Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)

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.

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

“…We therefore have given maximal points for features that cannot be had in the absence of an appropriate level of consciousness (e.g., negative symptoms) but have not assigned points that can only occur with an appropriate level of consciousness (e.g., positive symptoms of specifically features 7: Perceptual disturbances such as hallucinations and 8: Psychomotor agitation), yielding a score of 15. This practice is consistent with our previously published work, in which patients with coma were assigned maximal CAM-S short form scores (35). Expanded information on evaluation questions and rules used for delirium symptom severity scoring for each category are given in eTables 1 and 2 (http://links.lww.com/CCX/A884).…”

Monitoring of sedation in mechanically ventilated patients using remote technology

“…Van Sleuwen et al used machine learning to predict the presence and severity of delirium (EEG Confusion Assessment Method severity score; E-CAM-S score) in a heterogeneous ICU population ( n = 373) based on four frontal EEG channels. Severity of delirium was associated with more EEG slowing (increased delta content, reduced alpha content), and E-CAM-S score correlated well with clinical CAM-S as well as with in-hospital mortality [43 ▪ ].…”

Postoperative delirium in geriatric patients with hip fractures

“…Furthermore, it is demonstrated that the electrocardiogram (ECG) slowing, such as a composite of generalized theta or delta slowing (OR 10.3, 95% CI, and 5.3–20.1), is associated with delirium severity and poor clinical outcomes ( Kimchi et al, 2019 ). Recently, an automated physiologic process that quantifies the presence and severity of delirium directly has been reported, called the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S; van Sleuwen et al, 2022 ). It is based on a learning-to-rank machine learning model of forehead electroencephalography signals, with a level of performance comparable to conventional interview-based clinical assessment.…”