Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer
- 18 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in European Journal of Clinical Nutrition
- Vol. 75 (8), 1291-1301
- https://doi.org/10.1038/s41430-020-00844-8
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.Keywords
Funding Information
- Chongqing Technology Innovation and Application Demonstration Project for Social Livelihood
- National key research and development program
- National Natural Science Foundation of China (81673167)
This publication has 45 references indexed in Scilit:
- Effect of nutritional interventions on nutritional status, quality of life and mortality in patients with head and neck cancer receiving (chemo)radiotherapy: a systematic reviewClinical Nutrition, 2013
- Gastrointestinal symptoms and weight loss in cancer patients receiving chemotherapyBritish Journal of Nutrition, 2012
- Gastrointestinal symptoms in cancer patients with advanced diseaseCurrent Opinion in Supportive & Palliative Care, 2012
- Baseline nutritional evaluation in metastatic lung cancer patients: Mini Nutritional Assessment versus weight loss historyAnnals of Oncology, 2011
- Prognostic Factors in Patients With Advanced Cancer: Use of the Patient-Generated Subjective Global Assessment in Survival PredictionJournal of Clinical Oncology, 2010
- Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer CentresBritish Journal of Cancer, 2010
- Validation of the simplified Chinese version of EORTC QLQ-C30 from the measurements of five types of inpatients with cancerAnnals of Oncology, 2008
- Malnutrition was associated with poor quality of life in colorectal cancer: a retrospective analysisJournal of Clinical Epidemiology, 2006
- ESPEN Guidelines for Nutrition Screening 2002Clinical Nutrition, 2003
- Karnofsky Performance Status and Assessment of Global Health StatusJAIDS Journal of Acquired Immune Deficiency Syndromes, 1996