Home Outpatient Monitoring and Engagement to Predict HF Exacerbation
Status: | Enrolling by invitation |
---|---|
Conditions: | Cardiology |
Therapuetic Areas: | Cardiology / Vascular Diseases |
Healthy: | No |
Age Range: | 18 - Any |
Updated: | 10/18/2018 |
Start Date: | October 8, 2018 |
End Date: | March 2019 |
Home Outpatient Monitoring and Engagement to Predict HF Exacerbation (HOME PREDICT-HF)
The HOME PREDICT HF study looks at new ways to predict hospitalizations for heart failure. We
will use a set of devices at home and surveys to collect information about patient's health.
This study uses the Eureka app, a new study app developed by the University of California,
San Francisco. The study is designed to happen remotely, using this application on a
patient's smartphone, so that is as convenient as possible to participate.
will use a set of devices at home and surveys to collect information about patient's health.
This study uses the Eureka app, a new study app developed by the University of California,
San Francisco. The study is designed to happen remotely, using this application on a
patient's smartphone, so that is as convenient as possible to participate.
HOME PREDICT HF is single center, prospective, unblinded, randomly assigned training and
validation observational cohorts to develop machine learning algorithms from an in-home suite
of sensors in order to predict 3-month heart failure hospitalization and/or emergency
department visits. Study population includes adults presenting with a diagnosis of reduced
ejection fraction (LVEF <= 40%), NYHA class II-IV) who have had a hospitalization for HF in
the previous 6 months. The study objectives include (1) To collect observational data from
multiple sensors, patient-reported outcomes, and medical record data to develop (train)
machine-learning algorithms (2) To validate trained algorithms in a separate validation
cohort (3) To collect data to inform the design of a future intervention study. The primary
outcome is Ninety-day heart failure hospitalization/emergency department visit for heart
failure.
validation observational cohorts to develop machine learning algorithms from an in-home suite
of sensors in order to predict 3-month heart failure hospitalization and/or emergency
department visits. Study population includes adults presenting with a diagnosis of reduced
ejection fraction (LVEF <= 40%), NYHA class II-IV) who have had a hospitalization for HF in
the previous 6 months. The study objectives include (1) To collect observational data from
multiple sensors, patient-reported outcomes, and medical record data to develop (train)
machine-learning algorithms (2) To validate trained algorithms in a separate validation
cohort (3) To collect data to inform the design of a future intervention study. The primary
outcome is Ninety-day heart failure hospitalization/emergency department visit for heart
failure.
Inclusion Criteria:
1. Outpatient and cared for by a PINNACLE Registry practice
2. Age ≥ 18 years old
3. Has a diagnosis of HF in the PINNACLE Registry/Medical Record
4. Seen by PINNACLE practice within the last 12 months
5. Has an LVEF ≤40% on their last data entry (within 1 year) in the PINNACLE Registry
6. NYHA Class II-IV by self-report
7. Has had a hospitalization for HF in the previous 6 months by self-report
8. Owns an Android or iOS smartphone, within Verizon cellular coverage zone in order to
allow for data submission.
9. Sleep in the same bed at least 5 days per week
10. Willingness to complete the required surveys, measurements and study activities.
Exclusion Criteria:
1. Home oxygen use
2. Current or planned ventricular assist device
3. Previously or currently on a heart transplant list
4. Chronic dialysis
5. A diagnosis of any cancer and undergoing active treatment
6. In hospice or palliative care
7. Planned surgery/procedure in the next 3 months
8. Planned extended time away from home (>2 weeks) in the next 3 months
9. Living in a skilled nursing facility or other chronic care facility
10. Pregnancy or planned pregnancy in the next 3 months
11. Inability or unwillingness to consent and/or follow requirements of the study
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