Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence

NCT ID: NCT05028686

Last Updated: 2021-09-02

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-02-01
• Study Completion Date: 2029-09-30

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.

Conditions

Heart Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Hospitalized heart failure cohort

Patients hospitalized with heart failure

No intervention

Intervention Type: OTHER

Observational cohort

Interventions

No intervention

Observational cohort

Intervention Type: OTHER

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 105 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Ted Rogers Centre for Heart Research

UNKNOWN

Sponsor Role: collaborator

Peter Munk Cardiac Centre

UNKNOWN

Sponsor Role: collaborator

Vector Institute for Artificial Intelligence

UNKNOWN

Sponsor Role: collaborator

Institute for Clinical Evaluative Sciences

OTHER

Sponsor Role: lead

Responsible Party

Douglas Lee

Senior Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University Health Network

Toronto, Ontario, Canada

Site Status: RECRUITING

Countries

Canada

Central Contacts

Douglas S Lee, MD, PhD

Role: CONTACT

dlee@ices.on.ca

4163403861

Suzanne Perrett

Role: CONTACT

suzanne.perrett@ices.on.ca

4164804055

Facility Contacts

Douglas Lee, MD, PhD

Role: primary

dlee@ices.on.ca

416-340-3861

Desana Thayaparan, BSc

Role: backup

desana.thayaparan@uhn.ca

416-340-3721

Other Identifiers

4

Identifier Type: -

Identifier Source: org_study_id