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Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence

Heart Failure

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.

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Conditions de participation

  • Sexe:

    ALL

  • Âges admissibles:

    18 and up

Critères de participation

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.

Lieu de l'étude

University Health Network
University Health Network
Toronto, Ontario
Canada

Contactez l'équipe d'étude

Primary Contact

Douglas Lee, MD, PhD

[email protected]
416-340-3861
Backup Contact

Desana Thayaparan, BSc

[email protected]
416-340-3721
Étude parrainée par
Institute for Clinical Evaluative Sciences
Participants recherchés
Plus d'informations
ID de l'étude: NCT05028686