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- Title
Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer.
- Authors
Yin, Liangyu; Liu, Jie; Lin, Xin; Li, Na; Guo, Jing; Fan, Yang; Zhang, Ling; Shi, Muli; Zhang, Hongmei; Chen, Xiao; Wang, Chang; Deng, Li; Li, Wei; Fu, Zhenming; Song, Chunhua; Guo, Zengqing; Cui, Jiuwei; Shi, Hanping; Xu, Hongxia
- Abstract
<bold>Background: </bold>Malnutrition is prevalent that can impair multiple clinical outcomes in oncology populations. This study aimed to develop and utilize a tool to optimize the early identification of malnutrition in patients with cancer.<bold>Methods: </bold>We performed an observational cohort study including 3998 patients with cancer at two teaching hospitals in China. Hierarchical clustering was performed to classify the patients into well-nourished or malnourished clusters based on 17 features reflecting the phenotypic and etiologic dimensions of malnutrition. Associations between the identified clusters and patient characteristics were analyzed. A nomogram for predicting the malnutrition probability was constructed and independent validation was performed to explore its clinical significance.<bold>Results: </bold>The cluster analysis identified a well-nourished cluster (n = 2736, 68.4%) and a malnourished cluster (n = 1262, 31.6%) in the study population, which showed significant agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria (both P < 0.001). The malnourished cluster was negatively associated with the nutritional status, physical status, quality of life, short-term outcomes and was an independent risk factor for survival (HR = 1.38, 95%CI = 1.22-1.55, P < 0.001). Sex, gastrointestinal symptoms, weight loss percentages (within and beyond 6 months), calf circumference, and body mass index were incorporated to develop the nomogram, which showed high performance to predict malnutrition (AUC = 0.972, 95%CI = 0.960-0.983). The decision curve analysis and independent external validation further demonstrated the effectiveness and clinical usefulness of the tool.<bold>Conclusions: </bold>Nutritional features-based clustering analysis is a feasible approach to define malnutrition. The derived nomogram shows effectiveness for the early identification of malnutrition in patients with cancer.
- Subjects
MALNUTRITION diagnosis; NUTRITIONAL assessment; MALNUTRITION; QUALITY of life; TUMORS; CLUSTER analysis (Statistics); NUTRITIONAL status; DISEASE complications
- Publication
European Journal of Clinical Nutrition, 2021, Vol 75, Issue 8, p1291
- ISSN
0954-3007
- Publication type
journal article
- DOI
10.1038/s41430-020-00844-8