Copeptin predicts 10-year all-cause mortality in community patients: a 10-year prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Copeptin, the C-terminal part of the arginine vasopressin (AVP) precursor peptide, is secreted in response to stress and correlates with adverse clinical outcomes in the acute-care hospital setting. There are no comprehensive data regarding its prognostic value in the community. We evaluated associations of copeptin levels with 10-year mortality in patients visiting their general practitioner (GP) for a respiratory infection included in a previous trial. METHODS: This is a post hoc analysis including data from 359 patients included in the PARTI trial. Copeptin was measured in batch-analysis on admission and after 7 days. We calculated Cox regression models and area under the receiver operating characteristic curve (AUC) to assess an association of copeptin with mortality and adverse outcome. Follow-up data were collected by GP, patient and relative tracing through phone interviews 10 years after trial inclusion. RESULTS: After a median follow-up of 10.0 years, mortality was 9.8%. Median admission copeptin levels (pmol/L) were significantly elevated in non-survivors compared to survivors (13.8, IQR 5.9-27.8; vs. 6.3 IQR 4.1-11.5; p<0.001). Admission copeptin levels were associated with 10-year all-cause mortality [age-adjusted hazard ratio 1.7 (95% CI, 1.2-2.5); p<0.001, AUC 0.68]. Results were similar for discharge copeptin levels. Copeptin also predicted adverse outcomes defined as death, pulmonary embolism and major adverse cardiac and cerebrovascular events. CONCLUSIONS: In a sample of community-dwelling patients visiting their GP for a respiratory infection, copeptin levels were associated with 10-year all-cause mortality. In conjunction with traditional risk factors, this marker may help to better direct preventive measures in this population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".