Body mass indices and outcome in patients with chronic heart failure

Eur J Heart Fail. 2011 Feb;13(2):207-13. doi: 10.1093/eurjhf/hfq218. Epub 2010 Dec 7.

Abstract

Aims: There is an inverse relation between body mass and mortality in large populations of patients with chronic heart failure with a broad range of disease severity. The best measure of body size to describe the relation is not clear.

Methods and results: Patients with chronic heart failure (n = 2271, age 71.9 ± 11.3 years; 74.6% male) due to left ventricular systolic dysfunction were followed for a median of 1785 days (inter-quartile range, 874-2311 days) in survivors. We measured body mass index (BMI: weight/height²), ponderal index (PI: weight/height³), and body surface area (BSA). In a subset of 1025 patients, we also calculated the 'Charles index' [weight/(waist² × height)] together with bioimpedance data. During follow-up, 912 patients died. Measures of body mass were strong univariable predictors of outcome, and BSA (χ² = 71.3) was the strongest predictor followed by height (χ² = 68.6), weight (χ² = 57.4), then BMI (χ² = 15.2). The larger the patient's size, the lower the risk of mortality. Body surface area was the single strongest predictor of outcome in a multivariable model including 14 variables. In the subset with bioimpedance data, basal metabolic rate, BSA, weight, BMI, percentage body fat, fat mass, PI, and fat-free mass were all univariable predictors of outcome.

Conclusion: Measures of body size are strongly related to outcome in patients with chronic heart failure. Body surface area is a stronger predictor of mortality than other measures of body habitus, irrespective of height correction. The greater the overall bulk of the body, the better the survival.

Publication types

  • Comparative Study

MeSH terms

  • Body Mass Index*
  • Body Surface Area*
  • Cause of Death
  • Chronic Disease
  • Cohort Studies
  • Female
  • Heart Failure / diagnosis*
  • Heart Failure / mortality*
  • Heart Failure / therapy
  • Humans
  • Male
  • Multivariate Analysis
  • Obesity / diagnosis*
  • Obesity / mortality
  • Predictive Value of Tests
  • Prognosis
  • Sensitivity and Specificity
  • Severity of Illness Index
  • Survival Analysis