Non-Invasive Eye Tracking for the Diagnosis of Delirium on ICU

Delirium Clinical Trial

— CONfuSED  

Official title:

Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04589169
• Other study ID #: C&W19/064
• Secondary ID: IRAS number 2647
• Status: Recruiting
• Start date: December 7, 2020
• Est. completion date: October 12, 2021

Study information

• Verified date: December 2020
• Source: Chelsea and Westminster NHS Foundation Trust
• Contact: Research Delivery Operations Manager
• Phone: 020 3315 6825
• Email: research.development[@]chelwest.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 30
• Est. completion date: October 12, 2021
• Est. primary completion date: October 12, 2021
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Aged >18

2. Predicted Delirium as defined by the Early PREdiction of DELIRium in ICU patients (E-PREDELIRIC) score = 20%

3. Expected length of stay = 2 days

Exclusion Criteria:

1. Non-concordant eyes

2. Visual Impairment

3. Dementia

4. Inability for facial recognition and eye tracking to be performed reliably

5. Lack of signed consent form / nominated consultee form

6. In-ability to perform CAM-ICU reliably

Related Conditions & MeSH terms

• Delirium

Locations

Country	Name	City	State
United Kingdom	Chelsea and Westminster Hospital NHS Foundation Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
Chelsea and Westminster NHS Foundation Trust	BMA Foundation for Medical Research, Westminster Medical School Research Trust

Country where clinical trial is conducted

United Kingdom, 

References & Publications (20)

A. P. A. A. P. Association, et al., DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders, Wash ington, DC: American psychiatric association (1994) 143-147.

Beedie SA, Benson PJ, St Clair DM. Atypical scanpaths in schizophrenia: evidence of a trait- or state-dependent phenomenon? J Psychiatry Neurosci. 2011 May;36(3):150-64. doi: 10.1503/jpn.090169. Review. — View Citation

C. Exton, M. Leonard, Eye tracking technology: A fresh approach in delirium assessment?, International Review of Psychiatry 21 (2009) 8-14. [18] T. Fischer, H. Jin Chang, Y. Demiris, RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 334-352.

Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med. 2010 Jul 27;7(7):e1000316. doi: 10.1371/journal.pmed.1000316. Review. — View Citation

Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: recommendations for good practice. J Eval Clin Pract. 2004 May;10(2):307-12. — View Citation

Lin SM, Liu CY, Wang CH, Lin HC, Huang CD, Huang PY, Fang YF, Shieh MH, Kuo HP. The impact of delirium on the survival of mechanically ventilated patients. Crit Care Med. 2004 Nov;32(11):2254-9. — View Citation

Loughland CM, Williams LM, Gordon E. Schizophrenia and affective disorder show different visual scanning behavior for faces: a trait versus state-based distinction? Biol Psychiatry. 2002 Aug 15;52(4):338-48. — View Citation

Lundström M, Olofsson B, Stenvall M, Karlsson S, Nyberg L, Englund U, Borssén B, Svensson O, Gustafson Y. Postoperative delirium in old patients with femoral neck fracture: a randomized intervention study. Aging Clin Exp Res. 2007 Jun;19(3):178-86. — View Citation

M. Sarabia, Y. Demiris, A Humanoid Robot Companion for Wheelchair Users, Social Robotics 8239 (2013) 432-441.

MacMurchy M, Stemler S, Zander M, Bonafide CP. Research: Acceptability, Feasibility, and Cost of Using Video to Evaluate Alarm Fatigue. Biomed Instrum Technol. 2017 Jan-Feb;51(1):25-33. doi: 10.2345/0899-8205-51.1.25. — View Citation

McCusker J, Cole M, Abrahamowicz M, Primeau F, Belzile E. Delirium predicts 12-month mortality. Arch Intern Med. 2002 Feb 25;162(4):457-63. — View Citation

Meagher DJ. Delirium: optimising management. BMJ. 2001 Jan 20;322(7279):144-9. Review. — View Citation

Meier-Ruge W, Hunziker O, Iwangoff P. Senile dementia: a threshold phenomenon of normal aging? A contribution to the functional reserve hypothesis of the brain. Ann N Y Acad Sci. 1991;621:104-18. — View Citation

Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci Rep. 2016 May 17;6:26094. doi: 10.1038/srep26094. — View Citation

Ouimet S, Kavanagh BP, Gottfried SB, Skrobik Y. Incidence, risk factors and consequences of ICU delirium. Intensive Care Med. 2007 Jan;33(1):66-73. Epub 2006 Nov 11. — View Citation

Palmu R, Suominen K, Vuola J, Isometsä E. Mental disorders among acute burn patients. Burns. 2010 Nov;36(7):1072-9. doi: 10.1016/j.burns.2010.04.004. Epub 2010 May 18. — View Citation

Pisani MA, Araujo KL, Van Ness PH, Zhang Y, Ely EW, Inouye SK. A research algorithm to improve detection of delirium in the intensive care unit. Crit Care. 2006;10(4):R121. — View Citation

Trillenberg P, Lencer R, Heide W. Eye movements and psychiatric disease. Curr Opin Neurol. 2004 Feb;17(1):43-7. Review. — View Citation

Wassenaar A, van den Boogaard M, van Achterberg T, Slooter AJ, Kuiper MA, Hoogendoorn ME, Simons KS, Maseda E, Pinto N, Jones C, Luetz A, Schandl A, Verbrugghe W, Aitken LM, van Haren FM, Donders AR, Schoonhoven L, Pickkers P. Multinational development and validation of an early prediction model for delirium in ICU patients. Intensive Care Med. 2015 Jun;41(6):1048-56. doi: 10.1007/s00134-015-3777-2. Epub 2015 Apr 18. — View Citation

Y. Zhang, T. Wilcockson, K. I. Kim, T. Crawford, H. Gellersen, P. Sawyer, Monitoring Dementia with Automatic Eye Movements Analysis, in: I. Czarnowski, A. M. Caballero, R. J. Howlett, L. C. Jain (Eds.), Intelligent Decision Technologies 2016, volume 57, Springer International Publishing, Cham, 2016, pp. 299-309.

* Note: There are 20 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Demonstrate eye gaze correlates with CAM-ICU	Patient's usual clinical care will not be affected by the assessments performed by this study. In our institution, CAM-ICU is performed at least twice daily and more so if there are changes to mental status. The system will be switched on 5 minutes prior to performing CAM-ICU and will record for a duration of 10 minutes following which, the system will be turned off and the cameras covered. This procedure will occur every time CAM-ICU is performed.	8-12 months
Secondary	Acceptance of the use of cameras and artificial intelligence on ICU	Once per day, the research staff will complete an acceptability questionnaire designed to ascertain the acceptability of the use of cameras within this study on day to day activities in ICU.This questionnaire is based on work by MacMurchy et al.; Minimum value is 0, maximum value is 4.; Higher scores indicate that the system affected the nurse's ability to communicate with patients, nurses and doctors and that it affected the nurse's ability to care for them.; A yes answer = 1 point, a No answer = 0 points. Marking will be done per question to gain insights into how the system affected the nurses.	8-12 months