A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data. Issue 8 (August 2021)

• Record Type: Journal Article
• Authors: Yin, Liangyu; Song, Chunhua; Cui, Jiuwei; Lin, Xin; Li, Na; Fan, Yang; Zhang, Ling; Liu, Jie; Chong, Feifei; Wang, Chang; Liang, Tingting; Liu, Xiangliang; Deng, Li; Li, Wei; Yang, Mei; Yu, Jiami; Wang, Xiaojie; Liu, Xing; Yang, Shoumei; Zuo, Zheng; Yuan, Kaitao; Yu, Miao; Cong, Minghua; Li, Zengning; Jia, Pingping; Li, Suyi; Guo, Zengqing; Shi, Hanping; Xu, Hongxia
• Is Part Of: Clinical nutrition. Volume 40:Issue 8(2021)
• Page Start: 4958
• Page End: 4970
• Publication Date: 2021-08
• Subjects: K-means clustering -- Machine learning -- Malnutrition -- Cancer -- Real-world study; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2021.06.028
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background and aims: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This study established a machine learning (ML)-based, individualized decision system to identify and grade malnutrition using large-scale data from cancer patients. Methods: This was an observational, nationwide, multicenter cohort study that included 14134 cancer patients from five institutions in four different geographic regions of China. Multi-stage K-means clustering was performed to isolate and grade malnutrition based on 17 core nutritional features. The effectiveness of the identified clusters for reflecting clinical characteristics, nutritional status and patient outcomes was comprehensively evaluated. The study population was randomly split for model derivation and validation. Multiple ML algorithms were developed, validated and compared to screen for optimal models to implement the cluster prediction. Results: A well-nourished cluster (n = 8193, 58.0%) and a malnourished cluster with three phenotype-specific severity levels (mild = 2195, 15.5%; moderate = 2491, 17.6%; severe = 1255, 8.9%) were identified. The clusters showed moderate agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria. The severity of malnutrition was negatively associated with the nutritional status, physical status, quality of life, and short-term outcomes, and was monotonically correlated with reduced overall survival. A multinomial logistic regression was found to be the optimal ML algorithm, and models built based on this algorithm showed almost perfect performance to predict the clusters in the validation data. Conclusions: This study developed a fusion decision system that can be used to facilitate the identification and severity grading of malnutrition in patients with cancer. Moreover, the study workflow is flexible, and might provide a generalizable solution for the artificial intelligence-based assessment of malnutrition in a wider variety of scenarios.