Malnutrition 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.
We 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.
The 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.
This 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.
About The Expert
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
References
PubMed