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Recruiting NCT06819618

Prediction of Heart-Failure with Machine Learning

Trial Parameters

Condition Heart Failure with Reduced Ejection Fraction
Sponsor University Medical Center Goettingen
Study Type OBSERVATIONAL
Phase N/A
Enrollment 32
Sex ALL
Min Age 18 Years
Max Age N/A
Start Date 2024-04-01
Completion 2025-05-31
Interventions
Monitoring with Apple Watch

Brief 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 Criteria

Inclusion Criteria: * 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 Criteria: * life expectancy under 6 months due to non-cardiac conditions * inability to use smartwatch * severe valvular lesions

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