Practical Algorithms for Early Diagnosis of Heart Failure and Heart Stress Using NT-proBNP: A Clinical Consensus Statement from the Heart Failure Association of the ESC
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Bibliographic record
Abstract
Diagnosing heart failure is often difficult due to the non-specific nature of symptoms, which can be caused by a range of medical conditions. Natriuretic peptides (NPs) have been recognized as important biomarkers for diagnosing heart failure. This document from the Heart Failure Association examines the practical uses of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in various clinical scenarios. The concentrations of NT-proBNP vary according to the patient profile and the clinical scenario, therefore values should be interpreted with caution to ensure appropriate diagnosis. Validated cut-points are provided to rule in or rule out acute heart failure in the emergency department and to diagnose de novo heart failure in the outpatient setting. We also coin the concept of 'heart stress' when NT-proBNP levels are elevated in an asymptomatic patient with risk factors for heart failure (i.e. diabetes, hypertension, coronary artery disease), underlying the development of cardiac dysfunction and further increased risk. We propose a simple acronym for healthcare professionals and patients, FIND-HF, which serves as a prompt to consider heart failure: Fatigue, Increased water accumulation, Natriuretic peptide testing, and Dyspnoea. Use of this acronym would enable the early diagnosis of heart failure. Overall, understanding and utilizing NT-proBNP levels will lead to earlier and more accurate diagnoses of heart failure ultimately improving patient outcomes and reducing healthcare costs.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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