DeliriumNCT07536854
Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning
An observational study of Delirium and Trauma, run by Ajou University School of Medicine at 1 site in South Korea.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Delirium, Trauma, Brain Dysfunction
- How many people
- 73 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Apr 2024to 30 Apr 2025
- How long will I take part?
- 3 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
- 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)
- 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).
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Delirium, Trauma, Brain Dysfunction, Critical Illness
- Keywords
- delirium, EEG, machine learning, Delirium prediction
- Medical subject headings
- Delirium, Wounds and Injuries, Critical Illness
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
Predictive Performance for Delirium (Area Under the Receiver Operating Characteristic Curve, AUROC
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.
Measured: 3 to 4 days (during the longitudinal EEG data collection period)
Secondary outcomes (1)
Comparison of Model Performance: Eyes-Open vs. Eyes-Closed States
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.
Measured: 3 to 4 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2026
- Study start1 Apr 2024
- Primary completion27 Apr 2025
- Study completion30 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for DeliriumStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 73 participants (actual)
- Target duration
- 3 Days
Sponsor and investigators
- Lead sponsor
- Ajou University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jinjoo Kim, Ajou University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07536854
- Sponsor's protocol ID
- AJOUIRB-IV-2024-227
- Status last verified
- Apr 2026
- First submitted
- 12 Apr 2026
- Last updated
- 17 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to institutional policies regarding data privacy and the protection of sensitive patient information.
Publications
Sun H, Kimchi E, Akeju O, Nagaraj SB, McClain LM, Zhou DW, Boyle E, Zheng WL, Ge W, Westover MB. Automated tracking of level of consciousness and delirium in critical illness using deep learning. NPJ Digit Med. 2019 Sep 9;2:89. doi: 10.1038/s41746-019-0167-0. eCollection 2019.
PubMed 31508499Numan T, van den Boogaard M, Kamper AM, Rood PJT, Peelen LM, Slooter AJC; Dutch Delirium Detection Study Group. Delirium detection using relative delta power based on 1-minute single-channel EEG: a multicentre study. Br J Anaesth. 2019 Jan;122(1):60-68. doi: 10.1016/j.bja.2018.08.021. Epub 2018 Oct 2.
PubMed 30579407Kim H, McKinney A, Brooks J, Mashour GA, Lee U, Vlisides PE. Delirium, Caffeine, and Perioperative Cortical Dynamics. Front Hum Neurosci. 2021 Dec 20;15:744054. doi: 10.3389/fnhum.2021.744054. eCollection 2021.
PubMed 34987367Hshieh TT, Saczynski J, Gou RY, Marcantonio E, Jones RN, Schmitt E, Cooper Z, Ayres D, Wright J, Travison TG, Inouye SK; SAGES Study Group. Trajectory of Functional Recovery After Postoperative Delirium in Elective Surgery. Ann Surg. 2017 Apr;265(4):647-653. doi: 10.1097/SLA.0000000000001952.
PubMed 27501176Bryczkowski SB, Lopreiato MC, Yonclas PP, Sacca JJ, Mosenthal AC. Risk factors for delirium in older trauma patients admitted to the surgical intensive care unit. J Trauma Acute Care Surg. 2014 Dec;77(6):944-51. doi: 10.1097/TA.0000000000000427.
PubMed 25248058Walder B, Haase U, Rundshagen I. [Sleep disturbances in critically ill patients]. Anaesthesist. 2007 Jan;56(1):7-17. doi: 10.1007/s00101-006-1086-4. German.
PubMed 16953422
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Longitudinal Frontal EEG Trajectories Reveal Divergent Cortical Dynamics in Delirium After Severe Trauma
- ClinicalTrials.gov ID
- NCT07536854
- Acronym / sponsor ID
- AJOUIRB-IV-2024-227
- Conditions
- Delirium, Trauma, Brain Dysfunction, Critical Illness
- Keywords
- delirium, EEG, machine learning, Delirium prediction
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 73 participants (actual)
- Lead sponsor
- Ajou University School of MedicineUniversity, hospital or other
- Study start
- 1 Apr 2024 (actual)
- Primary completion
- 27 Apr 2025 (actual)
- Study completion
- 30 Apr 2025 (actual)
- First posted
- 17 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Days
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
1 secondary outcome
Comparison of Model Performance: Eyes-Open vs. Eyes-Closed States
Time frame: 3 to 4 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2026.
- Background: PubMed 31508499Sun H, Kimchi E, Akeju O, Nagaraj SB, McClain LM, Zhou DW, Boyle E, Zheng WL, Ge W, Westover MB. Automated tracking of level of consciousness and delirium in critical illness using deep learning. NPJ Digit Med. 2019 Sep 9;2:89. doi: 10.1038/s41746-019-0167-0. eCollection 2019.
- Background: PubMed 30579407Numan T, van den Boogaard M, Kamper AM, Rood PJT, Peelen LM, Slooter AJC; Dutch Delirium Detection Study Group. Delirium detection using relative delta power based on 1-minute single-channel EEG: a multicentre study. Br J Anaesth. 2019 Jan;122(1):60-68. doi: 10.1016/j.bja.2018.08.021. Epub 2018 Oct 2.
- Background: PubMed 34987367Kim H, McKinney A, Brooks J, Mashour GA, Lee U, Vlisides PE. Delirium, Caffeine, and Perioperative Cortical Dynamics. Front Hum Neurosci. 2021 Dec 20;15:744054. doi: 10.3389/fnhum.2021.744054. eCollection 2021.
- Background: PubMed 27501176Hshieh TT, Saczynski J, Gou RY, Marcantonio E, Jones RN, Schmitt E, Cooper Z, Ayres D, Wright J, Travison TG, Inouye SK; SAGES Study Group. Trajectory of Functional Recovery After Postoperative Delirium in Elective Surgery. Ann Surg. 2017 Apr;265(4):647-653. doi: 10.1097/SLA.0000000000001952.
- Background: PubMed 25248058Bryczkowski SB, Lopreiato MC, Yonclas PP, Sacca JJ, Mosenthal AC. Risk factors for delirium in older trauma patients admitted to the surgical intensive care unit. J Trauma Acute Care Surg. 2014 Dec;77(6):944-51. doi: 10.1097/TA.0000000000000427.
- Background: PubMed 16953422Walder B, Haase U, Rundshagen I. [Sleep disturbances in critically ill patients]. Anaesthesist. 2007 Jan;56(1):7-17. doi: 10.1007/s00101-006-1086-4. German.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to institutional policies regarding data privacy and the protection of sensitive patient information.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Delirium and Trauma, run by Ajou University School of Medicine at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07536854, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2026.