Utilization and validation of the Global Leadership Initiative on Malnutrition (GLIM): A scoping review

Correia, María Isabel Toulson Davisson; Tappenden, Kelly A.; Malone, Ainsley; Prado, Carla M.; Evans, David C.; Sauer, Abby C.; Hegazi, Refaat; Gramlich, Leah

doi:10.1016/j.clnu.2022.01.018

Cited by 63 publications

(38 citation statements)

References 99 publications

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“…The limitations of available tools should be recognized when implementing routine screening and assessment of these patients. A summary of the possible uses and limitations of selected tools, including the Malnutrition Screening Tool (MST), the Malnutrition Universal Screening Tool (MUST), the modified NUTrition Risk in the Critically ill (mNUTRIC) score, Nutrition-Focused Physical Exam (NFPE) and Subjective Global Assessment, is presented in Table 2 [ 24 , – 39 ].…”

Section: Malnutrition Screening and Assessmentmentioning

confidence: 99%

Obesity and critical care nutrition: current practice gaps and directions for future research

Dickerson

Andromalos²,

Brown

et al. 2022

Crit Care

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Background This review has been developed following a panel discussion with an international group of experts in the care of patients with obesity in the critical care setting and focuses on current best practices in malnutrition screening and assessment, estimation of energy needs for patients with obesity, the risks and management of sarcopenic obesity, the value of tailored nutrition recommendations, and the emerging role of immunonutrition. Patients admitted to the intensive care unit (ICU) increasingly present with overweight and obesity that require individualized nutrition considerations due to underlying comorbidities, immunological factors such as inflammation, and changes in energy expenditure and other aspects of metabolism. While research continues to accumulate, important knowledge gaps persist in recognizing and managing the complex nutritional needs in ICU patients with obesity. Available malnutrition screening and assessment tools are limited in patients with obesity due to a lack of validation and heterogeneous factors impacting nutrition status in this population. Estimations of energy and protein demands are also complex in patients with obesity and may include estimations based upon ideal, actual, or adjusted body weight. Evidence is still sparse on the role of immunonutrition in patients with obesity, but the presence of inflammation that impacts immune function may suggest a role for these nutrients in hemodynamically stable ICU patients. Educational efforts are needed for all clinicians who care for complex cases of critically ill patients with obesity, with a focus on strategies for optimal nutrition and the consideration of issues such as weight stigma and bias impacting the delivery of care. Conclusions Current nutritional strategies for these patients should be undertaken with a focus on individualized care that considers the whole person, including the possibility of preexisting comorbidities, altered metabolism, and chronic stigma, which may impact the provision of nutritional care. Additional research should focus on the applicability of current guidelines and evidence for nutrition therapy in populations with obesity, especially in the setting of critical illness.

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Section: Malnutrition Screening and Assessmentmentioning

confidence: 99%

Obesity and critical care nutrition: current practice gaps and directions for future research

Dickerson

Andromalos²,

Brown

et al. 2022

Crit Care

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“…The evaluation procedure relies on the professionalism of the evaluator and inevitably has a subjective nature. The ESPEN 2015 framework attempted to make the malnutrition evaluation more objective than the SGA scale but failed to take into account the etiological factors causing malnutrition [ 2 ], resulting in its inferior performance to the other two criteria in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…Malnutrition is commonly seen in elderly patients hospitalized for various acute and chronic diseases [ 1 ]. It has many causes, such as decreased food intake of the patients and adverse impacts of diseases [ 2 ]. It is known that many diseases are accompanied by a systemic inflammatory and catabolism-prone status that accelerates the development of malnutrition [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is simple to use because it consists of only objective criteria such as low body mass index (BMI), unconscious weight loss, and reduction in the fat-free body mass index (FFMI) [ 8 ]. As the first attempt to reach a global consensus on malnutrition diagnosis, the criteria only cover the phenotypic features of malnutrition and have omitted the etiology of it, further optimization is needed before they can be widely accepted [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, because the diagnostic results of the criteria mentioned above often differ, it is often confusing for clinicians when they want to select a method to assess the nutritional status of patients and predict their clinical outcomes and accordingly optimize their treatment regimens [ 1 , 2 , 21 ]. To address the problem, it is necessary to test the value of these diagnostic criteria in different clinical scenarios in a case-by-case manner before a consensus is reached on a widely accepted, universally applicable diagnostic criterion for malnutrition.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor

