Annular Rupture During Transcatheter Aortic Valve Replacement without a Surgical Option–The Double-edged Sword of Mechanical Circulatory Support
Bibliographic record
Abstract
Transcatheter aortic valve replacement (TAVR) has become a well-established treatment option for patients with severe aortic stenosis. With advances in the field and positive outcomes in clinical trials, TAVR use has expanded from being limited to older patients with prohibitive surgical risk to younger patients with intermediate and low surgical risk. As operators’ experiences have grown and technology has evolved, there are fewer TAVR complications. However, when complications do occur, they remain associated with significant morbidity and mortality. Aortic annulus rupture is a catastrophic complication of TAVR. Risk factors for annulus rupture include valve oversizing, female gender, a large calcification burden in the subannular area and left ventricular outflow tract, a small annulus, a calcified bicuspid aortic valve, and a narrow aortic root. When a rupture occurs, emergent cardiac surgery is a common intervention; however, not all patients are candidates for emergent surgery. We present a case of a 79-year-old female patient who developed an annular rupture during TAVR with a balloon-expandable valve. The patient was successfully treated; we reversed anticoagulation and controlled her blood pressure. We also performed a pericardiocentesis and provided temporary support by using extracorporeal membrane oxygenation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".