Assessing nutrition in the critically ill elderly patient: A comparison of two screening tools

JOURNAL TITLE: Indian Journal of Critical Care Medicine

Author
1. J.C. Mishra
ISSN
0972-5229
DOI
10.4103/0972-5229.164798
Volume
19
Issue
9
Publishing Year
2015
Pages
5
Author Affiliations
    1. Department of Anaesthesia, Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha, India
  • Article keywords
    Critical care outcome, elderly critically ill, malnutrition, nutrition screening tools

    Abstract

    Context: Few malnutrition screening tests are validated in the elderly Intensive Care Unit (ICU) patient. Aim: Having previously established malnutrition as a cause of higher mortality in this population, we compared two screening tools in elderly patients. Subjects and Methods: For this prospective study, 111 consecutive patients admitted to the ICU and > 65 years underwent the Malnutrition Universal Screening Tool (MUST), and the Geriatric Nutrition Risk Index (GNRI) screening tests. Statistical Analysis: Standard definition of malnutrition risk was taken as the gold standard to evaluate the sensitivity, specificity and predictive values of the tools. The k statistic was calculated to measure the agreement between the tools. The Shrout classification was used to interpret its values. Results: The mean age of the patients screened was 74.7 ± 8.4 (65-97 years). The standard definition, MUST and GNRI identified 52.2%, 65.4%, and 64.9% to be malnourished, respectively. The sensitivity and specificity of the tests were 96.5% computed tomography (CI) (87.9-99.5%) and 72.3% CI (57.5-84.5%) for MUST and 89.5% CI (75.2-96.7%) and 55.0% CI (75.2-96.9%) for GNRI, respectively. Screening was not possible by GNRI and MUST tool in 31% versus 4% of patients, respectively. The agreement between the tools was moderate for Standard-MUST k = 0.65 and MUST-GNRI k = 0.60 and fair for Standard-GNRI k = 0.43. Conclusions: The risk of malnutrition is high among our patients as identified by all the tools. Both GNRI and MUST showed a high sensitivity with MUST showing a higher specificity and greater applicability.

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