Valve-in-Valve Challenges: How to Avoid Coronary Obstruction
Bibliographic record
Abstract
Coronary obstruction is a rare but life-threatening complication in patients undergoing transcatheter aortic valve replacement (TAVR). Aortic valve-in-valve (VIV) procedures to treat failed surgical bioprosthesis is associated with ~6-fold higher risk for coronary obstruction in certain situations. The primary mechanism consists in the occlusion of the coronary ostium by the dislodged leaflet from the bioprosthesis after deployment of the transcatheter heart valve (THV), which most commonly occurs during the index procedure, but in up to 1/3 of cases a delayed presentation ensues. The clinical presentation consists of severe hypotension and ECG changes in most of the patients, with very high mortality rates. Therefore, pre-procedural multi-slice computed tomography is crucial for identifying high-risk features, such as low coronary heights, shallow sinuses of Valsalva, and short virtual THV to coronary ostial distance (VTC). Also, some models of surgical bioprosthesis present an increased risk for this dreadful complication. Preemptive protective strategies with coronary wiring, with or without placement of an undeployed stent, could mitigate the risks associated with this complication in high-risk patients, even though studies are lacking. This review aims to take a clinical perspective on the challenges in avoiding this complication during VIV procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".