Obesity, Acute Kidney Injury, and Mortality in Critical Illness
Name
nihms719585.pdf
Size
331.31 KB
Format
Adobe PDF
Checksum (MD5)
2e58064ac69b231844c2718bf0104e10
Author(s)
Danziger, John
Chen, Ken P.
Mukamal, Kenneth J.
Lee, Joonwu
Feng, Mengling
Celi, Leo Anthony G.
Mark, Roger G
Date Issued
February 2016
Journal
Critical Care Medicine
Publisher
Wolters Kluwer - Lippincott Williams & Wilkins
Citation
Danziger, John et al. “Obesity, Acute Kidney Injury, and Mortality in Critical Illness.” Critical Care Medicine 44, 2 (February 2016): 328–334 © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc
Version
Author's final manuscript
Abstract
Objectives: Although obesity is associated with risk for chronic kidney disease and improved survival, less is known about the associations of obesity with risk of acute kidney injury and post acute kidney injury mortality. Design: In a single-center inception cohort of almost 15,000 critically ill patients, we evaluated the association of obesity with acute kidney injury and acute kidney injury severity, as well as in-hospital and 1-year survival. Acute kidney injury was defined using the Kidney Disease Outcome Quality Initiative criteria. Measurements and Main Results: The acute kidney injury prevalence rates for normal, overweight, class I, II, and III obesity were 18.6%, 20.6%, 22.5%, 24.3%, and 24.0%, respectively, and the adjusted odds ratios of acute kidney injury were 1.18 (95% CI, 1.06-1.31), 1.35 (1.19-1.53), 1.47 (1.25-1.73), and 1.59 (1.31-1.87) when compared with normal weight, respectively. Each 5-kg/m 2 increase in body mass index was associated with a 10% risk (95% CI, 1.06-1.24; p < 0.001) of more severe acute kidney injury. Within-hospital and 1-year survival rates associated with the acute kidney injury episodes were similar across body mass index categories. Conclusion: Obesity is a risk factor for acute kidney injury, which is associated with increased short-and long-term mortality.
MIT Department
Institute for Medical Engineering and Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1097/CCM.0000000000001398
