Identifying predictors of high sodium excretion in patients with heart failure: A mixed effect analysis of longitudinal data

Ruth Masterson Creber, Maxim Topaz, Terry A. Lennie, Christopher S. Lee, Houry Puzantian, Barbara Riegel

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: A low-sodium diet is a core component of heart failure self-care but patients have difficulty following the diet. Aim: The aim of this study was to identify predictors of higher than recommended sodium excretion among patients with heart failure.

Methods: The World Health Organization Five Dimensions of Adherence model was used to guide analysis of existing data collected from a prospective, longitudinal study of 280 community-dwelling adults with previously or currently symptomatic heart failure. Sodium excretion was measured objectively using 24-hour urine sodium measured at three time points over six months. A mixed effect logistic model identified predictors of higher than recommended sodium excretion.

Results: The adjusted odds of higher sodium excretion were 2.90, (95% confidence interval (CI): 1.15-4.25, p<0.001) for patients who were obese; 2.80 (95% CI: 1.33-5.89, p=0.007) for patients with diabetes; and 2.22 (95% CI: 1.09-4.53, p=0.028) for patients who were cognitively intact.

Conclusion: Three factors were associated with excess sodium excretion and two factors, obesity and diabetes, are modifiable by changing dietary food patterns.

Original languageEnglish (US)
Pages (from-to)549-558
Number of pages10
JournalEuropean Journal of Cardiovascular Nursing
Volume13
Issue number6
DOIs
StatePublished - Dec 24 2014

Keywords

  • Heart failure
  • diet
  • diet therapy
  • sodium-restricted
  • urine sodium

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Medical–Surgical
  • Advanced and Specialized Nursing

Fingerprint Dive into the research topics of 'Identifying predictors of high sodium excretion in patients with heart failure: A mixed effect analysis of longitudinal data'. Together they form a unique fingerprint.

Cite this