Abstract 14922: Prognostic Value of Baseline BNP and NT-ProBNP and its Interaction With Spironolactone in Patients With Heart Failure and Preserved Ejection Fraction in the TOPCAT Trial
Bibliographic record
Abstract
Background: Plasma natriuretic peptides (NP) are helpful in the diagnosis of heart failure (HF) with preserved ejection fraction (HFpEF) and predict adverse outcomes. Levels of NP beyond a certain cut-off level are often used as inclusion criteria in clinical trials to ensure that the patients have HF, and to select patients at higher risk. Whether treatments have a differential effect on outcomes across the spectrum of NP levels is unclear. In the I-Preserve trial a benefit of irbesartan on all outcomes was only seen in HFpEF patients with low but not high NP levels. We hypothesized that in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial, spironolactone might have a greater benefit in patients with lower NP levels. Methods and Results: BNP (n=468) or NT-proBNP (n=400) levels were available at baseline in 868 patients with HFpEF enrolled in the natriuretic peptide stratum (BNP ≥100 pg/mL or an NT- proBNP ≥360 pg/mL) of the TOPCAT trial. In a multi-variable Cox regression model, that included age, gender, region (Americas vs. Russia/Georgia), atrial fibrillation, diabetes, eGFR, BMI and heart rate, higher BNP or NT-proBNP as a continuous, standardized log-transformed variable or grouped by terciles (see Figure for BNP & NT-proBNP tercile values) was independently associated with an increased risk of the primary endpoint of cardiovascular mortality, aborted cardiac arrest, or hospitalization for heart failure (Figure-1). There was a significant interaction between the effect of spironolactone and baseline BNP or NT-proBNP terciles for the primary outcome (P=0.02, Figure-2), with greater benefit of the drug in the lower compared to higher NP terciles. Conclusions: The benefit of spironolactone in lower risk HFpEF patients may indicate effects of the drug on early, but not late higher-risk stage of the disease. These findings question the strategy of using elevated NP as a patient selection criterion in HFpEF trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".