Management of Atrio-Esophageal Fistula Following Left Atrial Ablation
Bibliographic record
Abstract
Currently, no guidelines have been established for the treatment of atrio-esophageal fistula (AEF) secondary to left atrial ablation therapy. After comprehensive literature review, we aim to make suggestions on the management of this complex complication and also present a case series. We performed a review of the existing literature on AEF in the setting of atrial ablation. Using keywords atrial fibrillation, atrial ablation, fistula formation, atrio-esophageal fistula, complications, interventions, and prognosis, a search was made using the medical databases PUBMED and MEDLINE for reports in English from 2000 to April 2015. A statistical analysis was performed to compare the three different intervention arms: medical management, stent placement and surgical intervention. The results of our systematic review confirm the high mortality rate associated with AEF following left atrial ablation and the necessity to diagnose atrio-esophageal injury in a timely manner. The mortality rates of this complication are 96% with medical management alone, 100% with stent placement, and 33 % with surgical intervention. Atrio-esophageal injury and subsequent AEF is an infrequent but potentially fatal complication of atrial ablation. Early, prompt, and definitive surgical intervention is the treatment of choice.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".