• Liangyu Yin, Jie Liu, Xin Lin, Na Li, Muli Shi, Hongmei Zhang, Jing Guo, Xiao Chen, Chang Wang, Xu Wang, Tingting Liang, Xiangliang Liu, Li Deng, Wei Li, Zhenming Fu, Chunhua Song, Jiuwei Cui, Hanping Shi, Hongxia Xu, and Investigation on Nutrition Status and Clinical Outcome of Common Cancers Group.
• Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China; Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
• Nutrition. 2021 Apr 1; 84: 111102.

ObjectivesMalnutrition is frequently developed and outcome-related in patients with lung cancer (LC). Making a rapid and accurate diagnosis of malnutrition is the major concern for dietitians and clinicians.MethodsWe performed a multicenter, observational cohort study including 1219 patients with LC. Malnutrition was diagnosed using the Global Leadership Initiative on Malnutrition criteria, and the study population was randomly divided into a training group (n = 914) and a validation group (n = 305). A nomogram (to diagnose malnutrition) and two decision trees (to diagnose and grade malnutrition, respectively) were independently developed and tested. A random forest algorithm was used to calculate relative variable importance.ResultsThe Global Leadership Initiative on Malnutrition criteria identified 292 patients with malnutrition (24%). Sex, body mass index, weight loss within 6 mo, weight loss beyond 6 mo, calf circumference, and handgrip strength to weight ratio were screened for model development. The nomogram showed good discrimination with an area under the curve (AUC) of 0.982 (95% confidence interval, 0.969-0.995) and good calibration in the validation group. A decision curve analysis demonstrated that the nomogram was clinically useful. The diagnostic tree showed an accuracy of 0.98 (Kappa = 0.942; AUC = 0.978; 95% confidence interval, 0.964-0.992), and the classification tree showed an accuracy of 0.98 (Kappa = 0.955; AUC = 0.987) in the validation group. Weight loss within 6 mo contributed the largest importance to both trees.ConclusionsThis study presents a rapid-decision pathway, including a set of tools that can be conveniently used to facilitate the diagnosis and severity grading of malnutrition in patients with LC.Copyright © 2020 Elsevier Inc. All rights reserved.

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