DeliriumCogMeNCT05311761
CogMe for the Prevention and Early Detection of Delirium
A study testing CogMe Personal Assistant (PA) in people with Delirium, run by Rambam Health Care Campus at 1 site in Israel.
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… Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Delirium
- What is being tested?
- CogMe Personal Assistant (PA)
- Type of treatment
- Pill or oral medicine
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteIsrael
- Study dates
- 1 Mar 2022to 31 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Israel1 site, 1 recruiting
- Recruiting
Rambam Health Care Campus
Haifa, North, 3109601
Tzvi Dwolatzky, 502061183, t_dwolatzky@rambam.health.gov.il
Orit Meshulam, 972-47772952, o_meshulam@rambam.health.gov.il
About this study
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
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.
Read the detailed 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.
- Conditions
- Delirium
- Being tested
- CogMe Personal Assistant (PA)
- Keywords
- Elderly, Acute hospitalization, Technology
- Medical subject headings
- Delirium
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CogMe Personal Assistant (PA)Other
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.
| Study group | Role | What they receive |
|---|---|---|
| CogMe Personal Assistant (PA) | Experimental | 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. Receives: Other: CogMe Personal Assistant (PA) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 24 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Apr 2022
- Study start1 Mar 2022
- Primary completion30 Jun 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for DeliriumStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Single arm single center prospective interventional study.
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Rambam Health Care Campus
- Sponsor type
- University, hospital or other
- Collaborators
- CogMe Ltd
- Responsible party
- Principal investigator, Tzvi Dwolatzky, Rambam Health Care Campus
- Investigators
- Tzvi Dwolatzky, MD MBBCh, Principal investigator, Rambam Health Care Campus
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05311761
- Sponsor's protocol ID
- 0589-21-RMB
- Acronym
- CogMe
- Status last verified
- Sep 2023
- First submitted
- 7 Mar 2022
- Last updated
- 5 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data will be available to other researchers on request .
Publications
Inouye SK. Delirium in older persons. N Engl J Med. 2006 Mar 16;354(11):1157-65. doi: 10.1056/NEJMra052321. No abstract available.
PubMed 16540616Hshieh 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.
PubMed 30076080Yu 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.
PubMed 31015651O'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.
PubMed 8831879Inouye 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):1697-706. doi: 10.1111/j.1532-5415.2000.tb03885.x.
PubMed 11129764Inouye 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.
PubMed 2240918
Study at a glance
From the registry record, last updated by the sponsor on 5 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT05311761
- Acronym / sponsor ID
- CogMe / 0589-21-RMB
- Conditions
- Delirium
- Keywords
- Elderly, Acute hospitalization, Technology
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Rambam Health Care CampusUniversity, hospital or other
- Collaborators
- CogMe Ltd
- Study start
- 1 Mar 2022 (actual)
- Primary completion
- 30 Jun 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 5 Apr 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Single arm single center prospective interventional study.
| Arm | Type | Receives |
|---|---|---|
| CogMe Personal Assistant (PA) | Experimental | Other: CogMe Personal Assistant (PA)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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Other | CogMe Personal Assistant (PA) |
Primary outcome
The detection of delirium by the CogMe system
Time frame: 24 hours
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Sep 2023.
- Results publication: PubMed 16540616Inouye SK. Delirium in older persons. N Engl J Med. 2006 Mar 16;354(11):1157-65. doi: 10.1056/NEJMra052321. No abstract available.
- Results publication: PubMed 30076080Hshieh 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.
- Results publication: PubMed 31015651Yu 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.
- Results publication: PubMed 8831879O'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.
- Results publication: PubMed 11129764Inouye 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):1697-706. doi: 10.1111/j.1532-5415.2000.tb03885.x.
- Results publication: PubMed 2240918Inouye 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.
- Individual participant dataSharing planned: No. Data will be available to other researchers on request .
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Tzvi Dwolatzky, MD MBBCh principal investigator
Rambam Health Care Campus
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CogMe Personal Assistant (PA) in people with Delirium, run by Rambam Health Care Campus at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 5 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Tzvi Dwolatzky, MD MBBCh (+972502061183, t_dwolatzky@rambam.health.gov.il), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The detection of delirium by the CogMe system (time frame: 24 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05311761, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Sep 2023.