Cardiac magnetic resonance versus transthoracic echocardiography for the assessment and quantification of aortic regurgitation in patients undergoing transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: The transthoracic echocardiographic (TTE) evaluation of the severity of residual aortic regurgitation (AR) following transcatheter aortic valve implantation (TAVI) has been controversial and lacks validation. OBJECTIVES: This study sought to compare TTE and cardiac magnetic resonance (CMR) for assessment of AR in patients undergoing TAVI with a balloon-expandable valve. METHODS: TTE and CMR exams were performed pre-TAVI in 50 patients and were repeated postprocedure in 42 patients. All imaging data were analysed in centralised core laboratories. RESULTS: The severity of native AR as determined by multiparametric TTE approach correlated well with the regurgitant volume and regurgitant fraction determined by CMR prior to TAVI (Rs=0.79 and 0.80, respectively; p<0.001 for both). However, after TAVI, the correlation between the prosthetic AR severity assessed by TTE and regurgitant volume and fraction measured by CMR was only modest (Rs=0.59 and 0.59, respectively; p<0.001 for both), with an underestimation of AR severity by TTE in 61.9% of patients (1 grade in 59.5%). The TTE jet diameter in parasternal view and the multiparametric approach (Rs=0.62 and 0.59, respectively; both with p<0.001) showed the best correlation with CMR regurgitant fraction post-TAVI. The circumferential extent of prosthetic paravalvular regurgitation showed a poor correlation with CMR regurgitant volume and fraction (Rs=0.32, p=0.084; Rs=0.36, p=0.054, respectively). CONCLUSIONS: The severity of AR following TAVI with a balloon-expandable valve was underestimated by echocardiography as compared with CMR. The jet diameter, but not the circumferential extent of the leaks, and the multiparametric echocardiography integrative approach best correlated with CMR findings. These results provide important insight into the evaluation of AR severity post-TAVI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".