Bio-Adrenomedullin as a Marker of Congestion in Patients with New-Onset and Worsening Heart Failure
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it