Frail Elderly SyndromeNCT06013709
A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.
An observational study of Frail Elderly Syndrome, run by Presage.
Trial summary
Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85). Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Frail Elderly Syndrome
- What is being tested?
- PRESAE CARE
- Type of treatment
- Medical device
- How many people
- 1,478 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2016to 1 Dec 2022
- Phase
- Observational
- 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
Study population: Frail older adults receiving the help of a home care aide using PRESAGE CARE device in France.
Who may be able to join
- frail older adults aged 65 years old and over
- Receive the help of a home care aide using PRESAGE CARE
- All eligible persons were invited to participate and were included if they provided consent
Who cannot join
None listed.
About this study
Official title: A Machine Learning Algorithm to Predict Health Clinical Situations (Fall, Undernutrition Risk, Depression Risk, Heart Failure Risk) and Improv Decision-support Tools in Primary Healthcare for Older Adults Living at Home.
Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85). Data recorded by home aides were send in real time to a secure server to be analyzed by our machine learning algorithm, which predicted risk level and displayed it on a secure web-based medical device. This study aims to implement and to evaluate the sensitivity and specificity's predictions of Presage system for four clinical situations with a high impact on unscheduled hospitalization of older adults living at home: falls, risk of depression (is sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg).
Methods This is a retrospective observational multicenter study. To gain insight on both short-and middle-term predictions and how the risk factors evolve through different periods of observation, we developed a series of models which predict the risk of future clinical symptoms.
Read the detailed description
This is a retrospective observational multicenter study. This study was conducted on two distinct cohorts.
Data between January 2020 - February 2023 from 50 home care facilities using PRESAEGE CARE medical device on a daily basis were analyzed. 740 853 data from 27 439 visits by home aides for 1 478 patients. The patients' mean age was 84,89 years (SD = 8.9 years) with a moderate dependency level and the sample included 1 038 women (70%).
PRESAGE CARE is a medical device CE marked to predict emergency hospitalizations. This e-health system is based on a questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings.
Based on these data, some others risks are evaluated and predict by the artificial intelligence algorithm.
This study aims to evaluate the sensitivity and specificity's predictions of PRESAGE CARE system for four clinical situations with a high impact on unscheduled hospitalization of olders adults living at home: falls, risk of depression (is sadder), risk if (eat less well) and risk of heart failure (swollen leg).
The principal objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events (when events no appear in the observation window).
Secondary objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events (when events appear in the observation window).
- Conditions
- Frail Elderly Syndrome
- Being tested
- PRESAE CARE
- Keywords
- frailty, older adults, artificial intelligence, falls, undernutrition, depression, heart failure risk
- Medical subject headings
- Frailty, Malnutrition, Depression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PRESAE CAREDevice
PRESAGE CARE is a medical device CE marked based on artificial intelligence to prevent and reduce emergency department visits and unplanned hospitalization among frail older adults living at home.
These device is based on the use of a short questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the home care aides (HAs)or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 27 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level on emergency hospitalization risk and some health clinical situations and displayed it on a web-based secure medical device.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events ((when events no appear in the observation window).
To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.
Measured: between one to six weeks
Secondary outcomes (1)
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events ((when events no appear in the observation window).
To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.
Measured: between one to six weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2023
- Study start1 Apr 2016
- Primary completion1 Apr 2016
- Study completion1 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Frail Elderly SyndromeStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,478 participants (actual)
Sponsor and investigators
- Lead sponsor
- Presage
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Assistance Publique - Hôpitaux de Paris, Assistance Publique Hopitaux De Marseille
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06013709
- Sponsor's protocol ID
- PRESAGE
- Status last verified
- Aug 2023
- First submitted
- 21 Aug 2023
- Last updated
- 28 Aug 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning Algorithm to Predict Health Clinical Situations (Fall, Undernutrition Risk, Depression Risk, Heart Failure Risk) and Improv Decision-support Tools in Primary Healthcare for Older Adults Living at Home.
- ClinicalTrials.gov ID
- NCT06013709
- Acronym / sponsor ID
- PRESAGE
- Conditions
- Frail Elderly Syndrome
- Keywords
- frailty, older adults, artificial intelligence, falls, undernutrition, depression, heart failure risk
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,478 participants (actual)
- Lead sponsor
- PresageIndustry (pharma, biotech, devices)
- Collaborators
- Assistance Publique - Hôpitaux de Paris, Assistance Publique Hopitaux De Marseille
- Study start
- 1 Apr 2016 (actual)
- Primary completion
- 1 Apr 2016 (actual)
- Study completion
- 1 Dec 2022 (actual)
- First posted
- 28 Aug 2023 (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.
Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85). Data recorded by home aides were send in real time to a secure server to be analyzed by our machine learning algorithm, which predicted risk level and displayed it on a secure web-based medical device. This study aims to implement and to evaluate the sensitivity and specificity's predictions of Presage system for four clinical situations with a high impact on unscheduled hospitalization of older adults living at home: falls, risk of depression (is sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg).
Methods This is a retrospective observational multicenter study. To gain insight on both short-and middle-term predictions and how the risk factors evolve through different periods of observation, we developed a series of models which predict the risk of future clinical symptoms.
Detailed description
This is a retrospective observational multicenter study. This study was conducted on two distinct cohorts.
Data between January 2020 - February 2023 from 50 home care facilities using PRESAEGE CARE medical device on a daily basis were analyzed. 740 853 data from 27 439 visits by home aides for 1 478 patients. The patients' mean age was 84,89 years (SD = 8.9 years) with a moderate dependency level and the sample included 1 038 women (70%).
PRESAGE CARE is a medical device CE marked to predict emergency hospitalizations. This e-health system is based on a questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings.
Based on these data, some others risks are evaluated and predict by the artificial intelligence algorithm.
This study aims to evaluate the sensitivity and specificity's predictions of PRESAGE CARE system for four clinical situations with a high impact on unscheduled hospitalization of olders adults living at home: falls, risk of depression (is sadder), risk if (eat less well) and risk of heart failure (swollen leg).
The principal objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events (when events no appear in the observation window).
Secondary objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events (when events appear in the observation window).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- Study population
- Frail older adults receiving the help of a home care aide using PRESAGE CARE device in France.
Key inclusion criteria (3 listed)
- frail older adults aged 65 years old and over
- Receive the help of a home care aide using PRESAGE CARE
- All eligible persons were invited to participate and were included if they provided consent
Key exclusion criteria (0 listed)
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 |
|---|---|---|
| PRESAE CAREPRESAGE CARE is a medical device CE marked based on artificial intelligence to prevent and reduce emergency department visits and unplanned hospitalization among frail older adults living at home. These device is based on the use of a short questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the home care aides (HAs)or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 27 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level on emergency hospitalization risk and some health clinical situations and displayed it on a web-based secure medical device. | Device | – |
Primary outcome
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events ((when events no appear in the observation window).
Time frame: between one to six weeks
To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.
1 secondary outcome
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events ((when events no appear in the observation window).
Time frame: between one to six weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Aug 2023.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Frail Elderly Syndrome, run by Presage.
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 3 inclusion and 0 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: finished, as of the sponsor's last update on 28 Aug 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events ((when events no appear in the observation window). (time frame: between one to six weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 28 Aug 2023.