The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

Journal: Clinical nutrition (Edinburgh, Scotland)
PMID: 38603972

Abstract

BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information.

Authors

  • Tiantian Wu
    College of Life Science, Northwest A&F University, 712100, Shaanxi, Yangling, China.
  • Hongxia Xu
    Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China. Electronic address: hx_xu2015@163.com.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Fuxiang Zhou
    Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, Wuhan 430071, China.
  • Zengqing Guo
    Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, 350014, China. Electronic address: gzq_005@126.com.
  • Kunhua Wang
    Department of Gastrointestinal Surgery, Institute of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
  • Min Weng
    Department of Urology, The First Affiliated Hospital of Ningbo University, Ningbo, China.
  • Chunling Zhou
    The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Yuan Lin
  • Suyi Li
  • Ying He
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
  • Qinghua Yao
    Department of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital and Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Hanping Shi
    Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. Electronic address: shihp@ccmu.edu.cn.
  • Chunhua Song
    Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China.

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