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Record W4399676925 · doi:10.1002/ejhf.3336

Biologically Active Adrenomedullin as a Marker for Residual Congestion and Early Rehospitalization in Patients Hospitalized for Acute Heart Failure: Data from STRONG-HF

2024· article· en· W4399676925 on OpenAlexaff
Geert H.D. Voordes, Beth A. Davison, Jan Biegus, Chris Edwards, Kevin Damman, Jozine Ter Maaten, Alexandre Mebazaa, Koji Takagi, Marianna Adamo, Andrew P. Ambrosy, Mattia Arrigo, Marianela Barros, Jelena Čelutkienė, Kamilė Čerlinskaitė‐Bajorė, Ovidiu Chioncel, Alain Cohen‐Solal, Albertino Damasceno, Benjamin Deniau, Rafael Dı́az, Gerasimos Filippatos, Étienne Gayat, Antoine Kimmoun, Carolyn S.P. Lam, Marco Metra, Maria Novosadova, Matteo Pagnesi, Peter S. Pang, Piotr Ponikowski, Hadiza Saidu, Karen Sliwa, Daniela Tomasoni, Gad Cotter, Adriaan A. Voors

Bibliographic record

VenueEuropean Journal of Heart Failure · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHazard ratioAdrenomedullinHeart failureNatriuretic peptideConfidence intervalInternal medicineCardiologyUrologyReceptor

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.290
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2024
Admission routes1
Has abstractyes

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