Influence of Malnutrition According to the GLIM Criteria on the Clinical Outcomes of Hospitalized Patients With Cancer

Liu, Chengyu; Lu, Zhenhua; Li, Zijian; Xu, Jingyong; Hongyuan, Cui; Zhu, Mingwei

doi:10.3389/fnut.2021.774636

Cited by 21 publications

(30 citation statements)

References 44 publications

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“…At present, the specific indicators for muscle mass reduction and inflammation, as well as their optimal thresholds, in the diagnosis of malnutrition are still unknown. Many researchers have adopted calf circumference plus grip strength as an alternative indicator of muscle mass reduction and serum albumin concentration reduction as an alternative indicator of inflammation [ 7 , 24 ]. This practice is also adopted in the present study.…”

Section: Discussionmentioning

confidence: 99%

“…The data of this study came from a large-scale prospective observational cohort study conducted in 34 level-A tertiary hospitals in 18 cities in China from June to September 2014 with 7122 subjects [ 7 , 22 ]. According to the hospital grading system in China, there are three tiers of hospitals.…”

Section: Methodsmentioning

confidence: 99%

“…A standardized research protocol was adopted in the above-mentioned cohort study and the present study. Within 24 h of admission, patients were assessed by trained clinicians with the nutritional risk screening-2002 (NRS-2002) scale [ 23 ] and then diagnosed for malnutrition with the subjective global assessment (SGA) guideline [ 6 , 7 ]. Malnutrition as defined by the ESPEN 2015 guideline [ 8 ] and the GLIM framework [ 9 ] was retrospectively determined by reviewing the subjects’ data.…”

Section: Methodsmentioning

confidence: 99%

“…However, to date, widely applicable diagnostic criteria for malnutrition that can be used in a wide range of clinical settings have not been proposed. Several diagnostic criteria are now used in clinical practices around the world, such as the subjective comprehensive assessment (SGA) scale [ 6 , 7 ], the framework established by the European society for clinical nutrition and metabolism in 2015 (ESPEN 2015) [ 8 ], and the global leaders’ initiative on malnutrition (GLIM) criteria [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The SGA scale is a comprehensive method for malnutrition diagnosis. It incorporates both subjective and objective evaluations of the patients based on their medical history, physical examination, nutritional status, changes in body weight and dietary intakes, gastrointestinal symptoms, physical activity, diseases, and measurement of the skinfold thickness of the triceps and the existence/inexistence of edema [ 6 , 7 ]. SGA has shown excellent accuracy in predicting prolonged hospital stay, re-hospitalization, and mortality in various patients [ 10 , 11 ].…”

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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Malnutrition as a prognostic factor for 2‐year mortality in hospitalized patients in Norway: A matched cohort study

Trollebø,

Tangvik,

Skeie

et al. 2024

J Parenter Enteral Nutr

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BackgroundRisk of malnutrition and malnutrition have been previously associated with increased risk of mortality. It remains unclear, however, whether the severity of malnutrition differentiates in association with all‐cause mortality. The aim was to assess the association between being at risk of malnutrition or being diagnosed with malnutrition according to the diagnostic assessment of the Global Leadership Initiative on Malnutrition (GLIM) with all‐cause mortality during a 2‐year follow‐up in hospitalized patients.MethodsA matched cohort study was conducted in hospitalized patients (excluding cancer, intensive care, and transmissible infections) at a university hospital in Bergen, Norway. All patients underwent nutrition screening with the Nutritional Risk Screening 2002 and a further nutrition assessment using the GLIM criteria. All‐cause mortality was estimated from the Norwegian death registry after 2 years, and risk factors were calculated by Cox regression analysis.ResultsAmong 326 patients included, 55 patients died within 2 years (17% mortality rate). Risk of malnutrition was associated with increased all‐cause mortality, which disappeared after adjustment for age and sex. Malnutrition was associated with an increased risk of all‐cause mortality at 2 years also after adjustment for age and sex and, additionally, for further comorbidities (hazard ratio = 2.50; 95% CI, 1.41–4.42). When analyzed separately only severe malnutrition was associated with mortality (hazard ratio = 2.73; 95% CI, 1.44–5.15).ConclusionThe findings highlight a strong association between inpatients with severe malnutrition, defined by the GLIM criteria, and an increased risk of all‐cause mortality within a 2‐year follow‐up.

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Malnutrition accelerates the occurrence of infectious complications in patients with chronic kidney disease: A cross‐sectional survey of 682 patients with chronic kidney disease

Wang

Dai

et al. 2023

Nut in Clin Prac

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BackgroundTo investigate the influencing factors of infectious complications in patients with chronic kidney disease (CKD) and provide a basis for clinical diagnosis and prognosis evaluation of CKD.MethodsA total of 682 patients with CKD were selected and divided into CKD stage 1–5 subgroups according to their glomerular filtration rate. Infectious complications, length of hospital stay, and total cost of hospitalization were recorded. The Global Leadership Initiative on Malnutrition (GLIM) diagnostic tool was used to assess the detection rate of malnutrition among patients. Univariate and multivariate analyses were performed in patients with and without infectious complications.ResultsThe incidence rates of infectious complications in CKD stages 1–5 were 45.6%, 22.7%, 28.3%, 30.8%, and 40.4%, respectively. The overall detection rate of malnutrition among patients based on the GLIM criteria was 16.7%. The total detection rate of severe malnutrition was 14.2%, with all patients with severe malnutrition in CKD stages 3–5. The incidences of infectious complications in patients with and without malnutrition were 62.3% and 29%, respectively. Binary multivariate logistic regression analysis shows that malnutrition is a risk factor for infectious complications in patients with CKD, who are at 2.41 times higher risk than patients without malnutrition. There were significant differences in length of hospital stay and hospitalization costs between the patients with CKD with and without infectious complications (P < 0.01).ConclusionInfectious complications are relatively common in patients with CKD. As CKD advances, the incidence of infectious complications increases. Moreover, malnutrition accelerates the occurrence of infectious complications in patients with CKD.

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Influence of Malnutrition According to the GLIM Criteria on the Clinical Outcomes of Hospitalized Patients With Cancer

Cited by 21 publications

References 44 publications

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

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

Malnutrition as a prognostic factor for 2‐year mortality in hospitalized patients in Norway: A matched cohort study

Malnutrition accelerates the occurrence of infectious complications in patients with chronic kidney disease: A cross‐sectional survey of 682 patients with chronic kidney disease

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