Early Aortic Transcatheter Heart Valve Thrombosis
Bibliographic record
Abstract
BACKGROUND: Early stent valve thrombosis after transcatheter aortic valve implantation (TAVI) is a rare complication, which is diagnosed based on the appearance of clinical symptoms of heart failure and echocardiographic findings. After TAVI, transthoracic echocardiography is performed to assess transcatheter heart valve (THV) function. However, preliminary reports indicate the potential additive clinical value of multidetector computed tomography (MDCT) for the diagnosis of THV thrombosis. We sought to determine the value of MDCT for the diagnosis of THV thrombosis and the frequency of this complication after balloon-expandable TAVI. METHODS AND RESULTS: MDCT was performed in 140 patients within 1 to 3 months after TAVI with the Edwards Sapien XT THV to assess the presence of THV thrombosis and THV stent geometry. Post-TAVI MDCT identified THV thrombosis in 5 patients (4%). Of note, 4 of these patients were asymptomatic and had a normal transthoracic echocardiographic examination without signs of thrombus formation or flow obstruction. In patients with THV thrombosis, a left ventricular ejection fraction of <35% was present in 3 (60%), whereas 2 (40%) did not receive standard post-TAVI dual-antiplatelet therapy. Neither THV underexpansion nor THV noncircularity was detected in patients with THV thrombosis. CONCLUSIONS: Post-TAVI MDCT is a valuable tool for the diagnosis of THV thrombosis, and this complication seems to be more common than previously anticipated. Larger studies are required to identify specific risk factors of THV thrombosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".