Abstract 13459: Usefulness of B-Type Natriuretic Peptide and High-Sensitivity Cardiac Troponin for Risk Stratification in Low Flow, Low Gradient Aortic Stenosis -A Substudy of the TOPAS Study
Bibliographic record
Abstract
Background: B-type natriuretic peptide (BNP) and troponin T (TNT) have been shown to be associated with outcome in several clinical conditions. However the prognostic value of these 2 biomarkers combined together remains unclear. We aimed to explore the prognostic value of combined BNP and high-sensitivity TNT (hsTNT) in patients with low flow low gradient aortic stenosis (LGLG AS). Methods: Ninety eight patients (74±10 year; 75% men) with LFLG AS (LVEF <50% and/or SVi<35 ml/m2, mean gradient<40 mmHg, AVAi<0.6 cm2/m2) were prospectively enrolled in the TOPAS study and included in this analysis. The cohort was divided into three groups according to BNP and hsTNT plasma levels: Group A, patients with BNP<550 pg/mL and undetectable TNT (hsTNT<0.015 ng/mL); Group B, with BNP≥550 pg/mL or hsTNT≥0.015 ng/mL and Group C, patients with BNP≥550 pg/mL and hsTNT≥0.015 ng/mL. The primary endpoint was all-cause mortality. Results: Among the 98 patients, 32 (33%) were in Group A, 39 (40%) in Group B and 24 (27%) in Group C. During a median follow up of 2.8 [IQR=1.1-4.1] years, 55 patients died. Patients in Group C were older and had lower LVEF (all p<0.05). Two-year mortality was higher in Group C (41±9%) than in Group B (23±7) and Group A (5±4%) (p=0.0019). In Group B, there was no significant difference in 2-year mortality rates between the subgroup with hsTNT≥0.015 ng/mL versus that with BNP≥550 pg/mL (26±9% vs. 11±10%, respectively, p=0.21). In multivariable analysis adjusted for age, type of treatment (AVR vs. conservative), coronary artery disease and LVEF, Group C remained independently associated with increased risk of mortality (HR= 4.25; p=0.023), and Group B tended to have higher mortality (HR=3.63; p=0.058) as compared to Group A. Conclusion: The results of this study demonstrate the usefulness of combined measures of BNP and hsTNT to enhance risk stratification in patients with LFLG AS. Patients having activation of both BNP and hsTNT have a 4-fold increase risk of mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".