Machine Learning Enabled Time Series Analysis in Medicine

NCT05802563 | Unknown

• 1 other identifier: metime
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

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. 1.50 with heart failure
2. 2.50 with atrial fibrillation
3. 3.100 (healthy) individuals without the former two conditions

Conditions

Atrial Fibrillation; Heart Failure, Systolic

Keywords

cardiovascular disease; Artificial intelligence; machine learning

Outcome Measures

Primary Outcomes (1)

• Cardiovascular disease detection with an AI algorithm

  adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure

  Three months

Secondary Outcomes (1)

• Detection of absence of cardiovascular disease

  Three months

Study Arms (3)

Heart Failure

Study participants with systolic heart failure (Left ventricular ejection fraction \< 35%) without documented atrial fibrillation

Device: fitness tracker

Atrial Fibrillation

Study participants with documented atrial fibrillation without heart failure

Device: fitness tracker

Reference

Individuals without cardiovascular disease

Device: fitness tracker

Interventions

fitness tracker DEVICE

Study subjects will wear a Fitbit fitness tracker

Also known as: Fitbit Charge 5, Fitbit Inspire 2

Atrial Fibrillation; Heart Failure; Reference

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All included participants will receive an electrocardiogram.

You may qualify if:

• systolic heart failure (LVEF \< 35%)
• Atrial fibrillation without heart failure
• Individuals without cardiovascular disease

You may not qualify if:

• \> 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

Sponsors & Collaborators

• HagaZiekenhuis: lead
• Delft University of Technology: collaborator

Study Sites (1)

HagaZiekenhuis

The Hague, South Holland, 2545 AA, Netherlands

Location

MeSH Terms

Conditions

Atrial Fibrillation; Heart Failure, Systolic; Cardiovascular Diseases

Interventions

Fitness Trackers

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Heart Failure

Intervention Hierarchy (Ancestors)

Diagnostic Equipment; Equipment and Supplies; Wearable Electronic Devices; Electrical Equipment and Supplies

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 27, 2023
• First Posted: April 6, 2023
• Study Start: May 24, 2022
• Primary Completion: September 1, 2023
• Study Completion: September 1, 2023
• Last Updated: April 7, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ANALYTIC CODE
• Time Frame: Expected release in 2026
• Access Criteria: * Research institute * Open Access (full disclosure of results) * Clear data analysis plan

Locations

NL (1)