Three-dimensional printing for assessment of paravalvular leak in transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: Three-dimensional (3D) models have the unique ability to replicate individualized cardiac anatomy and may therefore provide clinical benefit. Transcatheter aortic valve implantation (TAVI) currently relies on preoperative imaging for accurate valve sizing, type of valve used, and avoidance of complications. Three-dimensional (3D) modelling may provide benefit for optimal preoperative TAVI planning. The goal of this study is to assess the utility of 3D modelling in the prediction of paravalvular leak (PVL) post TAVI. METHODS: Retrospective analysis of five patients who underwent TAVI at our center. Pre-operative cardiac gated CT images were utilized to create a 3D printed model with true size aortic root dimensions, including the coronary artery ostium location and left ventricular outflow tract. Deployment of the corresponding model and size TAVI valve into the created 3D model at a similar depth of implantation via fluoroscopy was performed for each patient. Degree of PVL was assessed using a closed system with water infusion under pressure over a duration of 5 s. Correlation was made between the volume obtained in the closed loop model during the pressurized period and the degree of PVL reported on the patients post TAVI placement on transthoracic echocardiogram. RESULTS: One female, and four males (age in years ranged from 68 to 87) underwent successful TAVI (0% 30-day mortality). PVL on post procedure TTE ranged from none to trivial. Successful deployment of TAVI valves inside the 3D model occurred in all cases. The average volume of water collected on three trials over 5 s ranged between 19.1-24.1 ml A multivariate linear regression showed significant association between the degree of PVL reported on post-operative transthoracic echocardiogram and the amount of volume detected in the 3D model (difference: -3.9657, 95% CI: (- 4.6761,-3.2554), p < 0.001). CONCLUSIONS: Our experiments show that replicated 3D models have potential clinical utilization in predicting PVL in the TAVI population. Future research into the role of 3D modelling in the field of TAVI should continue to be explored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".