Evaluation of the global leadership initiative on malnutrition criteria using different muscle mass indices for diagnosing malnutrition and predicting survival in lung cancer patients

Yin, Liangyu; Lin, Xin; Li, N.; Zhang, M.; He, Xueling; Liu, J.; Kang, Jun; Chen, X.; Wang, C.; Wang, X.; Liang, Tingting; Liu, X.; Deng, Li; Li, W.; Song, Chunhua; Cui, Jiuwei; Shi, Hanping; Xu, Hongxia

doi:10.1016/j.clnesp.2020.09.223

Cited by 5 publications

(21 citation statements)

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“…Fat mass loss has also been related to worse survival in patients with LC (30,31) . As we have described in our previous work, GLIM-defined malnutrition is an independent risk factor for LC survival (32) . However, due to the current architecture of the GLIM, it remains unknown if the inclusion of a fat assessment would enhance the prognostic value of the GLIM in LC patients.…”

supporting

confidence: 54%

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer

Yin¹,

Fan²,

Lin³

et al. 2021

Br J Nutr

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The present study evaluated whether fat mass assessment using the triceps skinfold (TSF) thickness provides additional prognostic value to the GLIM framework in patients with lung cancer (LC). We performed an observational cohort study including 2672 LC patients at two institutions in China. Comprehensive demographic, disease and nutritional characteristics were collected. Malnutrition was retrospectively defined using the GLIM criteria, and optimal stratification was used to determine the best thresholds for the TSF. The associations of malnutrition and TSF categories with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs). Malnutrition was identified in 808 (30.2%) patients, and the best TSF thresholds were 9.5 mm in men and 12 mm in women. Accordingly, 496 (18.6%) patients were identified as having a low TSF. Patients with concurrent malnutrition and a low TSF had a 54% (HR = 1.54, 95%CI = 1.25 to 1.88) greater death hazard compared with well-nourished individuals, which was also greater compared to malnourished patients with a normal TSF (HR = 1.23, 95%CI = 1.06 to 1.43) or malnourished patients without TSF assessment (HR = 1.31, 95%CI = 1.14 to 1.50). These associations were concentrated among those patients with adequate muscle mass (as indicated by the calf circumference). Additional fat mass assessment using the TSF enhances the prognostic value of the GLIM criteria. Using the population-derived thresholds for the TSF may provide significant prognostic value when used in combination with the GLIM criteria to guide strategies to optimize the long-term outcomes in patients with LC.

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supporting

confidence: 54%

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer

Yin¹,

Fan²,

Lin³

et al. 2021

Br J Nutr

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“…26,37 A value < p15 (males: <30 cm, females: <29.5 cm) was defined as positive for moderate muscle loss, and a value < p5 (males: <28 cm; females: <27.5 cm) was defined as indicative of severe muscle loss (Table S2). 27 For the etiologic criteria, reduced food intake (≤50% of normal requirements for >1 week or any reduction for >2 weeks) and gastrointestinal symptoms (dysphagia, nausea, vomiting, diarrhea, constipation, and abdominal pain) were evaluated based on the patient-reported information from the nutrition interview. Inflammation and disease burden were evaluated based on the chronic disease history, trauma history, pathological diagnosis, treatment information, and laboratory indices.…”

Section: Diagnosis Of Malnutrition Based On the Glim Criteriamentioning

confidence: 99%

“…Inflammation and disease burden were evaluated based on the chronic disease history, trauma history, pathological diagnosis, treatment information, and laboratory indices. The levels of C-reactive protein (normal: 0-10 mg/L), 27 white blood cell counts (normal: [4-10] × 10 9 /L), 27 and neutrophil to lymphocyte ratio (normal: <3) 38 were used as supportive laboratory indicators of inflammation, and at least 1 positive index was required to indicate an inflammation status.…”

Section: Diagnosis Of Malnutrition Based On the Glim Criteriamentioning

confidence: 99%

“…21 With increasing validations of its performance, the GLIM guidelines have proved effective in identifying malnutrition 22,23 and predicting outcomes 24,25 in patients with a wide spectrum of diseases, 25 including cancer. 26,27 However, since nutrition screening and assessment should be performed immediately after hospitalization to enable an early diagnosis and multidisciplinary/interdisciplinary interventions, 28,29 the need for laboratory indices (such as the C-reactive protein level to indicate the inflammation status in the etiologic criteria) to establish the GLIM diagnosis may delay this process for those patients whose inflammation status is difficult to determine only on the basis of clinical observation. Second, the relative importance of the parameters constituting the GLIM framework remain unclear.…”

