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J Heart Lung Transplant. 2016 Jun;35(6):714-21. doi: 10.1016/j.healun.2016.01.016. Epub 2016 Jan 15.

From statistical significance to clinical relevance: A simple algorithm to integrate brain natriuretic peptide and the Seattle Heart Failure Model for risk stratification in heart failure.

Author information

1
Department of Cardiovascular Diseases, Mayo Clinic and Foundation, Rochester, Minnesota, USA; Mayo Graduate School, Mayo Clinic and Foundation, Rochester, Minnesota, USA.
2
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
3
Mayo Medical School, Rochester, Minnesota, USA.
4
Department of Cardiovascular Diseases, Mayo Clinic and Foundation, Rochester, Minnesota, USA; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
5
Division of Cardiology, University of Washington, Seattle, Washington, USA.
6
Division of Cardiovascular Medicine, University Hospital, Salt Lake City, Utah, USA.
7
Division of Cardiology, University of Arizona, Tucson, Arizona, USA.
8
Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
9
Department of Cardiovascular Diseases, Mayo Clinic and Foundation, Rochester, Minnesota, USA. Electronic address: redfield.margaret@mayo.edu.

Abstract

BACKGROUND:

Heart failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk scores, such as the Seattle Heart Failure Model (SHFM), to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these 2 prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm.

METHODS:

The retrospective derivation cohort included consecutive patients with HFrEF at the Mayo Clinic. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (≤2.5%, 2.6% to ≤10%, >10%) according to BNP above or below 700 pg/ml and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study).

RESULTS:

Derivation (n = 441; 1-year event rate 17%) and validation (n = 1,513; 1-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function and higher BNP and SHFM scores. In both cohorts, across the 3 SHFM-predicted risk strata, a BNP >700 pg/ml consistently identified patients with approximately 3-fold the risk that the SHFM would have otherwise estimated, regardless of stage of HF, intensity and duration of HF therapy and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP <700 pg/ml.

CONCLUSION:

The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decision-making regarding HF therapeutic or palliative strategies.

KEYWORDS:

Seattle Heart Failure Model; biomarkers; heart failure; natriuretic peptides; prognosis; risk stratification

PMID:
27021278
PMCID:
PMC4917454
DOI:
10.1016/j.healun.2016.01.016
[Indexed for MEDLINE]
Free PMC Article

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