Prediction of Heart-Failure with Machine Learning
Predicting Heart Failure Recovery by Wearables and Machine Learning
University Medical Center Goettingen
32 participants
Apr 1, 2024
OBSERVATIONAL
Conditions
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
Inclusion Criteria3
- 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)
Exclusion Criteria3
- life expectancy under 6 months due to non-cardiac conditions
- inability to use smartwatch
- severe valvular lesions
Interventions
Patients will receive Apple Watch for Monitoring of Biosignals throughout the hospital stay
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06819618