Evaluation of the Global Leadership Initiative on Malnutrition Criteria Using Different Muscle Mass Indices for Diagnosing Malnutrition and Predicting Survival in Lung Cancer Patients. Issue 3 (1st June 2020)

• Record Type: Journal Article
• Authors: Yin, Liangyu; Lin, Xin; Li, Na; Zhang, Mengyuan; He, Xiumei; Liu, Jie; Kang, Jun; Chen, Xiao; Wang, Chang; Wang, Xu; Liang, Tingting; Liu, Xiangliang; Deng, Li; Li, Wei; Song, Chunhua; Cui, Jiuwei; Shi, Hanping; Xu, Hongxia
• Is Part Of: JPEN, Journal of parenteral and enteral nutrition. Volume 45:Issue 3(2021)
• Page Start: 607
• Page End: 617
• Publication Date: 2020-06-01
• Subjects: lung cancer -- malnutrition -- muscle mass -- prediction -- survival; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; 615.85484
• DOI: 10.1002/jpen.1873
• Languages: English
• ISSNs: 0148-6071

Abstract

Abstract: Background: Malnutrition is prevalent in lung cancer (LC) patients, yet there are no globally accepted criteria for diagnosing malnutrition. Recently, the Global Leadership Initiative on Malnutrition (GLIM) criteria were proposed. However, the role of these criteria in prospective LC cohorts remains unclear. Methods: We performed a multicenter, observational cohort study including 1219 LC patients. Different anthropometric measures were compared for assessment of reduced muscle mass (RMM) in the GLIM criteria. Least absolute shrinkage and selection operator and multivariate Cox regressions were performed to analyze the association between the GLIM criteria and survival. Independent prognostic predictors were incorporated to develop a nomogram for individualized survival prediction, and decision curve was applied to assess the clinical significance of the nomogram. Results: Patients in the stage II (severe) malnutrition group, diagnosed using combined calf circumference (CC) plus body weight–standardized handgrip strength (HGS/W) criteria, had the highest hazard ratio (HR, 2.07; 95%CI, 1.50–2.86) compared with other methods used to evaluate RMM. The GLIM criteria diagnosed malnutrition in 24% of cases (292 patients, using the CC and HGS/W criteria) and were effective for determining the nutrition status of LC patients. GLIM‐diagnosed malnutrition was an independent risk factor for survival, and malnutrition severity was monotonically associated with death hazards ( P = .002). The GLIM nomogram showed good performance in predicting the survival of LC patients, and the decision‐curve analysis demonstrated that the nomogram was clinically useful. Conclusion: These findings support the effectiveness of GLIM in diagnosing malnutrition and predicting survival among LC patients.