Effect of Concomitant Surgical Atrial Fibrillation Ablation in Patients with Reduced Left Ventricle Ejection Fraction: A Propensity‐Score Matching Analysis
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is common in patients with reduced left ventricle ejection fraction (RLVEF). The impact of concomitant surgical atrial fibrillation ablation (SAFA) in patients with RLVEF is uncertain. The purpose of this study was to assess the outcomes of concomitant SAFA in patients with RLVEF undergoing heart surgery on heart failure (HF) rehospitalization and mortality. Methods: Using a local registry and electronic health records linked with provincial civil register survival data from July 2002 to April 2019, we analyzed treatment and outcomes in a cohort of patients with AF and HF defined by left ventricle ejection fraction (LVEF) ≤ 40%. Health records were used to collect treatment and International Classification of Diseases (ICD 10) codes to determine outcomes. A negative binomial model was used to compare outcomes such as all-cause mortality and rehospitalization for heart failure. Results: The cohort included 682 patients with RLVEF and AF who underwent coronary artery bypass graft and/or valve surgery. A total of 196 patients (29%) underwent concomitant SAFA. After matching, 132 patients with concomitant SAFA were compared to 159 patients who did not undergo concomitant SAFA. At 6.0±3.7 years of follow-up, concomitant SAFA was not associated with lower all-cause mortality (P=0.9861) and reduction in rehospitalizations for heart failure decompensation (P=0.31) compared to patients who did not have concomitant SAFA performed. Post-operatively, concomitant SAFA might be associated with less vasopressor and mechanical support use (p=0.01). Conclusions: Concomitant SAFA during index cardiac surgery is safe but does not reduce mortality or rehospitalizations for HF. The effects of concomitant SAFA in the context of RLVEF needs to be better studied with prospective trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".