_test business.industry Area under the curve Patient Acuity Delirium Retrospective cohort study Length of Stay Middle Aged Prognosis Hospitals Cohort Female medicine.symptom business Complication |
| Zdroj: | Crit Care Med |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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