Biologically Active Adrenomedullin as a Marker for Residual Congestion and Early Rehospitalization in Patients Hospitalized for Acute Heart Failure: Data from STRONG-HF
Bibliographic record
Abstract
AIMS: Biologically active adrenomedullin (bio-ADM) is a promising marker of residual congestion. The STRONG-HF trial showed that high-intensity care (HIC) of guideline-directed medical therapy (GDMT) improved congestion and clinical outcomes in heart failure (HF) patients. The association between bio-ADM, decongestion, outcomes and the effect size of HIC of GDMT remains to be elucidated. METHODS AND RESULTS: We measured plasma bio-ADM concentrations in 1005 patients within 2 days prior to anticipated discharge (baseline) and 90 days later. Bio-ADM correlated with most signs of congestion, with the exception of rales. Changes in bio-ADM were strongly correlated with change in congestion status from baseline to day 90 (gamma -0.24; p = 0.0001). Patients in the highest tertile of baseline bio-ADM concentrations were at greater risk than patients in the lowest tertile for the primary outcome of 180-day all-cause mortality or HF rehospitalization (hazard ratio [HR] 2.14, 95% confidence interval [CI] 1.42-3.22) and 180-day HF rehospitalization (HR 2.33, 95% CI 1.38-3.94). Areas under the receiver-operating characteristic curves were 0.5977 (95% CI 0.5561-0.6393), 0.5800 (95% CI 0.5356-0.6243), and 0.6159 (95% CI 0.5711-0.6607) for bio-ADM, N-terminal pro-B-type natriuretic peptide (NT-proBNP) and their combination, respectively, suggesting that both bio-ADM and NT-proBNP provided similarly modest discrimination for this outcome. A trend towards better discrimination by combined bio-ADM and NT-proBNP than NT-proBNP alone was found (p = 0.059). HIC improved the primary outcome, irrespective of baseline bio-ADM concentration (interaction p = 0.37). In contrast to NT-proBNP, the 90-day change in bio-ADM did not differ significantly between HIC and usual care. CONCLUSIONS: Bio-ADM is a marker of congestion and predicts congestion at 3 months after a HF hospitalization. Higher bio-ADM was modestly associated with a higher risk of death and early hospital readmission and may have added value when combined with NT-proBNP.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".