Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2003 Jun;22(3):235-9.
doi: 10.1016/s0261-5614(02)00215-7.

The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis

Affiliations

The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis

M Isabel T D Correia et al. Clin Nutr. 2003 Jun.

Abstract

Malnutrition has been identified as affecting patient outcome. The purpose of this study was to correlate the nutritional status of hospitalized patients with their morbidity, mortality, length of hospital stay and costs. The patients were nutritionally assessed within the first 72 h of hospital admission. The patients' charts were surveyed on the incidence of complications and mortality. Hospital costs were calculated based on economic tables used by insurance companies. Multivariate logistic regression analysis and the Cox regression model were used to identify possible confounding factors. A P<0.05 was considered statistically significant. The mean age was 50.6+/-17.3 years with 50.2% being male. The incidence of complications in the malnourished was 27.0% [Relative risk (RR)=1.60]. Mortality in the malnourished patients was 12.4% vs 4.7% in the well nourished (RR = 2.63). Malnourished patients stayed in the hospital for 16.7+/-24.5 days vs 10.1+/-11.7 days in the nourished. Hospital costs in malnourished patients were increased up to 308.9%. It was concluded that malnutrition, as analyzed by a multivariate logistic regression model, is an independent risk factor impacting on higher complications and increased mortality, length of hospital stay and costs.

PubMed Disclaimer

Comment in

Similar articles

Cited by

Publication types

LinkOut - more resources