ECG Algorithms for CRT Response Evaluation

NCT04061434 | Completed

• 1 other identifier: OVERCOME
• Study Type: observational
• Target: 547
• Locations: 1 country
• Sites: 1

Brief Summary

Cardiovascular diseases (CVD) are associated with high healthcare costs,as well as are a leading cause of mortality and hospitalizations. One of CVDs is a heart failure which may be associated with dyssynchrony of contraction of right and left ventricle. Chance for group of patients whose pharmacotherapy is not enough is cardiac resynchronisation therapy (CRT). Effectiveness of CRT has been proven in various multicenter clinical studies. The challenge limiting CRT usage is it relative low effectiveness - with significant group of patients that do not respond to this method of therapy. The device itself does not always show the true level of stimulation during interrogation; then invalid functioning is often not detected, which presents a real danger to patient's health and life. The main challenge for today's researchers is to develop new technologies, which may help to improve diagnosis of CVD, thereby reducing healthcare costs and quality of patients' lives. Smart computed systems of ECG analysis and interpretation offer new capabilities for the diagnosis and management of patients with CRT. Several reports with intelligent machine-based learning algorithms have been published, in which achieved very positive results in detecting various ECG abnormalities. Aim of our study is to show utility of ECG interpretation software in patients with CRT to assess the CRT response using Cardiomatics system.

Conditions

ECG Monitoring

Keywords

Cardiac Resynchronization Therapy; ECG Algorithms; Machine based learning

Outcome Measures

Primary Outcomes (2)

• Number of correctly assessed ECG signals by the automatic recognition of resynchronization in CRT-mediated therapy.

  Evaluating the effectiveness of CRT therapy based on the record from an implantable device Assessment of the rationale for the use of machine based learning algorithms in detecting ECG abnormalities to determine which clinical conditions have impact on long-term effectiveness of cardiac resynchronization therapy using both standard 12-lead ECG and 24-hour Holter monitoring . The study might identify which clinical parameters in patients with CRT indicate the most benefit and the least benefit from CRT. It is planned to reach 99% sensitivity of automatized recognizing resynchronization in CRT-mediated therapy

  14 months

• Correctly recognized ECG signals after adding each cycle of 20 new ECG recordings from patients with electrical heart function disturbances.

  To achieve this goal we will collect representative base of ECG recordings containing both paced rhythm in subjects undergoing therapy and those in qualification process in order to use the software to predict CRT response. The final model assumes fully automatized diagnosis of CRT-therapy response based on machine learning. Using this feature in connection with new methods of digital signal processing will constantly increase system's efficacy measured by simultaneous achievement of high test specificity and sensitivity. Increase by 1% of test sensitivity withholding high specificity after adding each cycle of 20 new ECG recordings from patients with electrical heart function disturbances is planned.

  7 months

Secondary Outcomes (1)

• Number of registered ECG signals from patients holding a CIED.

  14 months

Study Arms (2)

Cardiac resynchronisation therapy recipients

Other cardiac implantable electronic devices recipients

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study will be consisted of two independent patient groups: 250 patients treated with cardiac resynchronisation therapy (CRT) of whom 225 ECG signal will be acquired, and 15 24-hour Holter monitoring will be collected; 250 patients with other cardiac implantable electronic devices, of whom 225 ECG signal will be acquired and 15 24-hour Holter monitoring will be collected.

You may qualify if:

• State after CRT implantation with cardiac defibrillation function (CRT-D)
• State after CRT implantation with pacing function (CRT-P)
• State after implantation of cardiac pacemaker
• State after ICD implantation with indications for periodic heart stimulation
• Signed written informed consent

You may not qualify if:

• Patient's lack of consent
• Pacemaker dependency with patient's own rhythm insufficient for appropriate perfusion of central nervous system

Sponsors & Collaborators

• Medical University of Warsaw: lead
• National Center for Research and Development, Poland: collaborator

Study Sites (1)

1st Department of Cariology of Medcial University of Warsaw

Warsaw, Masovian Voivodeship, 02-097, Poland

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Electrotherapy Department, First Department of Cardiology

Study Record Dates

• First Submitted: May 20, 2019
• First Posted: August 19, 2019
• Study Start: March 1, 2019
• Primary Completion: July 30, 2020
• Study Completion: July 30, 2020
• Last Updated: October 6, 2021

Record last verified: 2021-10

Data Sharing

• IPD Sharing: Will share

Locations

PL (1)