Observational studyEuropean journal of clinical nutrition2021
Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer.
Observational study in European journal of clinical nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 4 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Diagnostic performance of GLIM and PG-SGA for malnutrition assessment in adult cancer patients: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Risk prediction models for malnutrition in cancer patients: a systematic review and meta-analysis.Frontiers in nutrition · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- GLIM-defined malnutrition and overall survival in cancer patients: A meta-analysis.JPEN. Journal of parenteral and enteral nutrition · 2023Pooled it
- Article
- Development and Validation of a Risk-Prediction Nomogram for Nutritional Risk in Non-Dialysis Chronic Renal Failure Patients.Risk management and healthcare policy · 2026Article
- A Cross-Sectional Study: Association Between Nutritional Quality and Cancer Cachexia, Anthropometric Measurements, and Psychological Symptoms.Nutrients · 2025Article
- Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.BMC cancer · 2024Article
- Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study.The journal of nutrition, health & aging · 2024Observational
- PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping.BMC bioinformatics · 2023Article
- Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults.European journal of clinical nutrition · 2022Observational
- Article
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Authors and funding
19 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMalnutrition 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.
methodsWe 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.
resultsThe 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.
conclusionsNutritional 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.
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