Clinical trial NCT05028686: Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence
Official title: Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Heart Failure
Lead sponsor: Institute for Clinical Evaluative Sciences
Sponsor class: OTHER
Collaborators: name: Ted Rogers Centre for Heart Research
name: Peter Munk Cardiac Centre
name: Vector Institute for Artificial Intelligence
First posted: 2021-08-31
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 105
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Hospitalized heart failure patients

Brief 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.

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.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Cardiovascular readmission
timeFrame: 30 day
measure: Heart failure readmission
timeFrame: 30 day
Secondary outcomes:
measure: Mortality
timeFrame: 30-day
measure: Cardiovascular death
timeFrame: 30-day
measure: All-cause readmission
timeFrame: 30-day
Other outcomes:
Locations:
city: Toronto
country: Canada
facility: University Health Network
state: Ontario
Study officials:
References: