Longitudinal Frontal EEG Trajectories Reveal Divergent Cortical Dynamics in Delirium After Severe Trauma
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 73
- 试验地点
- 1
- 主要终点
- Predictive Performance for Delirium (Area Under the Receiver Operating Characteristic Curve, AUROC
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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)
排除标准
- •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).
研究组 & 干预措施
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.
结局指标
主要结局
Predictive Performance for Delirium (Area Under the Receiver Operating Characteristic Curve, AUROC
时间窗: 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.
次要结局
- Comparison of Model Performance: Eyes-Open vs. Eyes-Closed States(3 to 4 days)
研究者
Jinjoo Kim
Clinical associate professor
Ajou University School of Medicine