ED physicians are only about 80% accurate in their diagnosis of acute CHF. Why? There is no single element of past medical history, presenting symptoms or physical exam findings that can reliably rule in or rule out acute CHF in the ED. Orthopnea, PND and weight gain are not especially helpful in making the diagnosis and even the lauded S3 gallop which most of us cannot identify on the best of ED shifts is not that helpful. The classic signs of CHF are often absent on CXR and interobserver agreement (whether you are an ED doc or a radiologist) on the diagnosis of CHF by CXR is enormous. Nonetheless, when all of these elements are put together, ED physician clinical gestalt is not bad at diagnosing CHF. But we could do better.

Enter BNP. BNP is currently in use in many EDs across North America and Europe. In this Journal Jam podcast we discuss the clinical utility of BNP and pro-NT-BNP in the work-up of the dyspneic ED patient. We ask the questions: does BNP add much beyond physician gestalt? Which patients might BNP be useful for? Should we abandon BNP as a dichotomous rule-in/rule-out variable and instead use it as a continuous variable? Does using BNP effect patient oriented outcomes? Is lung POCUS a better test? Are prediction models that include BNP useful? and many more….

Podcast production by Anton Helman, Justin Morgenstern & Rory Spiegel. Editing and sound design by Anton Helman, March 2018.

Blog post by Anton Helman

Cite this podcast as: Helman, A, Morgenstern, J, Spiegel, R. BNP for Diagnosis of Acute CHF. Emergency Medicine Cases. March, 2018. https://emergencymedicinecases.com/epinephrine-cardiac-arrest/. Accessed [date].

BNP is a vasoactive hormone that is released by strained myocardium from a variety of causes. Since its discovery about 30 years ago, BNP has been utilized as a biomarker for the diagnosis and prognosis of acute heart failure both in the ED and the inpatient setting. Many factors potentially alter BNP:  age, kidney disease, obesity, hypertension, coronary artery disease, atrial fibrillation, and chronic respiratory disease. Many of the patients in which we have diagnostic uncertainty about CHF are older, have renal disease, are obese, hypertensive with respiratory or other cardiac disease. Nonetheless, there have been several observational studies and RCTs examining the role of BNP in the ED as a diagnostic aid and there remains controversy as to its utility. This Journal Jam podcast reviews these studies and suggests a clinical bottom line.


EBM topic highlighted in this Journal Jam: Spectrum Bias

Spectrum bias or case-mix bias is the inherent variability when performing a diagnostic test in different clinical scenarios. This is because in each clinical scenario you have a different case-mix or population of patients. As such, spectrum bias can be classified as a type of sampling bias.
Tests unfortunately don’t perform the same along the spectrum of disease or across all populations. A tests accuracy depends on the prevalence and severity of the disease within the study population. If you have a discrepancy between the study population and the population you are looking at, the test will be inaccurate due to spectrum bias. Using a test in a cohort of patients that don’t have the disease in question – well that’ll inflate the sensitivity. If you test a population that obviously have the disease, then you’ll inflate the specificity.
This has all to do of where patients lie on the disease spectrum. If you are looking at urine infections, a urinalysis will look a lot more sensitive if you only test young healthy males. On the other hand, it will look a lot more specific if you only test women coming in for a chief complaint of a typical UTI. In this case, it’s important to know what mix of patients the study is meant for. It’s important to contextualize the study population. For example studies done on inpatients may not apply to your ED population.  It’s always vital to keep that in mind when reading a paper so you know how this will apply to your clinical practice.


Brian Steinhart, lead author for GASP4Ar study describes the challenges of acute heart failure and BNP research and provides a different perspective on the literature in a short podcast here


Guest Peer Review of GASP4Ar Study by Chris Carpenter

Chris Carpenter MD, MSc, FACEP, FAAEM, AGSF,Emergency Physician, Barnes-Jewish Hospital
Associate Professor, Emergency Medicine, Washington University School of Medicine
Director, Evidence Based Medicine, Washington University School of Medicine

Distinguishing CHF from other causes of acute dyspnea in the decision-dense ED is sometimes challenging.  This diagnostic randomized controlled trial evaluates whether a decision aid using age, clinician gestalt, and NT-proBNP can improve diagnostic accuracy or alter measurable processes of care.

These investigators previously validated a 3-item (age, clinician pre-test probability, NT-proBNP) logarithmic equation designed to generate the probability that an acute dyspnea patient’s symptoms resulted from CHF. In this 4-site study, they now randomize clinicians to be informed of the instrument-based CHF probability or not after initial history/physical exam + CXR + EKG.  The primary outcome assessed was diagnostic accuracy and secondary outcomes included operational flow metrics (ED length of stay, ICU admission) and 60 day mortality.  Notably, these patients did not include the “slam dunk” subset of patients in whom clinicians are certain do not have CHF (clear pneumonia or COPD exacerbation) or definitely have CHF (peripheral edema, pulmonary edema, orthopnea) – eligible subjects had a pre-test probability b/w 20%-80% representing the indeterminate diagnostic zone with which clinicians struggle in making treatment and disposition decisions!

These patients are typical dyspnea patients evaluated in the ED.  Half had a history of CHF and half a history of COPD. With the receiver operating characteristic area under the curve of 0.93, the instrument displays excellent ability to distinguish those with CHF from those without.  The million dollar question, though, is does the instrument augment clinician’s gestalt?  Answer: There was no difference in clinicians’ diagnostic accuracy between those who were informed (77%) and those not informed (74%).  In addition, no clinically significant differences were observed in the ED length of stay or ICU admission rates.  The authors hypothesize that if the model thresholds of <20% or >80% probabilities had been used to define the presence of absence of acute heart failure that 48% of patients would have been redirected (alternative disposition or management decisions) with 95% accuracy, but that is not what occurred because clinicians rely upon myriad factors when determining management and disposition decisions for undifferentiated dyspnea patients including likelihood of alternative diagnoses, patient and family angst, patient goals of care, access to timely follow-up, referring consultants’ priorities and expectations, and malpractice concerns in the zero miss mentality of some healthcare systems.

