Global Leadership Initiative on Malnutrition (GLIM): Guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: de van der Schueren, M.A.E.; Keller, H.; Cederholm, T.; Barazzoni, R.; Compher, C.; Correia, M.I.T.D.; Gonzalez, M.C.; Jager-Wittenaar, H.; Pirlich, M.; Steiber, A.; Waitzberg, D.; Jensen, G.L.
• Is Part Of: Clinical nutrition. Volume 39:Issue 9(2020)
• Page Start: 2872
• Page End: 2880
• Publication Date: 2020-09
• Subjects: Adult -- Nutrition assessment -- Outcomes research/quality; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2019.12.022
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background: The Global Leadership Initiative on Malnutrition (GLIM) created a consensus-based framework consisting of phenotypic and etiologic criteria to record the occurrence of malnutrition in adults. This is a minimum set of practicable indicators for use in characterizing a patient/client as malnourished, considering the global variations in screening and nutrition assessment, and to be used across different health care settings. As with other consensus-based frameworks for diagnosing disease states, these operational criteria require validation and reliability testing as they are currently based solely on expert opinion. Methods: Several forms of validation and reliability are reviewed in the context of GLIM, providing guidance on how to conduct retrospective and prospective studies for criterion and construct validity. Findings: There are some aspects of GLIM criteria which require refinement; research using large data bases can be employed to reach this goal. Machine learning is also introduced as a potential method to support identification of the best cut-points and combinations of operational criteria for use with the different forms of malnutrition, which the GLIM criteria were created to denote. It is noted as well that the validation and reliability testing need to occur in a variety of sectors, populations and with diverse persons completing the criteria. Conclusion: The guidance presented supports the conduct and publication of quality validation and reliability studies for GLIM.