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Record W4395117037 · doi:10.1002/ejhf.3257

Is it NICE to Measure Natriuretic Peptides After a Hospitalization for Heart Failure?

2024· editorial· en· W4395117037 on OpenAlexaff
Matteo Pagnesi, Marianna Adamo, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2024
Typeeditorial
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHeart failureMeasure (data warehouse)Internal medicineNatriuretic peptideCardiologyNiceData mining

Abstract

fetched live from OpenAlex

This article refers to ‘N-terminal pro-B-type natriuretic peptide post-discharge monitoring in the management of patients with heart failure and preserved ejection fraction – a randomized trial: The NICE study’ by D.A. Pascual-Figal et al., published in this issue on pages 776–784. The role of natriuretic peptides (NPs) in the diagnosis of heart failure (HF) is well established, as shown by guidelines and consensus statements.1,2 However, it is still unclear how to use these biomarkers to guide HF treatment. Indeed, recent randomized trials evaluating the usefulness of NPs for optimization of guideline-directed medical therapy (GDMT) have shown conflicting results.3–6 In this context, studies aimed at assessing the role of NPs for the management of patients with HF are extremely welcome. In this issue of the Journal, Pascual-Figal et al.7 report the results of the multicentre, prospective, open-label, randomized NICE (NT-proBNP in the Management of Discharged Patients with Acutely Decompensated Heart Failure and Preserved Ejection Fraction) trial testing the benefit of serial post-discharge N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurement after a hospitalization for HF in patients with HF with preserved ejection fraction (HFpEF). In this trial, patients hospitalized for acute HF caused by HFpEF were randomized in a 1:1 ratio at the time of discharge to a usual care arm and a NT-proBNP arm. In the latter, on top of usual care, the investigators had access to NT-proBNP concentrations at three pre-specified post-discharge clinical visits performed at 2, 4 and 12 weeks. Out of a planned sample size of 420 patients, a total of only 157 patients (37.4%) were enrolled in the study due to coronavirus disease 2019 (COVID-19) restrictions and anticipated futility at the first interim analysis. Although patients randomized to NT-proBNP-based care were treated with higher doses of diuretics and renin–angiotensin system inhibitors, the primary endpoint of HF rehospitalizations at 6 months was similar between the two groups (12.8% in the NT-proBNP arm vs. 11.4% in the control arm).7 Worsening HF may include also events not followed by patients' hospitalization.8 However, no differences were found also for other HF events, including urgent and outpatient visits. Rather unexpectedly, instead, a significantly lower risk of all-cause death at 6 months was observed in the NT-proBNP versus control arm (1 death, 1.3%, vs. 8 deaths, 10.1%; hazard ratio 0.12, 95% confidence interval 0.02–0.98). However, the low number of events in the context of a relatively small sample size, leaves the doubt that this secondary finding may be due to chance.7 Quality of life and the 6-min walking test distance also had similar changes in both treatment arms with no significant difference.7

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)0.023
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.001
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0120.002

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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2024
Admission routes1
Has abstractyes

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