Distinguishing CHF from other causes of acute dyspnea (COPD, VTE, deconditioning) remains a challenge for some patients.  This model is not a panacea to overcome diagnostic hurdles, but may provide a reasonable risk stratification instrument to facilitate shared decision making with patients and referring providers.  More diagnostic accuracy studies in different populations are still needed to assure that the performance of the model holds up across different populations.  An additional uncertainty is how hospitals not using NT-proBNP could use this model, or how to adjust the model for assay differences between different NT-proBNP machines as the authors did in this study?  If additional accuracy studies in different settings confirm the accuracy of this model and skeptics are subsequently convinced that the model supplements clinical gestalt, implementation science will be necessary to convert knowledge into bedside application as opposed to simply educating clinicians that the model exists.




Articles discussed in this Journal Jam podcast on BNP for CHF


Martindale JL, Wakai A, Collins SP, et al. Diagnosing Acute Heart Failure in the Emergency Department: A Systematic Review and Meta-analysis. Acad Emerg Med. 2016;23(3):223-42.

Maisel AS, Krishnaswamy P, Nowak RM. Rapid measurement of B- type natriuretic peptide in the emergency diagnosis of heart failure. The New England journal of medicine. 2002; 347(3):161-7.

McCullough PA, Nowak RM, McCord J. B-type natriuretic peptide and clinical judgment in emergency diagnosis of heart failure: analysis from Breathing Not Properly (BNP) Multinational Study. Circulation. 2002; 106(4):416-22.

Maisel A, Hollander JE, Guss D. Primary results of the Rapid Emergency Department Heart Failure Outpatient Trial (REDHOT). A multicenter study of B-type natriuretic peptide levels, emergency department decision making, and outcomes in patients presenting with shortness of breath. Journal of the American College of Cardiology. 2004; 44(6):1328-33.

Januzzi JL, Camargo CA, Anwaruddin S. The N-terminal Pro-BNP investigation of dyspnea in the emergency department (PRIDE) study. The American journal of cardiology. 2005; 95(8):948-54.

Martindale JL, Wakai A, Collins SP. Diagnosing Acute Heart Failure in the Emergency Department: A Systematic Review and Meta- analysis. Academic emergency medicine. 2016; 23(3):223-42.

Mueller C, Scholer A, Laule-Kilian K. Use of B-type natriuretic peptide in the evaluation and management of acute dyspnea. The New England journal of medicine. 2004; 350(7):647-54.

Moe GW, Howlett J, Januzzi JL, Zowall H. N-terminal pro-B-type natriuretic peptide testing improves the management of patients with suspected acute heart failure: primary results of the Canadian prospective randomized multicenter IMPROVE-CHF study. Circulation. 2007; 115(24):3103-10.

Rutten JH, Steyerberg EW, Boomsma F. N-terminal pro-brain natriuretic peptide testing in the emergency department: beneficial effects on hospitalization, costs, and outcome. American heart journal. 2008; 156(1):71-7.

Schneider HG, Lam L, Lokuge A. B-type natriuretic peptide testing, clinical outcomes, and health services use in emergency department patients with dyspnea: a randomized trial. Annals of internal medicine. 2009; 150(6):365-71.

Singer AJ, Birkhahn RH, Guss D. Rapid Emergency Department Heart Failure Outpatients Trial (REDHOT II): a randomized controlled trial of the effect of serial B-type natriuretic peptide testing on patient management. Circulation. Heart failure. 2009; 2(4):287-93.

Boldanova T, Noveanu M, Breidthardt T. Impact of history of heart failure on diagnostic and prognostic value of BNP: results from the B-type Natriuretic Peptide for Acute Shortness of Breath Evaluation (BASEL) study. International journal of cardiology. 2010; 142(3):265-72.

Meisel SR, Januzzi JL, Medvedovski M. Pre-admission NT-proBNP improves diagnostic yield and risk stratification – the NT-proBNP for EValuation of dyspnoeic patients in the Emergency Room and hospital (BNP4EVER) study. European heart journal. Acute cardiovascular care. 2012; 1(2):99-108.

Al Deeb M, Barbic S, Featherstone R, Dankoff J, Barbic D. Point-of-care ultrasonography for the diagnosis of acute cardiogenic pulmonary edema in patients presenting with acute dyspnea: a systematic review and meta-analysis. Acad Emerg Med 2014;21:843–52.

Steinhart B, Thorpe KE, Bayoumi AM, Moe G, Januzzi JL Jr, Mazer CD. Improving the diagnosis of acute heart failure using a validated prediction model. J Am Coll Cardiol 2009;54:1515–21.

Steinhart BD, Levy P, Vandenberghe H, et al. A Randomized Control Trial Using a Validated Prediction Model for Diagnosing Acute Heart Failure in Undifferentiated Dyspneic Emergency Department Patients-Results of the GASP4Ar Study. J Card Fail. 2017;23(2):145-152.


Drs. Helman, Morgenstern and Spiegel have no conflicts of interest to declare. Dr. Steinhart’s study was partially funded by industry however he did not receive any payment from industry directly.


Other FOAMed Resources on BNP and POCUS in Acute CHF