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

Meike Van Sleuwen, Haoqi Sun, Christine Eckhardt, Anudeepthi Neelagiri, Ryan A. Tesh, Mike Westmeijer, Luis Paixao, Subapriya Rajan, Parimala Velpula Krishnamurthy, Pooja Sikka, Michael J. Leone, Ezhil Panneerselvam, Syed A. Quadri, Oluwaseun Akeju, Eyal Y. Kimchi, M. Brandon Westover

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)E11-E19
JournalCritical care medicine
Issue number1
StatePublished - Jan 1 2022


  • Clinical outcomes
  • Delirium severity
  • Electroencephalography
  • Machine learning

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine


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