Why the study?
Diagnosing heart failure is often difficult due to non-specific symptoms, and NT-proBNP concentrations vary according to patient profile and clinical scenario requiring cautious interpretation.
This consensus statement provides practical algorithms and validated cut-points for using NT-proBNP to diagnose heart failure and introduces the FIND-HF acronym to promote early detection.
Answers are grounded in this paper and cite their sources.
May support NT-proBNP use for early HF detection; leaves open prospective validation of 'heart stress' and FIND-HF.
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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Bayés‐Genís et al. (2023) studied this question.
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