Analysis of Deaths Reported for Percutaneous Cardiac Ablation Catheter Devices
Bibliographic record
Abstract
Cardiac ablation is a widely used intervention for cardiac arrhythmias.Indications for cardiac ablation include atrial fibrillation, atrial flutter, supraventricular tachycardia, and idiopathic ventricular tachycardia.While cardiac ablation is a valuable method for curing arrhythmia, the procedure carries significant risks.This study aimed to analyse the number of deaths attributed to percutaneous cardiac ablation catheters over the decade from 2011 to 2021, using data from the Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database.Reported deaths attributed to percutaneous cardiac ablation catheters rose significantly in 2014.Since then, deaths attributed to percutaneous cardiac ablation catheters have remained relatively constant, with approximately 40 to 50 deaths reported yearly from 2014 to 2021.Importantly, reported deaths attributed to this class of devices are almost entirely driven by ablation devices manufactured by Biosense Webster, including the Thermocool® Smarttouch® catheter.Biosense Webster catheters accounted for 92% of reported deaths attributed to all percutaneous cardiac ablation catheters from 2011 to 2021.These results suggest a need for increased innovation, continuous improvement, and greater competition and choice within this class of devices.The study further characterized the causes of death attributable to percutaneous cardiac ablation catheters from 2011 to 2021.The data reveal that the most frequent causes of death are cardiac tamponade (25.5%), esophageal or atrio-esophageal fistula (22.5%), and cardiac arrest (16.0%).Other important causes of death following cardiac ablation include hypotension, stroke, cardiac perforation, embolism, ventricular fibrillation, dissection, shock, hemorrhage, and hypovolemia.
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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 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".