Section: Introductionmentioning

confidence: 99%

“…The newly proposed Global Leadership Initiative on Malnutrition (GLIM) criteria, based on etiologic (including 3 components: weight loss, low body mass index [BMI], and reduced muscle mass [RMM]) and phenotypic criteria (including 2 components: reduced food intake/assimilation and inflammation) adapted to different populations, might meet this urgent need 21 . With increasing validations of its performance, the GLIM guidelines have proved effective in identifying malnutrition 22,23 and predicting outcomes 24,25 in patients with a wide spectrum of diseases, 25 including cancer 26,27 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification Tree–Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients

Yin

Lin

Liu

et al. 2021

J Parenter Enteral Nutr

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Background: The newly proposed Global Leadership Initiative on Malnutrition (GLIM) framework is promising to gain global acceptance for diagnosing malnutrition. However, the role of machine learning in facilitating its application in clinical practice remains largely unknown. Methods: We performed a multicenter, observational cohort study including 3998 patients with cancer. Baseline malnutrition was defined using the GLIM criteria, and the study population was randomly divided into a derivation group (n = 2998) and a validation group (n = 1000). A classification and regression trees (CART) algorithm was used to develop a decision tree for classifying the severity of malnutrition in the derivation group. Model performance was evaluated in the validation group. Results: GLIM criteria diagnosed 588 patients (14.7%) with moderate malnutrition and 532 patients (13.3%) with severe malnutrition among the study population. The CART cross-validation identified 5 key predictors for the decision tree construction, including age, weight loss within 6 months, body mass index, calf circumference, and the Nutritional Risk Screening 2002 score. The decision tree showed high performance, with an area under the curve of 0.964 (κ = 0.898, P < .001, accuracy = 0.955) in the validation group. Subgroup analysis showed that the model had apparently good performance in different cancers. Among the 5 predictors constituting the tree, age contributed the least to the classification power. Conclusion: Using the machine learning, we visualized and validated a decision tool based on the GLIM criteria that can be conveniently used to accelerate the pretreatment identification of malnutrition in patients with cancer.

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Global Leadership Initiative on Malnutrition criteria with different muscle assessments including muscle ultrasound with hospitalized internal medicine patients

Öztürk

Deniz

Çöteli

et al. 2021

J Parenter Enteral Nutr

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Background:The aim of this study is to identify cutoff values for muscle ultrasound (US) to be used in Global Leadership Initiative on Malnutrition (GLIM) criteria, and to define the effect of reduced muscle mass assessment on malnutrition prevalence at hospital admission.Methods: A total of 118 inpatients were enrolled in this cross-sectional study. Six different muscles were evaluated by US. Following defining thresholds for muscle US to predict low muscle mass measured by bioelectrical impedance analysis, malnutrition was diagnosed by GLIM criteria with seven approaches, including calf circumference, mid-upper arm circumference (MAC), handgrip strength (HGS), skeletal muscle index (SMI), rectus femoris (RF) muscle thickness, and cross-sectional area (CSA) in addition to without using the reduced muscle mass criterion. Results:The median age of patients was 64 (18-93) years, 55.9% were female. RF muscle thickness had moderate positive correlations with both HGS (r = 0.572) and SMI (r = 0.405). RF CSA had moderate correlation with HGS (r = 0.567) and low correlation with SMI (r = 0.389). The cutoff thresholds were 11.3 mm (area under the curve [AUC] = 0.835) and 17 mm (AUC = 0.737) for RF muscle thickness and 4 cmš (AUC = 0.937) and 7.2 cmš (AUC = 0.755) for RF CSA in females and males, respectively. Without using the reduced muscle mass criterion, malnutrition prevalence was 46.6%; otherwise, it ranged from 47.5% (using MAC) to 65.2% (using HGS). Conclusions:Muscle US may be used in GLIM criteria. However, muscle US needs a standard measurement technique and specific cutoff values in future studies.

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Evaluation of the global leadership initiative on malnutrition criteria using different muscle mass indices for diagnosing malnutrition and predicting survival in lung cancer patients

Cited by 5 publications

References 0 publications

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer

Classification Tree–Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients

Global Leadership Initiative on Malnutrition criteria with different muscle assessments including muscle ultrasound with hospitalized internal medicine patients

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