Evaluation of Home-based Sensor System to Detect Health Decompensation in Elderly Patients With History of CHF
Feasibility of Home-based, Ambient Passive Sensor Technology to Provide Early Warning of Health Decompensation by Detecting Deviations in Activities of Daily Living (ADLs) of Elderly Subjects With Diagnosed Chronic Heart Failure
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Christine Fernandez
- Phone Number: (973) 786-3573
- Email: christine@sensorum.ai
Study Contact Backup
- Name: Andrew Hotchkiss
- Phone Number: (973) 946-8382
- Email: andrew@sensorum.ai
Study Locations
-
-
New York
-
New York, New York, United States, 10021
- Recruiting
- Weill Cornell Medicine
-
Contact:
- Kate Zarzuela
- Phone Number: 646-962-5909
- Email: kaz4004@med.cornell.edu
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Current Patient at Weill Cornell Medicine
- Aged 55 years or older
- Able to consent
- Documented diagnosis of congestive heart failure (CHF)
- At least 1 of the following prior hospital utilization events in the past 12 months
- Inpatient admission for any reason
- Facility observation stay for any reason
- Emergency Department visit for any reason
Exclusion Criteria:
- Significant cardiac valvular disease
- End-Stage Renal Disease (ESRD)
- End-Stage CHF
- End-Stage COPD
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Passive monitoring
No intervention
|
Data collection of clinically relevant signals using home-based sensor system
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Recall of AI in passive sensor system
Time Frame: 6 months
|
Evaluation of AI ability to prospectively detect hospital utilization event
|
6 months
|
|
Precision of AI in passive sensor system
Time Frame: 6 months
|
Evaluation of AI ability to precisely predict hospital utilization event
|
6 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Recall of sensor data review by trained nurses
Time Frame: 6 months
|
Evaluation of nurse ability to prospectively detect hospital utilization event
|
6 months
|
|
Precision of sensor data review by trained nurses
Time Frame: 6 months
|
Evaluation of nurse ability to precisely predict hospital utilization event
|
6 months
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Parag Goyal, M.D., MSc, Weill Medical College of Cornell University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Estimated)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- Sensorum P-002
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
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