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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

https://doi.org/10.1016/j.healun.2016.01.016Get rights and content

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.

Section snippets

Derivation cohort

We identified a retrospectively compiled cohort of consecutive HF patients seen at Mayo outpatient clinics and associated hospitals in Rochester, Minnesota, from July 1, 2007 through December 31, 2007, a time-frame intentionally chosen to pre-date the era of widespread LVAD referrals. Using a modification of a previously described natural language processing program,20 all electronic clinical notes were searched for non-negated terms (refer to Table S1 in Supplementary material available online

Baseline characteristics and outcomes

In the derivation cohort, of the 441 consecutive patients identified, 268 (61%) were community patients (residing within 100 miles of center) and 308 (70%), 101 (23%) and 32 (7%) were seen in cardiovascular, non-cardiovascular sub-specialty and primary care settings, respectively. In the validation cohort, 1,513 patients were included. Baseline characteristics from each cohort are shown in Table 1. Missing data were rare, with 1 patient having no creatinine level, 12 having no NYHA

Discussion

In a single-center, retrospectively defined, derivation cohort of patients with HFrEF seen across a spectrum of care environments and providers, application of a simple SHFM+BNP risk stratification algorithm revealed that, regardless of patients’ SHFM predicted category of risk, a BNP ≥700 pg/ml identifies patients with ≥2-fold risk of mortality than the SHFM would suggest , whereas a BNP <700 pg/ml identifies patients in whom the SHFM risk is well calibrated. In an external, prospective,

Limitations

Our study should be interpreted in the context of inherent methodological limitations of retrospective studies. NYHA, comorbidities and medications were obtained by individualized chart review and were limited by care providers’ documentation. Missing values were imputed for calculation of composite scores. Other studies validating composite risk scores have been retrospective and had similar rates of missing data that had to be imputed.5 The similar performance in a prospectively enrolled

Disclosure statement

T.P.C. has received support for running biomarker assays from Abbott Diagnostics. A.S.J. has or presently consults for most of the major diagnostic companies, including those whose assays are used in this analysis. The remaining authors have no conflicts of interest to disclose. O.F.A. was supported by the National Institutes of Health (Training Grant T-91160), and support for his mentorship was provided by the Heart Failure Clinical Research Network Skills Development Core (HL 84907).

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