Bone Morphogenetic Protein 10—A Novel Biomarker to Predict Adverse Outcomes in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background Patients with atrial fibrillation (AF) face an increased risk of death and major adverse cardiovascular events (MACE). We aimed to assess the predictive value of the novel atrial-specific biomarker BMP10 (bone morphogenetic protein 10) for death and MACE in patients with AF in comparison with NT-proBNP (N-terminal prohormone of B-type natriuretic peptide). Methods and Results BMP10 and NT-proBNP were measured in patients with AF enrolled in Swiss-AF (Swiss Atrial Fibrillation Study), a prospective multicenter cohort study. A total of 2219 patients were included (median follow-up 4.3 years [interquartile range 3.9, 5.1], mean age 73±9 years, 73% male). In multivariable Cox proportional hazard models, the adjusted hazard ratio (aHR) associated with 1 ng/mL increase of BMP10 was 1.60 (95% CI, 1.37-1.87) for all-cause death, and 1.54 (95% CI, 1.35-1.76) for MACE. For all-cause death, the concordance index was 0.783 (95% CI, 0.763-0.809) for BMP10, 0.784 (95% CI, 0.765-0.810) for NT-proBNP, and 0.789 (95% CI, 0.771-0.815) for both biomarkers combined. For MACE, the concordance index was 0.732 (95% CI, 0.715-0.754) for BMP10, 0.747 (95% CI, 0.731-0.768) for NT-proBNP, and 0.750 (95% CI, 0.734-0.771) for both biomarkers combined. When grouping patients according to NT-proBNP categories (<300, 300-900, >900 ng/L), higher aHRs were observed in patients with high BMP10 in the categories of low NT-proBNP (all-cause death aHR, 2.28 [95% CI, 1.15-4.52], MACE aHR, 1.88 [95% CI, 1.07-3.28]) and high NT-proBNP (all-cause death aHR, 1.61 [95% CI, 1.14-2.26], MACE aHR, 1.38 [95% CI, 1.07-1.80]). Conclusions BMP10 strongly predicted all-cause death and MACE in patients with AF. BMP10 provided additional prognostic information in low- and high-risk patients according to NT-proBNP stratification. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02105844.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".