Atrial Fibrillation, Heart Failure, Systolic
Conditions
Keywords
cardiovascular disease, Artificial intelligence, machine learning
Brief summary
The goal of this observational cohort study is to investigate the potential of fitness trackers in combination with machine learning algorithms to identify cardiovascular disease specific patterns. Two hundred participants will be enrolled: 1. 50 with heart failure 2. 50 with atrial fibrillation 3. 100 (healthy) individuals without the former two conditions All participants are given a Fitbit device and monitored for three months. Researchers will compare differences in heart rate variability patterns between the groups and devise a machine learning algorithm to detect these patterns automatically.
Interventions
DEVICEfitness tracker
Study subjects will wear a Fitbit fitness tracker
Sponsors
Delft University of Technology
HagaZiekenhuis
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
Yes
Inclusion criteria
* systolic heart failure (LVEF \< 35%) * Atrial fibrillation without heart failure * Individuals without cardiovascular disease
Exclusion criteria
* \> 85 years old * Recent pulmonary venous antrum isolation procedure (\<1 year) * (end stage) kidney failure * (end stage) liver failure * Study participants with known systemic active inflammatory disease * Study participants with impaired mental state * Inability to use a fitness tracker or mobile phone * Impaired cognition and inability to understand the study protocol
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Cardiovascular disease detection with an AI algorithm | Three months | adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure |
Secondary
| Measure | Time frame |
|---|---|
| Detection of absence of cardiovascular disease | Three months |
Countries
Netherlands
Outcome results
None listed