Usefulness of the B-Type Natriuretic Peptides in Low Ejection Fraction, Low-Flow, Low-Gradient Aortic Stenosis Results from the TOPAS Multicenter Prospective Cohort Study
Bibliographic record
Abstract
Background Patients with low left ventricular ejection fraction (LVEF), low-flow, low-gradient (i.e. classical low flow [CLF]) aortic stenosis (AS) have a dismal short-term outcome without aortic valve replacement (AVR) but high operative mortality. We hypothesized that brain natriuretic peptides (BNP/NT-proBNP) can risk stratify patients with CLF AS and may assist in clinical decision-making. Methods Patients with aortic valve area ≤1.2 cm 2 , mean transvalvular gradient <40 mmHg, and left ventricular ejection fraction <50%, were prospectively recruited. BNP and/or NT-proBNP were measured at baseline. Results Among 234 patients (77 [68–83] years, 76% male), BNP > 550 pg/ml or NT-proBNP > 1,600 pg/ml (85% and 93% sensitivity, respectively, to correctly classify 1-year death) strongly predicted all-cause mortality (adjusted HR=2.53 [1.68–3.81], p < 0.001) outperforming flow reserve and baseline LVEF (all likelihood ratio p ≤ 0.02). For both natriuretic peptides, spline curve analysis showed gradual increase in mortality with higher biomarkers levels, which was blunted by AVR. In a head-to-head comparison (n = 104), NT-proBNP appeared to have superior incremental prognostic value than BNP (likelihood-ratio p < 0.001 vs. p = 0.07). Baseline NT-proBNP ≥ 1,600 pg/ml or BNP ≥ 550 pg/ml, identified: i) a high-risk cohort with a dismal outcome under conservative management, but a markedly better survival associated with early AVR (adjusted HR=0.41 [0.25–0.65], p < 0.001); and ii) a low-risk cohort with an excellent 1-year survival (94 ± 4%) with conservative management or deferred AVR. Conclusion In patients with CLF AS, BNP/NT-proBNP have the potential to identify high-risk patients who may benefit from early AVR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".