Heather Keller1,2, Marian A E de van der Schueren3, Gordon L Jensen4, Rocco Barazzoni5, Charlene Compher6, M Isabel T D Correia7, M Cristina Gonzalez8,9, Harriët Jager-Wittenaar10,11, Matthias Pirlich12, Alison Steiber13, Dan Waitzberg14, Tommy Cederholm15,16. 1. Schlegel-University of Waterloo Research Institute for Aging, Waterloo, Ontario, Canada. 2. Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada. 3. Department of Nutrition and Health, School of Allied Health, HAN University of Applied Sciences, Nijmegen, the Netherlands. 4. The Larner College of Medicine, University of Vermont, Burlington, Vermont, USA. 5. Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy. 6. Biobehavioral Health Sciences Department, School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 7. Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. 8. Post-Graduate Program in Health and Behaviour, Catholic University of Pelotas, Pelotas, Rio Grande do Sul, Brazil. 9. Post-Graduate Program in Nutrition and Food, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil. 10. Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, the Netherlands. 11. Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 12. Endocrinology, Gastroenterology, Clinical Nutrition, Imperial Oak Outpatient Clinic (Kaisereiche), Berlin, Germany. 13. Academy of Nutrition and Dietetics, Chicago, Illinois, USA. 14. Department of Gastroenterology, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil. 15. Clinical Nutrition and Metabolism, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden. 16. Theme Aging, Karolinska University Hospital, Stockholm, Sweden.
Abstract
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 healthcare 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. RESULTS: There are some aspects of GLIM that require refinement; research using large databases 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 indicators for use with the different forms of malnutrition, which the GLIM criteria were created to denote. It is noted as well that validation and reliability testing need to occur in a variety of sectors and populations and with diverse persons using GLIM criteria. CONCLUSION: The guidance presented supports the conduct and publication of quality validation and reliability studies for GLIM.
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 healthcare 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. RESULTS: There are some aspects of GLIM that require refinement; research using large databases 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 indicators for use with the different forms of malnutrition, which the GLIM criteria were created to denote. It is noted as well that validation and reliability testing need to occur in a variety of sectors and populations and with diverse persons using GLIM criteria. CONCLUSION: The guidance presented supports the conduct and publication of quality validation and reliability studies for GLIM.
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