Left Atrial Appendage Volume and Plasma Docosahexaenoic Acid Levels Are Associated With Atrial Fibrillation Recurrence After Catheter Ablation
Bibliographic record
Abstract
BACKGROUND: Risk factors for atrial fibrillation (AF) recurrence in patients who have undergone AF catheter ablation have not been fully clarified. The objective of this study was to assess whether the left atrium (LA) and LA appendage (LAA) volumes, and cardio-metabolic markers such as polyunsaturated fatty acids (PUFAs) levels were associated with AF recurrence. METHODS: Seventy-seven consecutive patients with AF (mean age, 59 ± 8 years; male, 81%; paroxysmal AF, 64%) undergoing catheter ablation were enrolled. Using contrast-enhanced cardiac multi-detector computed tomography (MDCT) scan, the LA and LAA volume and orifice area were assessed. Radiofrequency ablation was performed by an irrigation catheter, initially targeting the pulmonary veins with a wide area circumferential ablation. RESULTS: Patients with AF recurrence (36%) exhibited both larger LAA volumes and an LAA orifice area than those without AF recurrence, whereas the LA diameter and LA volumes were not significantly different. Notably, AF recurrence occurred in all patients with a large LAA (≥ 25 mL), and the LAA volume was significantly and negatively associated with docosahexaenoic acid (DHA) levels (β = -0.33, P = 0.003). A multiple regression analysis revealed that the log N-terminal proB-type natriuretic peptide and plasma DHA levels were independent factors for the LAA volume when adjusted for age, AF detected age, left ventricular (LV) ejection fraction, end-systolic LV diameter. CONCLUSIONS: These results suggest that the association between LAA volume and low plasma DHA levels may be an important factor for post-ablation AF recurrence.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".