Heart FailurePROBE AINCT05028686
Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence
An observational study of Heart Failure, run by Institute for Clinical Evaluative Sciences at 1 site in Canada.
Trial summary
This study is a prospective registry that aims to predict readmissions in patients with heart failure, using -omics, machine learning, patient reported outcomes, clinical data and other high-dimensional data sources. Read more about this study
- Who is it for?
- Ages 18 to 105 years
- Conditions the study is for
- Heart Failure
- How many people
- 500 participants
- Where
- 1 siteCanada
- Study dates
- 1 Feb 2019to 30 Sep 2029
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 to 105 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Hospitalized heart failure patients
Who may be able to join
- Any patient aged 18 years or older admitted to hospital or seen in the emergency department with heart failure defined clinically
- The diagnosis will be guided by the Framingham criteria for HF and/or BNP. A BNP >400 will be defined as definite heart failure and BNP 100-400 classified as possible heart failure.
- Provides informed consent
Who cannot join
- Patients who cannot communicate due to dementia or severe cognitive deficits
- non-Ontario residents
- nursing home residents
- those who are not discharged home but are discharged to a skilled nursing facility (long-term care or chronic institution)
- those who are unable to communicate who do not have a proxy (e.g. spouse or close family member) to facilitate communication with the patient.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Canada1 site, 1 recruiting
- Recruiting
University Health Network
Toronto, Ontario
Douglas Lee, MD, PhD, 416-340-3861, dlee@ices.on.ca
Desana Thayaparan, BSc, 416-340-3721, desana.thayaparan@uhn.ca
About this study
This study is a prospective registry that aims to predict readmissions in patients with heart failure, using -omics, machine learning, patient reported outcomes, clinical data and other high-dimensional data sources.
Read the detailed description
There is substantial need to better predict outcomes across the spectrum of heart failure (HF) phenotypes in order to provide more efficient care with greater precision. Specifically, no validated methods have been adopted to predict outcomes reflecting transitions in health status across the continuum of HF and changes in cardiac function. A key transition is hospitalization - either readmission or de novo cardiovascular hospital admission. This is a major unmet health care need, to be able to better predict who will require hospital admission.
Novel contributions of biomarkers, -omics, remote patient monitoring, and artificial intelligence (AI). It is anticipated that prediction of readmission and many other outcomes will be further improved by measurement of circulating biomarkers and by incorporating methods from AI including machine learning and probabilistic generative models that can incorporate the lens of how physicians and patients think. Machine learning that incorporates many different types of data, including physician interpretation and a broad array of biomarker/-omics molecular information can lead to significant improvements in predictive accuracy. Novel multimarker strategies coupled with machine learning may enable the ability of physicians to predict a range of outcomes (e.g., transitions in HF health status and LVEF) and refine clinical prediction models. Furthermore, the investigators will collect patient data, including patient reported outcome measures (PROMs), and physiological data (e.g. heart rate, blood pressure, and daily weights data) and integrate these data points into predictive models. The investigators will use the PROMs obtainable using Medly as a predictor of hospitalization, and as an outcome. In this proposal, the investigators will take advantage of recent advances in both deep and high throughput proteomics technologies to perform high-resolution analyses. These novel factors can be integrated into new electronic algorithms to improve HF care in the population.
- Conditions
- Heart Failure
- Being tested
- No intervention
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
Observational cohort
| Study group | Role | What they receive |
|---|---|---|
| Hospitalized heart failure cohort | Patients hospitalized with heart failure Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cardiovascular readmission
Non-elective readmission to hospital for a cardiovascular cause
Measured: 30 day
Heart failure readmission
Non-elective readmission to hospital for heart failure
Measured: 30 day
Secondary outcomes (3)
Mortality
All-cause death
Measured: 30-day
Cardiovascular death
Death from cardiovascular causes
Measured: 30-day
All-cause readmission
Non-elective readmission to hospital for a any reason
Measured: 30-day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2021
- Study start1 Feb 2019
- Primary completion30 Sep 2024 (estimated)
- Study completion30 Sep 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart FailureStudies 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
- Prospective
- Enrolment
- 500 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Institute for Clinical Evaluative Sciences
- Sponsor type
- University, hospital or other
- Collaborators
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, Vector Institute for Artificial Intelligence
- Responsible party
- Principal investigator, Douglas Lee, Institute for Clinical Evaluative Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05028686
- Sponsor's protocol ID
- 4
- Acronym
- PROBE AI
- Status last verified
- Aug 2021
- First submitted
- 25 Aug 2021
- Last updated
- 2 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05028686
- Acronym / sponsor ID
- PROBE AI / 4
- Conditions
- Heart Failure
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Institute for Clinical Evaluative SciencesUniversity, hospital or other
- Collaborators
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, Vector Institute for Artificial Intelligence
- Study start
- 1 Feb 2019 (actual)
- Primary completion
- 30 Sep 2024 (estimated)
- Study completion
- 30 Sep 2029 (estimated)
- First posted
- 31 Aug 2021 (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 a prospective registry that aims to predict readmissions in patients with heart failure, using -omics, machine learning, patient reported outcomes, clinical data and other high-dimensional data sources.
