Predictors of Mitral Regurgitation Severity Improvement in Patients With Severe Aortic Stenosis Undergoing Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
BACKGROUND: Mitral regurgitation (MR) is frequently associated with severe aortic stenosis (AS). Significant MR is associated with less favorable prognosis after transcatheter aortic valve implantation (TAVI), including higher early and late mortality rate. The severity of MR is improved in about half of patients undergoing TAVI. However, the predictors of MR improvement after TAVI are unknown. We sought to investigate whether several demographic, clinical, echocardiographic and laboratory parameters and procedure characteristics are predictive of MR severity improvement after TAVI procedure. METHODS: A total of 309 consecutive patients with severe symptomatic AS underwent TAVI procedure in our center from July 1, 2015 till December 31, 2019. The 85 patients had concomitant significant (grade 2 or 3) MR. We performed logistic regression analysis of age, sex, atrial fibrillation, left ventricular ejection fraction, end diastolic diameter, end systolic diameter, left atrial diameter, left atrial area, MR etiology (functional vs. degenerative), CHA2DS2-VASc score, pre-procedure B-type natriuretic peptide (BNP) levels and type of TAVI bioprosthesis as possible predictors of post-TAVI improvement of severity of MR. RESULTS: The 35 patients have at least one grade reduction in the severity of MR in follow-up echo. None of the analyzed parameters were predicting of the MR severity improvement. CONCLUSIONS: In this small single-center cohort study, we were unable to find any feasible demographic, clinical, echocardiographic or laboratory predictors of MR improvement after TAVI. There was no correlation between etiology of MR or type of TAVI bioprosthesis used and MR improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".