Clinical trial NCT04589169: Non-Invasive Eye Tracking for the Diagnosis of Delirium on ICU
Official title: Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Delirium
Keywords: Delirium
Inattention
Eye-gaze
ICU
Lead sponsor: Chelsea and Westminster NHS Foundation Trust
Sponsor class: OTHER
Collaborators: name: Westminster Medical School Research Trust
name: BMA Foundation for Medical Research
First posted: 2020-10-19
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult inpatients in ICU

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.

Eligibility criteria: 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

Primary outcomes:
measure: Demonstrate eye gaze correlates with CAM-ICU
timeFrame: 8-12 months
Secondary outcomes:
measure: Acceptance of the use of cameras and artificial intelligence on ICU
timeFrame: 8-12 months
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Chelsea and Westminster Hospital NHS Foundation Trust
Study officials:
affiliation: Chelsea and Westminster NHS Foundation Trust
name: Ahmed Al-Hindawi, BMBS, MRCA
role: PRINCIPAL_INVESTIGATOR
References:
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. doi: 10.1001/archinte.162.4.457.
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.
citation: M. Sarabia, Y. Demiris, A Humanoid Robot Companion for Wheelchair Users, Social Robotics 8239 (2013) 432-441.
citation: 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.
citation: 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.
citation: Palmu R, Suominen K, Vuola J, Isometsa 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.
citation: Lundstrom M, Olofsson B, Stenvall M, Karlsson S, Nyberg L, Englund U, Borssen 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. doi: 10.1007/BF03324687.
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. doi: 10.1111/j.1749-6632.1991.tb16973.x.
citation: Meagher DJ. Delirium: optimising management. BMJ. 2001 Jan 20;322(7279):144-9. doi: 10.1136/bmj.322.7279.144. No abstract available.
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. doi: 10.1097/01.ccm.0000145587.16421.bb.
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. doi: 10.1007/s00134-006-0399-8. Epub 2006 Nov 11.
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. doi: 10.1186/cc5027.
citation: 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.
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. doi: 10.1016/s0006-3223(02)01356-2.
citation: Trillenberg P, Lencer R, Heide W. Eye movements and psychiatric disease. Curr Opin Neurol. 2004 Feb;17(1):43-7. doi: 10.1097/00019052-200402000-00008.
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.
citation: Exton C, Leonard M. Eye tracking technology: a fresh approach in delirium assessment? Int Rev Psychiatry. 2009 Feb;21(1):8-14. doi: 10.1080/09540260802675106.
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. doi: 10.1111/j..2002.384.doc.x.
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.
citation: 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. No abstract available.