Liangyu Yin1, Jie Liu1, Xin Lin1, Na Li1, Jing Guo1, Yang Fan1, Ling Zhang1, Muli Shi1, Hongmei Zhang1, Xiao Chen2, Chang Wang2, Li Deng2, Wei Li2, Zhenming Fu3, Chunhua Song4, Zengqing Guo5, Jiuwei Cui6, Hanping Shi7, Hongxia Xu8. 1. Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China. 2. Cancer Center of the First Hospital of Jilin University, Changchun, 130021, Jilin, China. 3. Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China. 4. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China. 5. Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China. 6. Cancer Center of the First Hospital of Jilin University, Changchun, 130021, Jilin, China. cuijw@jlu.edu.cn. 7. Department of Gastrointestinal Surgery | Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 100038, Beijing, China. shihp@ccmu.edu.cn. 8. Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China. hx_xu2015@163.com.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Arwa S Almasaudi; Stephen T McSorley; Ross D Dolan; Christine A Edwards; Donald C McMillan Journal: Am J Clin Nutr Date: 2019-12-01 Impact factor: 7.045
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702
Authors: Alexandra Canonici; Alacoque L Browne; Mohamed F K Ibrahim; Kevin P Fanning; Sandra Roche; Neil T Conlon; Fiona O'Neill; Justine Meiller; Mattia Cremona; Clare Morgan; Bryan T Hennessy; Alex J Eustace; Flavio Solca; Norma O'Donovan; John Crown Journal: Ther Adv Med Oncol Date: 2020-01-28 Impact factor: 8.168