Detailed description
There is substantial need to better predict outcomes across the spectrum of heart failure (HF) phenotypes in order to provide more efficient care with greater precision. Specifically, no validated methods have been adopted to predict outcomes reflecting transitions in health status across the continuum of HF and changes in cardiac function. A key transition is hospitalization - either readmission or de novo cardiovascular hospital admission. This is a major unmet health care need, to be able to better predict who will require hospital admission.
Novel contributions of biomarkers, -omics, remote patient monitoring, and artificial intelligence (AI). It is anticipated that prediction of readmission and many other outcomes will be further improved by measurement of circulating biomarkers and by incorporating methods from AI including machine learning and probabilistic generative models that can incorporate the lens of how physicians and patients think. Machine learning that incorporates many different types of data, including physician interpretation and a broad array of biomarker/-omics molecular information can lead to significant improvements in predictive accuracy. Novel multimarker strategies coupled with machine learning may enable the ability of physicians to predict a range of outcomes (e.g., transitions in HF health status and LVEF) and refine clinical prediction models. Furthermore, the investigators will collect patient data, including patient reported outcome measures (PROMs), and physiological data (e.g. heart rate, blood pressure, and daily weights data) and integrate these data points into predictive models. The investigators will use the PROMs obtainable using Medly as a predictor of hospitalization, and as an outcome. In this proposal, the investigators will take advantage of recent advances in both deep and high throughput proteomics technologies to perform high-resolution analyses. These novel factors can be integrated into new electronic algorithms to improve HF care in the population.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Hospitalized heart failure cohort | – | Other: No interventionPatients hospitalized with heart failure |
Who may qualify
- Ages
- 18 to 105 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Hospitalized heart failure patients
Key inclusion criteria (3 listed)
- Any patient aged 18 years or older admitted to hospital or seen in the emergency department with heart failure defined clinically
- The diagnosis will be guided by the Framingham criteria for HF and/or BNP. A BNP >400 will be defined as definite heart failure and BNP 100-400 classified as possible heart failure.
- Provides informed consent
Key exclusion criteria (5 listed)
- Patients who cannot communicate due to dementia or severe cognitive deficits
- non-Ontario residents
- nursing home residents
- those who are not discharged home but are discharged to a skilled nursing facility (long-term care or chronic institution)
- those who are unable to communicate who do not have a proxy (e.g. spouse or close family member) to facilitate communication with the patient.
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 |
|---|---|---|
| No interventionObservational cohort | Other | Hospitalized heart failure cohort |
Primary outcome
Cardiovascular readmission
Time frame: 30 day
Non-elective readmission to hospital for a cardiovascular cause
Heart failure readmission
Time frame: 30 day
Non-elective readmission to hospital for heart failure
3 secondary outcomes
Mortality
Time frame: 30-day
Cardiovascular death
Time frame: 30-day
All-cause readmission
Time frame: 30-day
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2021.
- Individual participant dataSharing planned: No
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure, run by Institute for Clinical Evaluative Sciences at 1 site in Canada.
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 5 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: looking for participants now, as of the sponsor's last update on 2 Sep 2021. 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 Canada. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Douglas S Lee, MD, PhD (4163403861, dlee@ices.on.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cardiovascular readmission (time frame: 30 day). The study lists 2 primary outcomes in all. 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 2 Sep 2021.