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

Crit Care Med. 2022 Jan 1;50(1):e11-e19. doi: 10.1097/CCM.0000000000005224.


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.

Publication types

  • Observational Study

MeSH terms

  • Academic Medical Centers / statistics & numerical data
  • Adult
  • Aged
  • Comorbidity
  • Confusion / diagnosis*
  • Delirium / diagnosis*
  • Electroencephalography / methods*
  • Female
  • Hospital Mortality / trends
  • Hospitals / statistics & numerical data
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Length of Stay / statistics & numerical data
  • Machine Learning*
  • Male
  • Middle Aged
  • Patient Acuity
  • Prognosis
  • Retrospective Studies
  • Severity of Illness Index