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Record W4388594991 · doi:10.1093/eurheartj/ehad655.477

Catheter ablation for atrial fibrillation in patients with heart failure with reduced versus preserved ejection fraction; a systematic review and meta-analysis

2023· review· en· W4388594991 on OpenAlexaff
Alireza Oraii, Ratika Parkash, Jorge Wong, William F. McIntyre, Ghazal Razeghi, Krzysztof Kowalik, Emilie P. Belley‐Côté, Nima Zamiri, Stuart J. Connolly, Antony Tang, Jeff S. Healey

Bibliographic record

VenueEuropean Heart Journal · 2023
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsWestern UniversityMcMaster UniversityQueen Elizabeth II Health Sciences CentrePopulation Health Research Institute
Fundersnot available
KeywordsMedicineHeart failureAtrial fibrillationEjection fractionCardiologyInternal medicineCatheter ablationRandomized controlled trialMeta-analysisAblation

Abstract

fetched live from OpenAlex

Abstract Background Catheter ablation is superior to pharmacological therapies for lowering adverse cardiovascular outcomes in patients with both atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF). Yet, it is uncertain whether patients with heart failure with mildly reduced or preserved ejection fraction (HFpEF) derive the same treatment benefit from catheter ablation. Purpose This systematic review and meta-analysis aimed to assess whether the efficacy of AF catheter ablation in reducing all-cause mortality and heart failure events is different in patients with HFrEF versus HFpEF. Methods We searched MEDLINE, Embase, and Cochrane Central to September 2022 for randomized controlled trials in patients with heart failure comparing catheter ablation for AF with medical therapy (defined as rate or rhythm control with medications, device implantation, and/or cardioversion). The outcomes of interest were all-cause mortality and heart failure events (i.e. hospitalization or worsening). Pairs of reviewers systematically screened the eligible studies and used random-effects models to combine data. We contacted authors of eligible studies to obtain unpublished data on heart failure subgroups. We used an interaction p-value to test the statistical significance of difference between subgroups with HFrEF and HFpEF. Results A total of 8 studies (2118 participants, mean age: 65.6 years, 28.1% women) assessed the effect of catheter ablation on all-cause mortality in patients with AF and heart failure. We found no statistical evidence of a different effect for AF catheter ablation in reducing all-cause mortality in patients with HFrEF (196 events, RR 0.67, 95% CI: 0.50-0.90) compared to those with HFpEF (77 events, RR 0.95, 95% CI: 0.39-2.30) – p for subgroup differences=0.46. In addition, 7 studies (2057 participants, mean age: 65.8 years, 29% women) evaluated the effect of catheter ablation on heart failure events in patients with AF and heart failure. As compared to conventional medical therapies, AF catheter ablation significantly reduced the risk of heart failure events in patients with HFrEF (285 events, RR 0.59, 95% CI: 0.48-0.72); however, it had little or no effect on heart failure events in those with HFpEF (106 events, RR 0.93, 95% CI: 0.65-1.32). Interaction analysis showed significant effect modification for AF catheter ablation in patients with HFpEF – p for subgroup differences=0.03. Conclusion Catheter ablation is superior to conventional medical therapies for reducing all-cause mortality in patients with AF and heart failure. However, its beneficial effect on heart failure events may be limited to patients with HFrEF. The available evidence on patients with HFpEF is insufficient for a definitive conclusion. Future trials are warranted to study the prognostic effect of catheter ablation in patients with AF and HFpEF.All-cause MortalityHeart Failure Events

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)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0220.032
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.215
GPT teacher head0.394
Teacher spread0.179 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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