Evaluation of the Global Leadership Initiative on Malnutrition Criteria Using Different Muscle Mass Indices for Diagnosing Malnutrition and Predicting Survival in Lung Cancer Patients

Abstract
Background Malnutrition is prevalent and outcome‐related 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 from two institutions in China. Different anthropometric measures were compared for the assessment of reduced muscle mass (RMM) in the GLIM criteria. LASSO 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 the combined calf circumference (CC) plus body weight‐standardized hand grip strength (HGS/W) criteria had the highest hazard ratio (HR = 2.07, 95%CI = 1.50‐2.86) compared to 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 nutritional status of LC patients. GLIM‐diagnosed malnutrition was an independent risk factor for survival and the malnutrition severity was monotonically associated with death hazards (P = 0.002). 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. This article is protected by copyright. All rights reserved
Funding Information
  • National Basic Research Program of China (2017YFC1309200)
  • National Natural Science Foundation of China (81673167)

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