Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning
Summary
NIH registered observational study NCT07536854 on ClinicalTrials.gov. The study aims to develop a machine learning model predicting delirium in trauma ICU patients using serial EEG recordings. Researchers will analyze brainwave patterns across recording conditions to identify early delirium biomarkers before clinical onset.
What changed
NIH registered a new observational clinical trial (NCT07536854) titled 'Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning' on ClinicalTrials.gov. The study will collect EEG data over several days in the trauma ICU, analyzing brainwave patterns under different conditions to develop a machine learning model for pre-symptomatic delirium detection.
Healthcare providers and clinical investigators should be aware this observational study may inform future delirium monitoring protocols in critical care settings. The research focuses on trauma ICU patients and represents an exploratory approach to predictive analytics in critical illness management.
Archived snapshot
Apr 17, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning
Observational NCT07536854 Kind: OBSERVATIONAL Apr 17, 2026
Abstract
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.
Conditions: Delirium, Trauma, Brain Dysfunction, Critical Illness
Related changes
Get daily alerts for ClinicalTrials.gov Studies
Daily digest delivered to your inbox.
Free. Unsubscribe anytime.
Source
About this page
Every important government, regulator, and court update from around the world. One place. Real-time. Free. Our mission
Source document text, dates, docket IDs, and authority are extracted directly from NIH.
The summary, classification, recommended actions, deadlines, and penalty information are AI-generated from the original text and may contain errors. Always verify against the source document.
Classification
Who this affects
Taxonomy
Browse Categories
Get alerts for this source
We'll email you when ClinicalTrials.gov Studies publishes new changes.
Subscribed!
Optional. Filters your digest to exactly the updates that matter to you.