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
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Bibliographic record
Abstract
BackgroundPatients 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.MethodsPatients with aortic valve area ≤1.2 cm2, mean transvalvular gradient <40 mmHg, and left ventricular ejection fraction <50%, were prospectively recruited. BNP and/or NT-proBNP were measured at baseline.ResultsAmong 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.ConclusionIn 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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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 | 0.000 |
| 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 it