Predicting hospital readmissions for heart failure patients using advanced data techniques
Predicting Readmissions Using Omics, Biostatistical Evaluate and Artificial Intelligence
This study is trying to see if using advanced data techniques can help predict which heart failure patients are likely to be readmitted to the hospital, so they can get better care.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 500 (estimated) |
| Ages | 18 Years to 105 Years |
| Sex | All |
| Sponsor | Institute for Clinical Evaluative Sciences Academic / other |
| Locations | 1 site (Toronto, Ontario) |
| Trial ID | NCT05028686 on ClinicalTrials.gov |
What this trial studies
This observational study aims to create a prospective registry to predict hospital readmissions in patients with heart failure by utilizing -omics, machine learning, patient-reported outcomes, and clinical data. The study addresses the critical need for improved prediction methods for health transitions in heart failure, particularly focusing on hospital admissions. By integrating various high-dimensional data sources, including biomarkers and artificial intelligence, the research seeks to enhance the accuracy of predicting patient outcomes and refine clinical prediction models. The ultimate goal is to provide more efficient and precise care for heart failure patients.
Who should consider this trial
Good fit: Ideal candidates for this study are adults aged 18 years or older who have been admitted to the hospital or seen in the emergency department with clinically defined heart failure.
Not a fit: Patients who cannot communicate due to cognitive deficits or those residing in nursing homes may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could significantly improve the ability to predict hospital readmissions for heart failure patients, leading to better management and care.
How similar studies have performed: Other studies have shown promise in using similar approaches with -omics and machine learning to predict health outcomes, indicating potential for success in this novel application.
Eligibility criteria
Show full inclusion / exclusion 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.
Where this trial is running
Toronto, Ontario
- University Health Network — Toronto, Ontario, Canada (Recruiting)
Study contacts
- Study coordinator: Douglas S Lee, MD, PhD
- Email: dlee@ices.on.ca
- Phone: 4163403861
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.