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NCT Number: NCT04293471

Prediction of Outcome by Echocardiography in 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

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Key information

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

University Hospital North Norway

Tromsø, Troms, 9038, Norway

Location status: Recruiting

Location contact

Assami Rösner, PhD

CONTACT

[email protected]

+4795990071

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Cardiovascular death

    Time frame: 15 years

    Timepoint (day) of death and its cause

  2. Death of any cause

    Time frame: 15 years

    Timepoint (day) of death and its cause

Secondary outcomes

  1. Hospital admission due to heart-failure

    Time frame: 15 years

    Time point of hospital admission and main-diagnosis

Other outcomes

  1. Remodelling

    Time frame: 5 years

    Increase or decrease of ventricular volume in ml

  2. Cardiac function

    Time frame: 5 years

    Increase or decrease of ejection fraction in %

  3. Heart failure

    Time frame: 5 years

    Increase or decrease of heart failure by proBNP and NYHA class

Study contacts

Contact information is provided by the study sponsor or research team.

Assami Rösner, MD,PhD

CONTACT

[email protected]

04795990071

Sponsors and collaborators

Lead sponsor

University Hospital of North Norway

Other

Collaborators

  • KU Leuven
  • Norwegian University of Science and Technology
  • Oslo University Hospital
  • University of Bergen
  • University of Tromso

Registry information

Official study title

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

Acronym: EchoLBBB

Important dates

Study start
2021
Primary completion
2027
Study completion
2036
First posted
Mar 3, 2020
Registry last updated
May 24, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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