2021, Article / Letter to editor (Clinical Nutrition, vol. 40, iss. 3, (2021), pp. 690-701)Background & aims: The year 2019 marked the centenary of the publication of the Harris and Benedict equations for estimation of energy expenditure. In October 2019 a Scientific Symposium was organized by the European Society for Clinical Nutrition and Metabolism (ESPEN) in Vienna, Austria, to celebrate this historical landmark, looking at what is currently known about the estimation and measurement of energy expenditure. Methods: Current evidence was discussed during the symposium, including the scientific basis and clinical knowledge, and is summarized here to assist with the estimation and measurement of energy requirements that later translate into energy prescription. Results: In most clinical settings, the majority of predictive equations have low to moderate performance, with the best generally reaching an accuracy of no more than 70%, and often lead to large errors in estimating the true needs of patients. Generally speaking, the addition of body composition measurements did not add to the accuracy of predictive equations. Indirect calorimetry is the most reliable method to measure energy expenditure and guide energy prescription, but carries inherent limitations, greatly restricting its use in real life clinical practice. Conclusions: While the limitations of predictive equations are clear, their use is still the mainstay in clinical practice. It is imperative to recognize specific patient populations for whom a specific equation should be preferred. When available, the use of indirect calorimetry is advised in a variety of clinical settings, aiming to avoid under-as well as overfeeding. (C)2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
2020, Article / Letter to editor (Jpen, Journal of Parenteral and Enteral Nutrition, vol. 44, iss. 6, (2020), pp. 992-1003)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.
2020, Article / Letter to editor (Clinical Nutrition, vol. 39, iss. 9, (2020), pp. 2872-2880)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. (c) 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism and American Society for Parenteral and Enteral Nutrition [Published by Wiley]. All rights reserved.