The natriuretic peptide MR-proANP predicts all-cause mortality and adverse outcome in community patients: a 10-year follow-up study
Bibliographic record
Abstract
BACKGROUND: The precursor peptide of atrial natriuretic peptide (MR-proANP) has a physiological role in fluid homeostasis and is associated with mortality and adverse clinical outcomes in heart failure patients. Little is known about the prognostic potential of this peptide for long-term mortality prediction in community-dwelling patients. We evaluated associations of MR-proANP levels with 10-year all-cause mortality in patients visiting their general practitioner for a respiratory tract infection. METHODS: In this post-hoc analysis including 359 patients (78.5%) of the original trial, we calculated cox regression models and area under the receiver operating characteristic curve (AUC) to assess associations of MR-proANP blood levels with mortality and adverse outcome including death, pulmonary embolism, and major adverse cardiac or cerebrovascular events. RESULTS: After a median follow-up of 10.0 years, 9.8% of included patients died. Median admission MR-proANP levels were significantly elevated in non-survivors compared to survivors (80.5 pmol/L, IQR 58.6-126.0; vs. 45.6 pmol/L, IQR 34.2-68.3; p<0.001) and associated with 10-year all-cause mortality (age-adjusted HR 2.0 [95% CI 1.3-3.1, p=0.002]; AUC 0.79). Results were similar for day 7 blood levels and also for the prediction of other adverse outcomes. CONCLUSIONS: Increased MR-proANP levels were associated with 10-year all-cause mortality and adverse clinical outcome in a sample of community-dwelling patients. If diagnosis-specific cut-offs are confirmed in future studies, this marker may help to direct preventive measures in primary care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".