et al. 2022

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Background: Malnutrition is prevalent in elderly inpatients and is associated with various adverse outcomes during their hospital stay, but the diagnosis of malnutrition still lacks widely applicable criteria. This study aimed to investigate the association of malnutrition diagnosed with the SGA, ESPEN 2015, and GLIM criteria, respectively, with in-hospital complications in elderly patients. Method: Hospitalized patients over 65 years old who had been assessed with the SGA guideline for malnutrition at admission were retrospectively recruited from a large observational cohort study conducted in 34 level-A tertiary hospitals in 18 cities in China from June to September 2014. Malnutrition was then retrospectively diagnosed using the GLIM and ESPEN 2015 criteria, respectively, for comparison with the results of the SGA scale. The risk factors for malnutrition were analyzed using logistic regression, and the value of the three diagnostic criteria in predicting the in-hospital complications was subsequently explored using multivariate regression and the random forest machine learning algorithm. Results: A total of 2526 subjects who met the inclusion and exclusion criteria of the study were selected from the 7122 patients in the dataset, with an average age of 74.63 ± 7.12 years, 59.2% male, and 94.2% married. According to the GLIM, SGA, and ESPEN 2015 criteria, the detection rates of malnutrition were 37.8% (956 subjects), 32.8% (829 subjects), and 17.0% (429 subjects), respectively. The diagnostic consistency between the GLIM and the SGA criteria is better than that between the ESPEN 2015 and the SGA criteria (Kappa statistics, 0.890 vs. 0.590). Logistic regression showed that the risk of developing complications in the GLIM-defined malnutrition patients is 2.414 times higher than that of normal patients, higher than those of the ESPEN 2015 and SGA criteria (1.745 and 1.786 times, respectively). The random forest classifications show that the GLIM criteria have a higher ability to predict complications in these elderly patients than the SGA and ESPEN 2015 criteria with a mean decrease in accuracy of 12.929, 10.251, and 5.819, respectively, and a mean decrease in Gini of 2.055, 1.817, and 1.614, respectively. Conclusion: The prevalence of malnutrition diagnosed with the GLIM criteria is higher than that of the SGA and the ESPEN 2015 criteria. The GLIM criteria are better than the SGA and the ESPEN 2015 criteria for predicting in-hospital complications in elderly patients.

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A Prognostic Model Based on Nutritional Indexes for Patients With Pan‐Cancer: A Real‐World Cohort Study

Zheng,

Yu,

Ruan

et al. 2024

Cancer Reports

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BackgroundThe aim was to identify the nutritional indexes, construct a prognostic model, and develop a nomogram for predicting individual survival probability in pan‐cancers.MethodsNutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients were collected. The enrolled patients were randomly divided into training and validation cohorts. Least absolute shrinkage and selection operator (Lasso) regression cross‐validation was used to determine the variables to include in the cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration, and clinical effectiveness of the model.ResultsA total of 2020 patients were included. The median OS was 56.50 months (95% CI, 50.36–62.65 months). In the training cohort of 1425 patients, through Lasso regression cross‐validation, 13 characteristics were included in the model. Cox proportional hazards model was developed and visualized as a nomogram. The C‐indexes of the model for predicting 1‐, 3‐, 5‐, and 10‐year OS were 0.848, 0.826, 0.814, and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723–8.397; log‐rank p < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180–11.497; log‐rank p < 0.001).ConclusionThe prognostic model based on the nutritional indexes of pan‐cancer can divide patients into different survival risk groups and performed well in the validation cohort.

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Utilization and validation of the Global Leadership Initiative on Malnutrition (GLIM): A scoping review

Cited by 63 publications

References 99 publications

Obesity and critical care nutrition: current practice gaps and directions for future research

Obesity and critical care nutrition: current practice gaps and directions for future research

Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor

A Prognostic Model Based on Nutritional Indexes for Patients With Pan‐Cancer: A Real‐World Cohort Study

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