Development and validation of a rapid-decision pathway to diagnose malnutrition in patients with lung cancer.

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

Journal: Nutrition (Burbank, Los Angeles County, Calif.) 2021;84():111102

PMID: 33453621

Full Text on Elsevier Science View on PubMed

Abstract

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Address: 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.; Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.; Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, China.; Cancer Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.; Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.; Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, China. Electronic address: [email protected].; Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China. Electronic address: [email protected].; Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China. Electronic address: [email protected].

DOI: 10.1016/j.nut.2020.111102

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MeSH Terms: Area Under Curve, Hand Strength, Humans, Lung Neoplasms, Malnutrition, Nomograms