Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning

April 12, 2026 updated by: Jinjoo Kim, Ajou University School of Medicine

Longitudinal Frontal EEG Trajectories Reveal Divergent Cortical Dynamics in Delirium After Severe Trauma

The goal of this observational study is to develop a machine learning model that can predict delirium in trauma patients before it clinically appears. The study focuses on analyzing brainwave (EEG) patterns collected over several days in the trauma ICU. By comparing different recording conditions-such as having eyes open versus closed-researchers aim to identify the most effective way to monitor brain health and detect early signs of delirium in critically ill patients.

Study Overview

Status

Completed

Conditions

Detailed Description

Background and Rationale:

Delirium is a critical manifestation of acute brain dysfunction, affecting 10-15% of all hospitalized patients and over 25% of those in intensive care units (ICU). In the trauma ICU, patients are particularly vulnerable due to an inflammatory cascade from repeated surgeries, blood-brain barrier disruption, traumatic brain injury (TBI), and mandatory opioid administration. Despite its clinical significance-including increased mortality and long-term cognitive impairment-early detection remains challenging. Current bedside tools like the CAM-ICU are limited by their periodic nature and dependence on clinician expertise, often missing the rapid neurophysiologic fluctuations that define delirium.

Study Objectives and Methodology:

While previous studies have used electroencephalography (EEG) as a "snapshot" to identify delirium, such cross-sectional approaches often reflect transient sedative depth rather than true neurocognitive vulnerability. This study proposes a longitudinal approach, focusing on the trajectory of change in cortical dynamics over time.

We acquired brief, serial resting-state EEG three times daily for at least three consecutive days from critically ill trauma patients. Using a feasible frontal montage, we quantified a comprehensive set of features, including spectral power (slowing), nonlinear complexity, and phase-based functional connectivity.

Research Hypothesis:

The framework utilizes machine learning (ML) to harness these longitudinal trajectories, aiming to predict delirium vulnerability before formal clinical diagnosis. Furthermore, we hypothesize that eyes-open recordings-by imposing a minimal arousal constraint-will better capture wakeful network integrity and provide superior predictive power compared to traditional eyes-closed recordings, which are often confounded by sedation and drowsiness in the trauma ICU environment.

Clinical Impact:

By identifying the optimal recording condition and establishing an ML-based prediction framework, this study seeks to define a standardized neurophysiologic monitoring strategy. This will ultimately facilitate early intervention and improve the long-term neurological prognosis of severe trauma survivors.

Study Type

Observational

Enrollment (Actual)

73

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • South Korea
    • Kyonggi-do
      • Suwon, Kyonggi-do, South Korea, 16499
        • Ajou University Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population consists of adult trauma patients (aged 18-65) admitted to the Trauma Intensive Care Unit (TICU) at a level 1 trauma center in Korea. The cohort includes critically ill patients with a record of severe injury (ISS≥ 9) who are able to undergo serial EEG monitoring and clinical delirium assessments. Patients with pre-existing neurological or psychiatric disorders, or those with severe traumatic brain injury (AIS ≥ 2), are excluded to ensure the specificity of the neurophysiologic data.

Description

  1. Inclusion Criteria:

    Trauma patients admitted to the Trauma Intensive Care Unit (TICU) who meet the following criteria:

    • Patients aged 18 to 65 years.
    • Severe trauma patients with an Injury Severity Score (ISS)
  2. Exclusion Criteria:

Patients with a head Abbreviated Injury Scale (AIS) ≥ 2 Patients with a Richmond Agitation-Sedation Scale (RASS) score ≤ -2 History of neurological disorders (e.g., Parkinson's disease, dementia, cerebrovascular disease) History of major psychiatric disorders (e.g., schizophrenia, bipolar disorder, intellectual disability, autism spectrum disorder) History of illicit drug use disorder or positive results on a urine drug screen for substances other than Benzodiazepines or Tricyclic antidepressants.

Clinical evidence of acute alcohol withdrawal (CIWA-Ar score > 10) History of liver failure or hepatic encephalopathy (Child-Pugh Class B or C) Renal impairment requiring renal replacement therapy (RRT) Inability to perform the Confusion Assessment Method for the ICU (CAM-ICU) due to the following Inability to communicate in Korean Failure to obey commands (unable to follow test instructions) Severe visual or hearing impairment Refusal to undergo CAM-ICU assessment Requirement for isolation due to infectious diseases (e.g., COVID-19, active tuberculosis).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Delirum group
Patients who developed delirium during their ICU stay (confirmed by CAM-ICU)
Non-Delirium Group
Patients who did not develop delirium during their ICU stay.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive Performance for Delirium (Area Under the Receiver Operating Characteristic Curve, AUROC
Time Frame: 3 to 4 days (during the longitudinal EEG data collection period)
The predictive accuracy of the machine learning model based on longitudinal EEG trajectories will be evaluated to identify patients at risk of delirium. Model performance will be assessed using AUROC, sensitivity, specificity, and F1-score.
3 to 4 days (during the longitudinal EEG data collection period)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison of Model Performance: Eyes-Open vs. Eyes-Closed States
Time Frame: 3 to 4 days
Comparison of the area under the receiver operating characteristic curve (AUROC) between EEG data recorded during eyes-open and eyes-closed resting states to determine which condition provides superior predictive power.
3 to 4 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ajou University School of Medicine

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 1, 2024

Primary Completion (Actual)

April 27, 2025

Study Completion (Actual)

April 30, 2025

Study Registration Dates

First Submitted

April 12, 2026

First Submitted That Met QC Criteria

April 12, 2026

First Posted (Actual)

April 17, 2026

Study Record Updates

Last Update Posted (Actual)

April 17, 2026

Last Update Submitted That Met QC Criteria

April 12, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AJOUIRB-IV-2024-227

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared due to institutional policies regarding data privacy and the protection of sensitive patient information.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Delirium

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Estonia

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe