An interventional study of CogMe Personal Assistant (PA) in Delirium, sponsored by Rambam Health Care Campus. Status unknown at 1 site in Israel. Open to participants aged 65 Years and older. Per ClinicalTrials.gov, last updated 2023-09-05.
Sponsored by Rambam Health Care Campus · Not applicable, Interventional, and Diagnostic
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.
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.
1,071 studies on the registry are indexed under Delirium; 239 are open to participants now.
This study's planned enrollment of 100 is below the median of 119 across 608 interventional studies indexed under Delirium.
Browse Delirium studies →Rambam Health Care Campus is the lead sponsor of 466 studies on the registry; 34 are open to participants now.
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Exclusion Criteria:
The CogMe PA is a dedicated application built by CogMe with the purpose of assessing the cognitive functions of patients and providing them with a short and stimulating interaction. The application runs on a standard tablet. The CogMe PA is designed to be easily understandable and usable also for older adults with little or no experience in mobile applications. The questions in the Q\&A session are based on validated cognitive tests shown to be associated with delirium and are built to assess the subjective wellbeing and cognitive function of the patients. The repeated use of the application will allow to detect any changes or anomalies during the hospitalization period.
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.
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.
Time frame: 24 hours
Plan to share: No — Data will be available to other researchers on request .
Started Mar 2022 (actual). Primary completion is estimated for Jun 2024, 27 months after the start.
Of 248 non-industry interventional studies with no phase under Delirium, half reached primary completion within 27 months of starting (the median), counting those still running; a quarter within 12 and three quarters within 45. This study's planned 27 months is close to that.
Counting every sponsor, half of 250 studies reached it within 27 months; a quarter within 12 and three quarters within 45.
How these figures are worked out, and which studies are left out of them.
This study is status unknown, as verified in Sep 2023. You cannot join it, but the record below documents what was studied.
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