Abstract 19213: Long-Term Prognostic Value of and Serial Changes in Plasma N-terminal Pro B-type Natriuretic Peptide in Patients Undergoing Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Background: Little is known about the usefulness of evaluating cardiac neurohormones in patients undergoing transcatheter aortic valve implantation (TAVI). The objectives of this study were to evaluate the baseline values and serial changes of NT-proBNP following TAVI, its related factors and prognostic value. Methods and Results: A total of 333 consecutive patients undergoing TAVI were included. Baseline, procedural and follow-up (median: 20 [8 to 35] months) data were prospectively collected. Systematic NT-proBNP measurements were performed at baseline, hospital discharge, 1-month, 6- to12 months, and yearly thereafter. Baseline NT-proBNP values were elevated in 86% of the patients (median: 1692 [667-3910] pg/mL); lower left ventricular ejection fraction and stroke volume index, higher LV mass, and renal dysfunction were associated with greater baseline values (p<0.01 for all). Higher NT-proBNP levels were independently associated with increased long-term overall and cardiovascular mortality (P<0.001 for both), with a baseline cut-off level of ~2,000 pg/mL best determining poorer outcomes (P<0.001). At 6- to 12-month follow-up, NT-proBNP levels had decreased (P<0.001) by 23 (IQR: -62 to +32)% and remained stable up to 4-year follow-up. In 39% of the patients, however, NT-proBNP values increased to some degree. Pre-procedural chronic atrial fibrillation, lower mean transaortic gradient and moderate/severe mitral regurgitation were the predictors of the lack of NT-proBNP improvement after TAVI (P<0.01 for all). Conclusions: Very high NT-proBNP levels were observed in most TAVI candidates, which in turn predicted a higher overall and cardiac mortality after a median follow-up of approximately 2 years. TAVI was associated with a significant decrease in NT-proBNP levels over time, but a lack of improvement was observed in more than one third of the patients, mainly due to the presence of chronic atrial fibrillation, a lower transvalvular gradient and moderate-to-severe mitral regurgitation. These results strongly suggest the usefulness of implementing NT-proBNP measurements for the clinical decision-making process and follow-up of TAVI patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".