Three‐Dimensional Echocardiography for Transcatheter Aortic Valve Replacement Sizing: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Transcatheter aortic valve replacement (TAVR) is the standard of care for many patients with severe symptomatic aortic stenosis and relies on accurate sizing of the aortic annulus. It has been suggested that 3-dimensional transesophageal echocardiography (3D TEE) may be used instead of multidetector computed tomography (MDCT) for TAVR planning. This systematic review and meta-analysis compared 3D TEE and MDCT for pre-TAVR measurements. Methods and Results A systematic literature search was performed. The primary outcome was the correlation coefficient between 3D TEE- and MDCT-measured annular area. Secondary outcomes were correlation coefficients for mean annular diameter, annular perimeter, and left ventricular outflow tract area; interobserver and intraobserver agreements; mean differences between 3D TEE and MDCT measurements; and pooled sensitivities, specificities, and receiver operating characteristic area under curve values of 3D TEE and MDCT for discriminating post-TAVR paravalvular aortic regurgitation. A random effects model was used. Meta-regression and leave-one-out analysis for the primary outcome were performed. Nineteen studies with a total of 1599 patients were included. Correlations between 3D TEE and MDCT annular area, annular perimeter, annular diameter, and left ventricular outflow tract area measurements were strong (0.86 [95% CI, 0.80-0.90]; 0.89 [CI, 0.82-0.93]; 0.80 [CI, 0.70-0.87]; and 0.78 [CI, 0.61-0.88], respectively). Mean differences between 3D TEE and MDCT between measurements were small and nonsignificant. Interobserver and intraobserver agreement and discriminatory abilities for paravalvular aortic regurgitation were good for both 3D TEE and MDCT. Conclusions For pre-TAVR planning, 3D TEE is comparable to MDCT. In patients with renal dysfunction, 3D TEE may be potentially advantageous for TAVR measurements because of the lack of contrast exposure.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".