HAN University of Applied Sciences, School of Allied Health, Department of Nutrition and Health, Nijmegen, the Netherlands.
Schlegel-University of Waterloo Research Institute for Aging, Canada; Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada.
Clin Nutr. 2020 Sep;39(9):2872-2880. doi: 10.1016/j.clnu.2019.12.022. Epub 2020 Jun 11.
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.
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.
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.
The guidance presented supports the conduct and publication of quality validation and reliability studies for GLIM.
全球营养不良领导倡议(GLIM)制定了一个基于共识的框架,包括表型和病因标准,以记录成年人营养不良的发生。这是一组用于描述患者/客户营养不良的实用指标,考虑到全球筛查和营养评估的差异,并在不同的医疗保健环境中使用。与其他用于诊断疾病状态的基于共识的框架一样,这些操作标准需要验证和可靠性测试,因为它们目前仅基于专家意见。
本文回顾了 GLIM 背景下的几种验证和可靠性方法,提供了如何进行回顾性和前瞻性研究以评估标准和结构有效性的指导。
GLIM 标准中有一些需要改进的方面;可以使用大型数据库研究来实现这一目标。机器学习也被引入作为一种潜在的方法,以支持确定用于不同形式营养不良的最佳操作标准的截止值和组合,GLIM 标准旨在表示这些形式的营养不良。还需要注意的是,验证和可靠性测试需要在不同的部门、人群和不同的人进行,以完成标准。
本文提供的指导支持了 GLIM 的质量验证和可靠性研究的进行和发表。