Impact of renal dysfunction on the Seattle Heart Failure Model

Kairav P. Vakil, Todd Dardas, Sunil Dhar, Alec Moorman, Inder Anand, Aldo Maggioni, David T. Linker, Dariush Mozaffarian, Wayne C. Levy

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background Renal dysfunction (RD) is a strong predictor of mortality in patients with heart failure (HF). However, its impact on the discrimination of the Seattle Heart Failure Model (SHFM) is poorly understood. Methods Serum creatinine (SCr) and creatinine clearance (CrCl) were reviewed for patients from four of the six cohorts originally used to derive and validate the SHFM. Patients were followed for death. The independent prediction of adding SCr or CrCl to the SHFM was assessed using multivariable Cox proportional hazards and the incremental value for prediction by changes in the ROC curves for 1- and 2-year event prediction. Results Among 7,146 patients (mean age 63 ± 11 years), 1,511 deaths occurred during a mean follow-up of 2.04 years. SCr and CrCl had a modest positive correlation with SHFM (r = 0.30, p = 0.002). In combination with SHFM, SCr (hazard ratio [HR] per mg/dl 1.25, 95% CI 1.13 to 1.38, p < 0.0001) and CrCl (HR per 10 ml/min 0.95, 95% CI 0.93 to 0.97, p < 0.0001) were both multivariable predictors of events. When stratified by absolute risk based on the SHFM, SCr or CrCl provided more additional information in lower risk patients and less or no additional information in higher risk patients. The addition of SCr and the SHFM*SCr, or CrCl and the SHFM*CrCl interaction to the SHFM was associated with almost no change in the 1- and 2-year area under ROC curves for the SHFM score. Conclusions Compared with the SHFM alone, RD is independently predictive of mortality only in lower risk patients. Overall discrimination is only minimally improved with addition of SCr or CrCl to the SHFM.

Original languageEnglish (US)
Pages (from-to)163-169
Number of pages7
JournalJournal of Heart and Lung Transplantation
Volume33
Issue number2
DOIs
StatePublished - Feb 1 2014

Keywords

  • Seattle Heart Failure Model
  • chronic kidney disease
  • heart failure
  • risk prediction

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