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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
Author(s) -
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
Publication year - 2021
Publication title -
journal of parenteral and enteral nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.935
H-Index - 98
eISSN - 1941-2444
pISSN - 0148-6071
DOI - 10.1002/jpen.1873
Subject(s) - malnutrition , nomogram , medicine , body mass index , anthropometry , hazard ratio , proportional hazards model , lung cancer , multivariate analysis , intensive care medicine , confidence interval
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.