Management of Lost Atherectomy Devices in the Coronary Arteries
Bibliographic record
Abstract
Rotational and orbital atherectomy are important tools to treat calcific coronary disease. Entrapment of an atherectomy device, that is, rotational atherectomy burr or orbital atherectomy crown, is a serious complication during atherectomy. Loss of an atherectomy device is a more challenging complication that usually follows device entrapment. Although uncommon, given the increased adoption of atherectomy, it is important for interventional cardiologist to understand the mechanisms and principles for prevention and management of lost atherectomy devices. Given the paucity of reported cases, there is no clear consensus or defined approach as to how this complication is best managed. The current recommendations for burr/crown entrapment are generally to remove everything from the coronary vasculature and range from different percutaneous strategies and ultimately cardiac surgery. Conservative treatment with leaving the burr within the coronary vasculature has not been recommended. This article serves to review and provide a contemporary update on the management of lost atherectomy devices in the coronary arteries. We review the management and techniques centered around cases of lost atherectomy devices including a unique cohort of patients with lost burrs/crowns that were left in the coronary arteries. Finally, we provide an algorithmic approach to the contemporary management of lost atherectomy devices, incorporating a conservative strategy arm, and discuss situations where this may be considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".