Thrombus characterisation and evolution of hypoattenuating leaflet thickening after transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: Quantifying hypoattenuating leaflet thickening (HALT) on computed tomography angiography (CTA) may provide insights into its clinical implications and guide decisions on oral anticoagulation therapy following transcatheter aortic valve implantation (TAVI). AIMS: We sought to assess the association between quantitative CTA features of HALT and its evolution over time in a real-world cohort after TAVI. METHODS: Among 612 patients who underwent CTA 30 days post-TAVI with balloon-expandable bioprostheses, HALT was detected in 118 (19%). We prospectively followed 99 patients who had undergone a second CTA at 1 year to assess HALT progression. Thrombus volume and mean attenuation were quantified using semiautomated software, and various parameters of bioprosthetic deformation were analysed. RESULTS: Complete resolution of HALT was observed in 43 patients. Multivariate logistic regression showed that lower thrombus attenuation was an independent predictor of HALT resolution (odds ratio [OR] 0.45; p=0.030), along with the eccentricity index (OR 0.42; p=0.003), deformation index (OR 0.53; p=0.005), and implant canting (OR 1.88; p=0.026). In the 56 patients without complete HALT resolution, thrombus evolution was visually categorised as regression (48%), stability (29%), or progression (23%). In a quantitative assessment, regression was associated with a significant decrease in thrombus volume (291 mm³ to 130 mm³; p=0.007), while progression showed an increase (187 mm³ to 667 mm³; p=0.005). The change in thrombus volume between 30 days and 1 year correlated with the magnitude of changes in mean transvalvular gradients over the same period (r=0.462; p<0.001). CONCLUSIONS: Quantitative thrombus characterisation on CTA is predictive of HALT resolution and correlates with the haemodynamic performance of transcatheter aortic valves.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".