MétaCan
Menu
← Back to cohort
Record W2981704291 · doi:10.1093/eurheartj/ehz748.0115

2211Prevalence, incidence and prognostic implications of left bundle branch block in patients with stable coronary artery disease. an analysis from the CLARIFY registry

2019· article· en· W2981704291 on OpenAlexaff
Arthur Darmon, Grégory Ducrocq, Yedid Elbez, Emmanuel Sorbets, Roberto Ferrari, Ian Ford, Jean‐Claude Tardif, Michał Tendera, Kim Fox, Philippe Gabríel Steg

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineLeft bundle branch blockInternal medicineCardiologyCoronary artery diseaseMyocardial infarctionHeart failureIncidence (geometry)CardiomyopathyBundle branch blockStroke (engine)PopulationAtrial fibrillationElectrocardiography

Abstract

fetched live from OpenAlex

Abstract Background The prevalence, and prognostic implication of left bundle branch block (LBBB) in general population and patients admitted for acute myocardial infarction (MI) as been extensively studied. However, data are scarce about patients with stable coronary artery disease (CAD) and it remains unclear whether LBBB is only a marker of a severe cardiomyopathy or an independent predictor of events in these patients. Purpose We aimed to describe the prevalence, incidence and prognostic implications of LBBB in patients with stable CAD. Additionally, we aimed to describe the incidence of newly diagnosed LBBB that occurred without recent myocardial infarction. Methods CLARIFY is an international registry of more than 30.000 patients with stable CAD. LBBB was collected at baseline and at each follow-up visit, and patients were considered to have LBBB if the length of the QRS complex was of more than 120 milliseconds. Patients with previous pacemaker implantation of internal cardiac defibrillator were excluded. The primary outcome was a composite of cardiovascular (CV) Death, MI or stroke, and secondary outcomes included hospitalization for heart failure (HF) or the need for pacemaker implantation. Results From the 23.457 patients with available data regarding LBBB status, 1.041 (4.4%) had LBBB at baseline and 1.237 (5.3%) had at least one LBBB assessed during 5-year follow-up. Only 21 patients with newly diagnosed LBBB overtime, had a documented MI the same year. Compared to patients without LBBB, patients with LBBB had a higher risk profile regarding age (67.2±10.1 versus 63.6±10.4 years, p<0.0001), history of coronary artery bypass grafting (29.2% vs 23.7%, p<0.0001), diabetes (35.1% vs 28.4%, p<0.0001), and HF (25.2% vs 16.8%, p<0.0001) (Table). In unadjusted analysis, patients with LBBB had a higher risk of primary outcome (13.4% vs 8.7%, p<0.0001) and each secondary outcome. In multivariate analysis taking into account several possible confounders, there was no difference in the rate of CV death, MI or stroke between LBBB or no-LBBB patients (adjusted HR 1.04, 95% CI 0.85–1.29). However, patients with LBBB had a higher rate of pacemaker implantation (adjusted HR 2.21, 95% CI 1.55–3.15, p<0.0001) and hospitalization for HF (adjusted HR 1.53, 95% CI 1.25–1.88, p<0.0001) (Figure). Outcomes according to LBBB status Conclusion The prevalence of LBBB in patients with stable CAD was 4.4% and 5.3% with 5-year follow-up. The overwhelming majority of newly diagnosed LBBB were not contemporary of documented myocardial infarction. LBBB was not associated with a higher rate of major adverse cardiovascular events, including all cause mortality but with a higher risk of pacemaker implantation and hospitalization for heart failure. To our knowledge this is the first study reporting such results in a broad population of stable CAD patients. Acknowledgement/Funding None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.267
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Heart Journal→Same topicCardiac pacing and defibrillation studies→French-language works237,207→