Prediction of Outcome by Echocardiography in Left Bundle Branch Block (EchoLBBB)

Prediction of Heart-failure and Mortality by Echocardiographic Parameters and Machine Learning in Individuals With Left Bundle Branch Block

Patients with left bundle branch block have an increased risk for the development of heart-failure and death. However, risk factors for unfavorable outcomes are still poorly defined. This study aims to identify echocardiographic parameters and ECG characteristics by machine learning in order to develop individual risk assessment

Study Overview

• Status: Recruiting
• Conditions: Left Bundle-Branch Block

Detailed Description

The project investigates patients with left bundle branch block (LBBB) which describes a specific block in the electrical conduction system, where the electrical impulses must follow a detour, with the result that different parts of the heart-muscle do not contract at the same time. This condition is called left ventricular dyssynchrony. LBBB can be found in people who are otherwise completely healthy and need not have any practical consequences. In others LBBB is present in patients with different heart diseases such as after myocardial infarctions or other diseases involving the heart-muscle. Patients with implanted pacemakers have a similar failure in the conduction system. Both conditions can increase the risk for development of heart-failure and cardiovascular death. Dyssynchrony can be treated with a special pacemaker (cardiac resynchronisation therapy, CRT) in addition to regular medical treatment. The therapy is well established and has shown to reduce morbidity and mortality and even reverse heart-failure in some patients completely. However, the patients in need and responding to CRT treatment is still not optimally defined. New echocardiographic parameters based on strain imaging such as regional myocardial work are able quantify the degree of dyssynchrony and give new insights into the interplay of activation delay through the LBBB and loading conditions and weakness of the myocardium due to other diseases. These new and complex measures can be integrated with clinical information by machine learning (ML) as a promising tools for accurate patient selection for CRT. The project aims to find markers on ultrasound improved by ML based selection to distinguish those patients who have problems associated with the branch block from those who remain stable. This will facilitate both, an optimized patient selection for CRT treatment and follow-up schedule for those who have a stable condition.

• Study Type: Observational
• Enrollment (Anticipated): 2000

Contacts and Locations

• Study Contact: Name: Assami Rösner, MD,PhD; Phone Number: 04795990071; Email: assami.rosner@unn.no

Study Locations

• Norway
  • Troms
    • Tromsø, Troms, Norway, 9038
      • Recruiting
      • University Hospital North Norway
      • Contact: Assami Rösner, PhD; Phone Number: +4795990071; Email: assami.rosner@unn.no

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients will be recruited based on epidemiological studies from Tromsø, where LBBB or ventricular pacing has been identified. Further in-hospital patients and patients from the out-patient clinics will be recruited due to ECG assessment

Description

Inclusion Criteria:

• QRS complex >130 ms and R-wave duration in
• V6 >70 ms
• ventricular pacing>50%
• Previously implanted cardiac resynchronisation therapy (CRT)

Exclusion Criteria:

• Typical right bundle branch block.
• No ability to give informed consent,
• non-cardiovascular co-mobidities with reduced life-expectancy < 1 year
• patients with complex congenital heart disease.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cardiovascular death	Timepoint (day) of death and its cause	15 years
Death of any cause	Timepoint (day) of death and its cause	15 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Hospital admission due to heart-failure	Time point of hospital admission and main-diagnosis	15 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Remodelling	Increase or decrease of ventricular volume in ml	5 years
Cardiac function	Increase or decrease of ejection fraction in %	5 years
Heart failure	Increase or decrease of heart failure by proBNP and NYHA class	5 years

Collaborators and Investigators

• Sponsor: University Hospital of North Norway
• Collaborators: Oslo University Hospital; University of Bergen; Norwegian University of Science and Technology; University of Tromso; KU Leuven
• Investigators: Principal Investigator: Assami Rösner, MD,PhD, University Hospital North Norway

Study record dates

Study Major Dates

• Study Start (Actual): April 15, 2021
• Primary Completion (Anticipated): December 31, 2027
• Study Completion (Anticipated): December 31, 2036

Study Registration Dates

• First Submitted: March 1, 2020
• First Submitted That Met QC Criteria: March 1, 2020
• First Posted (Actual): March 3, 2020

Study Record Updates

• Last Update Posted (Actual): May 24, 2022
• Last Update Submitted That Met QC Criteria: May 18, 2022
• Last Verified: May 1, 2022

More Information

Terms related to this study

• Keywords: Left bundle branch block; strain-imaging; myocardial work assessment; machine learning
• Additional Relevant MeSH Terms: Pathologic Processes; Heart Diseases; Cardiovascular Diseases; Arrhythmias, Cardiac; Cardiac Conduction System Disease; Bundle-Branch Block; Heart Block
• Other Study ID Numbers: REK2019/134

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Yes
• IPD Plan Description: All individual analytic codes for participants fom other Norwegian Hospitals need to be transferred to University Hospital North Norway (UNN) for registering outcome follow-up
• IPD Sharing Time Frame: 15 years
• IPD Sharing Access Criteria: Patient have been included and five-year outcome data will have been revised.
• IPD Sharing Supporting Information Type: Study Protocol; Informed Consent Form (ICF); Clinical Study Report (CSR); Analytic Code

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No