An automated electroencephalography algorithm to detect polymorphic delta activity in acute encephalopathy presenting as postoperative delirium

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

OBJECTIVES

To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs).

DESIGN

Prospective cross-sectional study.

SETTING

Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals.

PARTICIPANTS

494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious.

MEASUREMENTS

DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets.

METHODS

Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus.

RESULTS

DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity.

CONCLUSIONS

DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.

Electroencephalographic Measures of Delirium in the Perioperative Setting: A Systematic Review

Postoperative delirium (POD) is frequent in older adults and is associated with adverse cognitive and functional outcomes. In the last several decades, there has been an increased interest in exploring tools that easily allow the early recognition of patients at risk of developing POD. The electroencephalogram (EEG) is a widely available tool used to understand delirium pathophysiology, and its use in the perioperative setting has grown exponentially, particularly to predict and detect POD. We performed a systematic review to investigate the use of EEG in the pre-, intra-, and postoperative settings. We identified 371 studies, and 56 met the inclusion criteria. A range of techniques was used to obtain EEG data, from limited 1-4 channel setups to complex 256-channel systems. Power spectra were often measured preoperatively, yet the outcomes were inconsistent. During surgery, the emphasis was primarily on burst suppression (BS) metrics and power spectra, with a link between the frequency and timing of BS, and POD. The EEG patterns observed in POD aligned with those noted in delirium in different contexts, suggesting a reduction in EEG activity. Further research is required to investigate preoperative EEG indicators that may predict susceptibility to delirium.

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.