Comparison of bioelectrical impedance analysis and computed tomography for the assessment of muscle mass in patients with gastric cancer

Zuo, Junbo; Zhou, Da; Zhang, Li; Zhou, Xiaodong; Gao, Xuejin; Hou, Wenji; Wang, Chen; Jiang, Pengcheng; Wang, Xinying

doi:10.1016/j.nut.2024.112363

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Malnutrition was diagnosed according to the GLIM criteria 7 , and the diagnostic procedure followed the description provided in our previous study 14 . Brie y, patients with GC were considered to ful l the etiological criterion of in ammation or disease burden, and the diagnosis of malnutrition was based on meeting at least one of the phenotypic criteria: (1) involuntary weight loss de ned as > 5% within the past 6 months or > 10% beyond 6 months; (2) low BMI de ned as < 18.5 kg/m 2 for patients under 70 years old and < 20 kg/m 2 for those over 70 years; (3) reduced muscle mass de ned as ≤ 40.8 cm 2 /m 2 in males and ≤ 34.9 cm 2 /m 2 in females using sex-speci c cut-off values of L3-SMI 14 .…”

Section: Diagnosis Of Malnutritionmentioning

confidence: 99%

Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

Junbo,

Yan

et al. 2024

Preprint

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Objective This study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC). Method Objective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria. Results A total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 patients (47.8%) based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition. Conclusions Compared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients.

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Section: Diagnosis Of Malnutritionmentioning

confidence: 99%