Predicting Heart Failure Recovery by Wearables and Machine Learning

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Observational
SUMMARY

In this monocentric observational study the research question is to what extent data collected via Apple Watch can predict the heart failure status of decompensated HF patients. For this purpose, physiological data from the Apple Watch (such as single-lead electrocardiogram, SpO2, respiratory rate, step count, nighttime temperature, etc.) will be extracted and used as predictor variables to forecast outcomes like risk of decompensation and rehospitalization within the follow-up period. Since this is a data-driven study, additional data collected as part of guideline-compliant treatment will also be included.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:

• age over 17

• HFrEF with LV-EF under 41

• hospitalized for decompensated heart failure with a) nTproBNP over 1000 AND b) willing to participate AND c) at least one out of three clinical signs (edema, pleural effusion, ascites)

Locations
Other Locations
Germany
University Medical Center Goettingen
RECRUITING
Goettigen
Contact Information
Primary
Soeren Sievers, Dr. med.
soeren.sievers@med.uni-goettingen.de
00495513965044
Time Frame
Start Date: 2024-04-01
Estimated Completion Date: 2025-05-31
Participants
Target number of participants: 32
Treatments
Study Cohort
Related Therapeutic Areas
Sponsors
Leads: University Medical Center Goettingen

This content was sourced from clinicaltrials.gov