Heart Failure Association of the European Society of Cardiology Practical Guidance on the Use of Natriuretic Peptide Concentrations
Bibliographic record
Abstract
Natriuretic peptide [NP; B-type NP (BNP), N-terminal proBNP (NT-proBNP), and midregional proANP (MR-proANP)] concentrations are quantitative plasma biomarkers for the presence and severity of haemodynamic cardiac stress and heart failure (HF). End-diastolic wall stress, intracardiac filling pressures, and intracardiac volumes seem to be the dominant triggers. This paper details the most important indications for NPs and highlights 11 key principles underlying their clinical use shown below. NPs should always be used in conjunction with all other clinical information. NPs are reasonable surrogates for intracardiac volumes and filling pressures. NPs should be measured in all patients presenting with symptoms suggestive of HF such as dyspnoea and/or fatigue, as their use facilitates the early diagnosis and risk stratification of HF. NPs have very high diagnostic accuracy in discriminating HF from other causes of dyspnoea: the higher the NP, the higher the likelihood that dyspnoea is caused by HF. Optimal NP cut-off concentrations for the diagnosis of acute HF (very high filling pressures) in patients presenting to the emergency department with acute dyspnoea are higher compared with those used in the diagnosis of chronic HF in patients with dyspnoea on exertion (mild increase in filling pressures at rest). Obese patients have lower NP concentrations, mandating the use of lower cut-off concentrations (about 50% lower). In stable HF patients, but also in patients with other cardiac disorders such as myocardial infarction, valvular heart disease, atrial fibrillation or pulmonary embolism, NP concentrations have high prognostic accuracy for death and HF hospitalization. Screening with NPs for the early detection of relevant cardiac disease including left ventricular systolic dysfunction in patients with cardiovascular risk factors may help to identify patients at increased risk, therefore allowing targeted preventive measures to prevent HF. BNP, NT-proBNP and MR-proANP have comparable diagnostic and prognostic accuracy. In patients with shock, NPs cannot be used to identify cause (e.g. cardiogenic vs. septic shock), but remain prognostic. NPs cannot identify the underlying cause of HF and, therefore, if elevated, must always be used in conjunction with cardiac imaging.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.024 |
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".