M A E de van der Schueren1, H Keller2, T Cederholm3, R Barazzoni4, C Compher5, M I T D Correia6, M C Gonzalez7, H Jager-Wittenaar8, M Pirlich9, A Steiber10, D Waitzberg11, G L Jensen12. 1. HAN University of Applied Sciences, School of Allied Health, Department of Nutrition and Health, Nijmegen, the Netherlands. Electronic address: marian.devanderschueren@han.nl. 2. Schlegel-University of Waterloo Research Institute for Aging, Canada; Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada. Electronic address: hkeller@uwaterloo.ca. 3. Clinical Nutrition and Metabolism, Dept. of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden; Theme Aging, Karolinska University Hospital, Stockholm, Sweden. Electronic address: tommy.cederholm@pubcare.uu.se. 4. Department of Medical, Surgical and Health Sciences University of Trieste, Trieste, Italy. Electronic address: barazzon@units.it. 5. Healthy Community Practices, University of Pennsylvania, School of Nursing, Biobehavioral Health Sciences Department, Claire M. Fagin Hall, 331, 418 Curie Blvd, Philadelphia, PA 19104-4217, USA. Electronic address: compherc@upenn.edu. 6. Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. Electronic address: isabeldavissoncorreia@gmail.com. 7. Post-Graduate Program in Health and Behaviour, Catholic University of Pelotas, Pelotas, RS, Brazil; Post-Graduate Program in Nutrition and Food, Federal University of Pelotas, Pelotas, RS, Brazil. Electronic address: cristinagbs@hotmail.com. 8. Hanze University of Applied Sciences, Research Group Healthy Ageing, Allied Health Care and Nursing, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Department of Maxillofacial Surgery, Groningen, the Netherlands. Electronic address: ha.jager@pl.hanze.nl. 9. Imperial Oak Outpatient Clinic (Kaisereiche), Endocrinology, Gastroenterology, Clinical Nutrition, Berlin, Germany. Electronic address: pirlich@kaisereiche.de. 10. Academy of Nutrition and Dietetics, 120 South Riverside Plaza, Suite 2190, Chicago, IL 60606, USA. Electronic address: asteiber@eatright.org. 11. Department of Gastroenterology, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil. Electronic address: dan@ganep.com.br. 12. Medicine and Nutrition, The Larner College of Medicine, University of Vermont, Burlington, VT, USA. Electronic address: Gordon.jensen@med.uvm.edu.
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 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.
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.
Authors: Carlos Serón-Arbeloa; Lorenzo Labarta-Monzón; José Puzo-Foncillas; Tomas Mallor-Bonet; Alberto Lafita-López; Néstor Bueno-Vidales; Miguel Montoro-Huguet Journal: Nutrients Date: 2022-06-09 Impact factor: 6.706