Bio-Adrenomedullin as a Marker of Congestion in Patients with New-Onset and Worsening Heart Failure
Bibliographic record
Abstract
Abstract Background Secretion of adrenomedullin (ADM) is stimulated by volume overload to maintain endothelial barrier function, and higher levels of biologically active (bio-) ADM in heart failure (HF) are a counteracting response to vascular leakage and tissue oedema. This study aimed to establish the value of plasma bio-ADM as a marker of congestion in patients with worsening HF. Methods and results The association of plasma bio-ADM with clinical markers of congestion, as well as its prognostic value was studied in 2179 patients with new-onset or worsening HF enrolled in BIOSTAT-CHF. Data were validated in a separate cohort of 1703 patients. Patients with higher plasma bio-ADM levels were older, had more severe HF and more signs and symptoms of congestion (all P < 0.001). Amongst 20 biomarkers, bio-ADM was the strongest predictor of a clinical congestion score (r2 = 0.198). In multivariable regression analysis, higher bio-ADM was associated with higher body mass index, more oedema, and higher fibroblast growth factor 23. In hierarchical cluster analysis, bio-ADM clustered with oedema, orthopnoea, rales, hepatomegaly and jugular venous pressure. Higher bio-ADM was independently associated with impaired up-titration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers after 3 months, but not of beta-blockers. Higher bio-ADM levels were independently associated with an increased risk of all-cause mortality and HF hospitalization (hazard ratio 1.16, 95% confidence interval 1.06–1.27, P = 0.002, per log increase). Analyses in the validation cohort yielded comparable findings. Conclusions Plasma bio-ADM in patients with new-onset and worsening HF is associated with more severe HF and more oedema, orthopnoea, hepatomegaly and jugular venous pressure. We therefore postulate bio-ADM as a congestion marker, which might become useful to guide decongestive therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".