Comparison of the prevalence of 21 GLIM phenotypic and etiologic criteria combinations and association with 30-day outcomes in people with cancer: A retrospective observational study.
Cancer
GLIM
Malnutrition
Validation
Journal
Clinical nutrition (Edinburgh, Scotland)
ISSN: 1532-1983
Titre abrégé: Clin Nutr
Pays: England
ID NLM: 8309603
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
received:
10
10
2021
revised:
17
03
2022
accepted:
20
03
2022
pubmed:
13
4
2022
medline:
3
5
2022
entrez:
12
4
2022
Statut:
ppublish
Résumé
The Global Leadership Initiative on Malnutrition (GLIM) criteria require validation in various clinical populations. This study determined the prevalence of malnutrition in people with cancer using all possible diagnostic combinations of GLIM etiologic and phenotypic criteria and determined the combinations that best predicted mortality and unplanned hospital admission within 30 days. The GLIM criteria were applied, in a cohort of participants from two cancer malnutrition point prevalence studies (N = 2801), using 21 combinations of the phenotypic (≥5% unintentional weight loss, body mass index [BMI], subjective assessment of muscle stores [from PG-SGA]) and etiologic (reduced food intake, inflammation [using metastatic disease as a proxy]) criteria. Machine learning approaches were applied to predict 30-day mortality and unplanned admission. We analysed 2492 participants after excluding those with missing data. Overall, 19% (n = 485) of participants were malnourished. The most common GLIM combinations were weight loss and reduced food intake (15%, n = 376), and low muscle mass and reduced food intake (12%, n = 298). Machine learning models demonstrated malnutrition diagnosis by weight loss and reduced muscle mass plus either reduced food intake or inflammation were the most important combinations to predict mortality at 30-days (accuracy 88%). Malnutrition diagnosis by weight loss or reduced muscle mass plus reduced food intake was most important for predicting unplanned admission within 30-days (accuracy 77%). Machine learning demonstrated that the phenotypic criteria of weight loss and reduced muscle mass combined with either etiologic criteria were important for predicting mortality. In contrast, the etiologic criteria of reduced food intake in combination with weight loss or reduced muscle mass was important for predicting unplanned admission. Understanding the phenotypic and etiologic criteria contributing to the GLIM diagnosis is important in clinical practice to identify people with cancer at higher risk of adverse outcomes.
Sections du résumé
BACKGROUND & AIMS
The Global Leadership Initiative on Malnutrition (GLIM) criteria require validation in various clinical populations. This study determined the prevalence of malnutrition in people with cancer using all possible diagnostic combinations of GLIM etiologic and phenotypic criteria and determined the combinations that best predicted mortality and unplanned hospital admission within 30 days.
METHODS
The GLIM criteria were applied, in a cohort of participants from two cancer malnutrition point prevalence studies (N = 2801), using 21 combinations of the phenotypic (≥5% unintentional weight loss, body mass index [BMI], subjective assessment of muscle stores [from PG-SGA]) and etiologic (reduced food intake, inflammation [using metastatic disease as a proxy]) criteria. Machine learning approaches were applied to predict 30-day mortality and unplanned admission.
RESULTS
We analysed 2492 participants after excluding those with missing data. Overall, 19% (n = 485) of participants were malnourished. The most common GLIM combinations were weight loss and reduced food intake (15%, n = 376), and low muscle mass and reduced food intake (12%, n = 298). Machine learning models demonstrated malnutrition diagnosis by weight loss and reduced muscle mass plus either reduced food intake or inflammation were the most important combinations to predict mortality at 30-days (accuracy 88%). Malnutrition diagnosis by weight loss or reduced muscle mass plus reduced food intake was most important for predicting unplanned admission within 30-days (accuracy 77%).
CONCLUSIONS
Machine learning demonstrated that the phenotypic criteria of weight loss and reduced muscle mass combined with either etiologic criteria were important for predicting mortality. In contrast, the etiologic criteria of reduced food intake in combination with weight loss or reduced muscle mass was important for predicting unplanned admission. Understanding the phenotypic and etiologic criteria contributing to the GLIM diagnosis is important in clinical practice to identify people with cancer at higher risk of adverse outcomes.
Identifiants
pubmed: 35413572
pii: S0261-5614(22)00105-4
doi: 10.1016/j.clnu.2022.03.024
pii:
doi:
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1102-1111Informations de copyright
Copyright © 2022 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Déclaration de conflit d'intérêts
Conflict of interest Dr. Kiss reports grants from Medical Nutrition Industry, grants from Medical Research Future Fund, grants from AuSPEN, grants from Amgen OA-ANZBMS, grants from Victorian Cancer Agency, outside the submitted work. Irene Deftereos reports grants from AuSPEN and Nestle Health Science, outside the submitted work. Dr. Alizadehsani, Ms. Steer, Ms. Loeliger, Dr. de van der Schueren, Dr. Edbrooke, Dr. Laing and Dr. Khosravi have nothing to disclose.