The impact of obesity on long-term survival after coronary artery bypass grafting

J Surg Res. 2010 Sep;163(1):7-11. doi: 10.1016/j.jss.2010.02.014. Epub 2010 Mar 10.

Abstract

Background: Obesity is a well-known risk factor for coronary artery disease. The objective of our study was to examine the impact of obesity on long-term survival after coronary artery bypass grafting (CABG).

Materials and methods: Using prospectively gathered data, we reviewed records of 1163 consecutive patients who underwent isolated primary CABG between 1997 and 2007. We compared outcomes of obese patients (body mass index [BMI] > or = 30 kg/m(2); n = 472) and non-obese patients (BMI < 30 kg/m(2); n = 691). Long-term survival was assessed by using Kaplan-Meier curves generated by log-rank tests and adjusted for confounding factors with Cox logistic regression analysis.

Results: Obese patients were slightly younger (60 +/- 8 versus 63 +/- 9y; P < 0.0001), were less likely to be current tobacco smokers (30% versus 41%; P < 0.0001), had a higher incidence of diabetes (51% versus 33%; P < 0.0001), and had a lower incidence of cerebral vascular disease (18% versus 24%; P = 0.009) than non-obese patients. The two groups of patients had similar 30-d rates of mortality (1.3% versus 1.5%; P = 0.8) and major adverse cardiac events (2.3% versus 2.5%; P = 0.9). Adjusted Cox regression survival curves were also similar between the two groups of patients (adjusted hazard ratio, 1.2; 95% confidence interval, 0.8-1.8; P = 0.28).

Conclusions: Obese patients who underwent CABG had 30-d mortality rates and early outcomes similar to those of non-obese patients. Long-term survival was also similar between these two groups of patients after adjustment for confounding variables.

MeSH terms

  • Aged
  • Coronary Artery Bypass / mortality*
  • Coronary Artery Disease / complications
  • Coronary Artery Disease / mortality*
  • Coronary Artery Disease / surgery
  • Female
  • Humans
  • Male
  • Middle Aged
  • Obesity / complications
  • Obesity / mortality*
  • Proportional Hazards Models
  • Retrospective Studies
  • Texas / epidemiology