Actively Recruiting
Prediction of Heart-Failure with Machine Learning
Led by University Medical Center Goettingen · Updated on 2025-02-11
32
Participants Needed
1
Research Sites
60 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Prediction of Heart-Failure with Machine Learning
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age over 17 years
- Diagnosed with heart failure with reduced ejection fraction (LV-EF under 41%)
- Hospitalized for decompensated heart failure with nTproBNP over 1000
- Willing to participate in the study
- Presence of at least one clinical sign: edema, pleural effusion, or ascites
You will not qualify if you...
- Life expectancy under 6 months due to non-cardiac conditions
- Unable to use a smartwatch
- Severe valvular heart lesions
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
University Medical Center Goettingen
Goettigen, Lower Saxony, Germany, 37075
Actively Recruiting
Research Team
S
Soeren Sievers, Dr. med.
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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