Machine Learning Enabled Time Series Analysis in Medicine

• Conditions: Heart Failure, Systolic; Atrial Fibrillation
• Interventions: Device: fitness tracker

• Registration Number: NCT05802563
• Lead Sponsor: HagaZiekenhuis

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-05-24
• Study First Submitted: 2023-03-27
• Study First Submitted QC: 2023-03-27
• Status Verified: 2023-04
• Study First Posted: 2023-04-06
• Last Update Submitted: 2023-04-06
• Last Update Posted: 2023-04-07
• Primary Completion: 2023-09-01
• Study Completion: 2023-09-01

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 200

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Heart Failure	fitness tracker	Study participants with systolic heart failure (Left ventricular ejection fraction \< 35%) without documented atrial fibrillation
Reference	fitness tracker	Individuals without cardiovascular disease
Atrial Fibrillation	fitness tracker	Study participants with documented atrial fibrillation without heart failure

Primary Outcome Measures

Name	Time	Method
Cardiovascular disease detection with an AI algorithm	Three months	adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure

Secondary Outcome Measures

Name	Time	Method
Detection of absence of cardiovascular disease	Three months

Trial Locations

Locations (1)

HagaZiekenhuis; 🇳🇱 Den Haag, Zuid-Holland, Netherlands