Modification of the Association of B-Type Natriuretic Peptides With Mortality and Hospitalization Outcomes by Sex
Bibliographic record
Abstract
BACKGROUND: The effects of sex on the prognostic implications of natriuretic peptide (NP) elevation have not been fully elucidated in the population. OBJECTIVES: The purpose of this study was to examine if sex modifies associations of NPs with mortality and hospitalization. METHODS: In a population-based retrospective cohort study, we identified all patients (aged ≥40 years) undergoing NP testing in Ontario, Canada (2015-2020). We examined for the presence of sex-by-NP interactions for 1-year outcomes and conducted sex-specific analyses for continuously increasing NP concentrations. RESULTS: We studied 91,017 individuals with B-type natriuretic peptide (BNP) tests (median 75 years; 48.0% females) and 81,578 individuals with N-terminal pro-BNP (NT-proBNP) tests (74 years; 48.6% females). Adjusted 1-year risks of all-cause mortality at any given NP concentration were higher in males than females. For example, 1-year mortality at a BNP of 400 ng/L was 16.8% in females and 21.6% in males. At an NT-proBNP of 900 ng/L, 1-year mortality was 14.2% in females and 18.5% in males. However, there were also significant sex interactions with BNP (P = 0.002) and NT-proBNP (P = 0.03) for mortality outcomes. When we examined cardiovascular hospitalizations, there was also a significant sex-by-NP interaction. For BNP, the risk of cardiovascular hospitalization was higher in males at lower concentrations but was higher in females at higher concentrations (P-interaction = 0.005). For NT-proBNP, the risk of cardiovascular hospitalization was higher in males at lower NP concentrations, but the gap narrowed at higher NP levels (P interaction = 0.03). CONCLUSIONS: Sex modifies the association between NP concentrations and all-cause mortality or cardiovascular hospitalizations. Prognostically, interpretation of NP levels should consider effect modification by sex.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".