Ignite Creation Date:
2025-12-24 @ 11:13 PM
Ignite Modification Date:
2025-12-25 @ 8:49 PM
Study NCT ID:
NCT04589169
Status:
UNKNOWN
Last Update Posted:
2020-12-08
First Post:
2020-10-09
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Non-Invasive Eye Tracking for the Diagnosis of Delirium on ICU
Sponsor:
Chelsea and Westminster NHS Foundation Trust
Organization:
Study Overview
Official Title:
Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit
Status:
UNKNOWN
Status Verified Date:
2020-12
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
CONfuSED
Brief Summary:
In this trial, the investigators seek to demonstrate the feasibility of a system in continuously detecting 'inattention' as a subset of CAM-ICU in a small representative sample of patients in the Intensive Care Unit (ICU) at Chelsea \& Westminster NHS Foundation Trust.
Detailed Description:
Delirium is an acute confusional state that affects many patients admitted to the hospital, especially intensive care. The current diagnosis of delirium is through the use of the Confusional Assessment Method in Intensive Care Unit (CAM-ICU) task based questionnaire. The core prinicipal to CAM-ICU is inattention; this is tested through asking the patient to remember a task and execute it on demand, e.g. squeezing the operator's hand everytime the letter A is said and then spelling CASABLANCA.
The aim of this study is to find correlates to inattention. Eye-gaze data is ideally suited for this task as eyes move to pay attention to the environment.
A video camera based eye-tracker has been developed that sits at the end of the bed (head-camera) and another behind the patient (scene-camera). The head-camera uses machine learning to measure the gaze of the patient's eyes while the scene-camera finds what the patient is looking at. Simulations are then run from the scene camera and the patient's gaze is then compared to find whether the patient is paying attention to what is simulated.
Once per day, a member of the local research team will fill in a non-validated questionnaire based on work by MacMurchy et al.
M. MacMurchy, S. Stemler, M. Zander, C. P. Bonafide, Acceptability, Feasibility, and Cost of Using Video to Evaluate Alarm Fatigue, Biomedical Instrumentation \& Technology 51 (2017) 25-33.
The aim of this study is to find correlates to inattention. Eye-gaze data is ideally suited for this task as eyes move to pay attention to the environment.
A video camera based eye-tracker has been developed that sits at the end of the bed (head-camera) and another behind the patient (scene-camera). The head-camera uses machine learning to measure the gaze of the patient's eyes while the scene-camera finds what the patient is looking at. Simulations are then run from the scene camera and the patient's gaze is then compared to find whether the patient is paying attention to what is simulated.
Once per day, a member of the local research team will fill in a non-validated questionnaire based on work by MacMurchy et al.
M. MacMurchy, S. Stemler, M. Zander, C. P. Bonafide, Acceptability, Feasibility, and Cost of Using Video to Evaluate Alarm Fatigue, Biomedical Instrumentation \& Technology 51 (2017) 25-33.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| IRAS number 264759 | OTHER | Integrated Research Application System | View |