CogMe for the Prevention and Early Detection of Delirium

Delirium Clinical Trial

— CogMe  

Official title:

Evaluation of the CogMe Technology Platform for the Prevention and Early Detection of Delirium Among Older Patients in an Acute Hospital Setting: A Proof of Concept Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05311761
• Other study ID #: 0589-21-RMB
• Status: Recruiting
• Phase: N/A
• Start date: March 1, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: September 2023
• Source: Rambam Health Care Campus
• Contact: Tzvi Dwolatzky, MD MBBCh
• Phone: +972502061183
• Email: t_dwolatzky[@]rambam.health.gov.il
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This study is designed as a prospective interventional study to evaluate the CogMe system for early detection and prevention of delirium. The study will collect physiological and cognitive measurements to evaluate the ability of the CogMe system to predict and detect delirium and to aid the development of future delirium prevention methods.

Description:

Delirium is a syndrome defined as an acute disturbance of both consciousness and cognition that tends to fluctuate over time and is caused by the physiological consequences of a medical condition. It is a common disorder in acute care settings, in internal medicine units, in post-operative patients and the intensive care unit. Delirium is associated with increased mortality, longer hospital stays, long-term cognitive impairment and increased healthcare costs. The pathophysiology of delirium is multifactorial and is not completely understood.

The prevalence of delirium increases with age and is very common in elderly hospitalized patients. In certain departments delirium rates can reach over 40%. However, delirium is underdiagnosed in almost two thirds of cases or misdiagnosed as depression or dementia. Furthermore, it has been previously shown that the diagnosis of delirium is often delayed, and that the recognition and documentation of delirium by physicians and nurses is far from optimal. Early diagnosis of delirium may improve clinical outcome, with shortened duration of symptoms, decreased length of admission and reduced long-term complications.

Clinical studies have demonstrated that delirium may be prevented in up to one-third of cases by multifactored non-pharmacological interventions, yet they can be costly to implement and require specially trained staff members. In addition, they do not usually consider physiological parameters.

Three recent technological advances now provide opportunities for a new delirium prevention approach. First, over the recent years vital signs monitoring with wearable sensors powered by advanced processing algorithms has become technically feasible. This development may provide opportunities for early detection of delirium and for detection of physiological triggers of delirium such as dehydration, infections, and lack of sleep. Second, recent advances in virtual dialogue systems (e.g. Amazon's Alexa or Apple's Siri) provide new and exciting opportunities for automatic patient interaction. Devices with voice or multimodal communication can be used by older patients with little or no experience in modern mobile technology. Lastly, recent progress in digitized data acquisition, computing infrastructure and algorithm development, now allow artificial intelligence and machine learning applications to expand into areas in medicine that were previously thought to be only the province of human experts. The combination of these three data sources can greatly improve current prediction models and allow for earlier and more accurate delirium prediction.

An automated system which could aid with delirium detection and alert clinicians to a possible onset of the syndrome can greatly improve treatment and outcomes for patients. The CogMe system utilizes current technology to provide a holistic and scalable approach for delirium prediction, detection and prevention covering both physiological and cognitive aspects. The system uses wearables for physiological vitals monitoring and communicates with patients by a dedicated tablet app - the CogMe Personal Assistant (PA). In this study, the data collected by the wearables and the CogMe PA, in combination with patient data from the EMR, will be analyzed retrospectively using machine learning techniques (CogMe Data Analytics) to evaluate the ability of the CogMe system to predict and detect delirium.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: December 31, 2024
• Est. primary completion date: June 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 65 Years and older

Eligibility

Inclusion Criteria:

• Male and female patients aged 65 years of age and older.

• Patients with an expected length of hospitalization of 4 days or longer.

• Patients who are conscious and cognitively able to provide written informed consent as suggested by a score of 0 on 4AT screening.

• Patients who have no diagnosis of delirium prior to enrollment.

Exclusion Criteria:

• Male and female patients younger than 65 years of age.

• Patients with an expected length of hospitalization of less than 4 days.

• Patients with uncorrected visual or hearing impairment.

• Patients with impaired consciousness or cognitive impairment as determined by a score of 1 or more on 4AT screening.

Related Conditions & MeSH terms

• Delirium

Intervention

Other:
• CogMe Personal Assistant (PA)
Twice a day, in the morning and evening, the electronic tablet with the CogMe PA will be given to the patient by the research assistant. Patients will be asked to respond to a short question and answer (Q&A) session of approximately 5-10 minutes duration. This intervention will continue throughout the hospitalization period, estimated at approximately 5 days.

Locations

Country	Name	City	State
Israel	Rambam Health Care Campus	Haifa	North

Sponsors (2)

Lead Sponsor	Collaborator
Rambam Health Care Campus	CogMe Ltd

Country where clinical trial is conducted

Israel, 

References & Publications (6)

Hshieh TT, Yang T, Gartaganis SL, Yue J, Inouye SK. Hospital Elder Life Program: Systematic Review and Meta-analysis of Effectiveness. Am J Geriatr Psychiatry. 2018 Oct;26(10):1015-1033. doi: 10.1016/j.jagp.2018.06.007. Epub 2018 Jun 26. — View Citation

Inouye SK, Bogardus ST Jr, Baker DI, Leo-Summers L, Cooney LM Jr. The Hospital Elder Life Program: a model of care to prevent cognitive and functional decline in older hospitalized patients. Hospital Elder Life Program. J Am Geriatr Soc. 2000 Dec;48(12):1 — View Citation

Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med. 1990 Dec 15;113(12):941-8. doi: 10.7326/0003-4819-113-12-941. — View Citation

Inouye SK. Delirium in older persons. N Engl J Med. 2006 Mar 16;354(11):1157-65. doi: 10.1056/NEJMra052321. No abstract available. Erratum In: N Engl J Med. 2006 Apr 13;354(15):1655. — View Citation

O'Keeffe ST, Lavan JN. Predicting delirium in elderly patients: development and validation of a risk-stratification model. Age Ageing. 1996 Jul;25(4):317-21. doi: 10.1093/ageing/25.4.317. — View Citation

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719-731. doi: 10.1038/s41551-018-0305-z. Epub 2018 Oct 10. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The detection of delirium by the CogMe system	Time between the detection of delirium by the CogMe Data Analytics model and the first diagnosis of delirium based on the Confusion Assessment Method (CAM) instrument.	24 hours