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Record W4405818849 · doi:10.1002/ehf2.15190

Real-World Outcomes in Cardiac Resynchronization Therapy Patients: Primary Results of the SMART Registry

2024· article· en· W4405818849 on OpenAlexaboutno aff
Ignacio García‐Bolao, Roy S. Gardner, Daniel Gras, Antonio D’Onofrio, George E. Mark, Devi G. Nair, Nicolas Lellouche, Miroslav Novák, Ronald Lo, E. W. Chew, David J. Wright, Andrew J. Kaplan, Matteo Bertini, Sara Veraghtert, Michelle M. Harbin, Elizabeth Matznick, Patrick Yong, Kenneth M. Steín

Bibliographic record

VenueESC Heart Failure · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiac resynchronization therapyEjection fractionInternal medicineHeart failureCardiologyLeft bundle branch blockClinical endpointImplantable cardioverter-defibrillatorAtrial fibrillationCanadian Cardiovascular SocietyQuality of life (healthcare)Clinical trialMyocardial infarction

Abstract

fetched live from OpenAlex

AIMS: Cardiac resynchronization therapy (CRT) is guideline recommended for the treatment of symptomatic heart failure (HF) with reduced left ventricular ejection fraction and prolonged QRS. However, patients with common comorbidities, such as persistent/permanent atrial fibrillation (AF), are often under-represented in clinical trials. METHODS: The Strategic Management to Optimize Response to Cardiac Resynchronization Therapy (SMART) registry (NCT03075215) was a global, multicentre, registry that enrolled de novo CRT implants, or upgrade from pacemaker or implantable cardioverter defibrillator to CRT-defibrillator (CRT-D), using a quadripolar left ventricular lead in real-world clinical practice. The primary endpoint was CRT response between baseline and 12 month follow-up defined as a clinical composite score (CCS) consisting of all-cause mortality, HF-associated hospitalization, New York Heart Association (NYHA) class and quality of life global assessment. RESULTS: The registry enrolled 2035 patients, of which 1558 had completed CCS outcomes at 12 months. The patient cohort was 33.0% female, mean age at enrolment was 67.5 ± 10.4 years and the mean left ventricular ejection fraction was 29.6 ± 7.9%. Notably, there was a high prevalence of mildly symptomatic patients (NYHA class I/II 51.3%), non-left bundle branch block (LBBB) morphology (38.0%), AF (37.2%) and diabetes mellitus (34.7%) at baseline. CCS at 12 months improved in 58.9% (n = 917) of patients; 20.1% (n = 313) of patients stabilized and 21.0% (n = 328) worsened. Several patient characteristics were associated with a lower likelihood of response to CRT including older age, ischaemic aetiology, renal dysfunction, AF, non-LBBB morphology and diabetes. Higher HF hospitalization (P < 0.001) and all-cause mortality (P < 0.001) were observed in patients with AF. These patients also had lower percentages of ventricular pacing than patients in sinus rhythm at baseline and follow-up (P < 0.001, both). A further association between AF and non-LBBB was observed with 81.4% of AF non-LBBB patients experiencing an HF hospitalization compared with 92.5% of non-AF LBBB patients (P < 0.001). Mortality between subgroups was also statistically significant (P = 0.019). CONCLUSIONS: This large, global registry enrolled a CRT-D population with higher incidence of comorbidities that have been historically underrepresented in clinical trials and provides new insight into factors influencing response to CRT. As defined by CCS, 58.9% of patients improved and 20.1% stabilized. Patients with AF had particularly worse clinical outcomes, higher HF hospitalization and mortality rates and lower percentages of ventricular pacing. High incidence of HF hospitalization in patients with AF and non-LBBB in this real-world cohort suggests that ablation may play an important role in increasing future CRT response rates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.204
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.279
Teacher spread0.267 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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