Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S). Issue 1 (27th September 2021)

• Record Type: Journal Article
• Authors: van Sleuwen, Meike; Sun, Haoqi; Eckhardt, Christine; Neelagiri, Anudeepthi; Tesh, Ryan A.; Westmeijer, Mike; Paixao, Luis; Rajan, Subapriya; Velpula Krishnamurthy, Parimala; Sikka, Pooja; Leone, Michael J.; Panneerselvam, Ezhil; Quadri, Syed A.; Akeju, Oluwaseun; Kimchi, Eyal Y.; Westover, M. Brandon
• Is Part Of: Critical care medicine. Volume 50:Issue 1(2022)
• Page Start: e11
• Page End: e19
• Publication Date: 2021-09-27
• Subjects: clinical outcomes -- delirium severity -- electroencephalography -- machine learning; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000005224
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : Supplemental Digital Content is available in the text. 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 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.