个人科研网厅 | | 帮助
高级检索
当前位置： 首页 > 详情页

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

| 导出 | |

文献详情

资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE

作者：
Wu Tiantian[1] * ; Xu Hongxia[2] ; Li Wei[3] ; Zhou Fuxiang[4] ; Guo Zengqing[5] ; Wang Kunhua[6] ; Weng Min[7] ; Zhou Chunling[8] ; Liu Ming[9] ; Lin Yuan[10] ; Li Suyi[11] ; He Ying[12] ; Yao Qinghua[13] ; Shi Hanping[14,*2] ; Song Chunhua[1,*1] ;
机构： [1]Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China [2]Department of Clinical Nutrition, Daping Hospital, Army Medical University, Chongqing, China [3]Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, China [4]Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China [5]Department of Medical Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China [6]Department of Gastrointestinal Surgery, Institute of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China [7]Department of Clinical Nutrition, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China [8]The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China [9]Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China [10]Department of Gastrointestinal Surgery, Affiliated Cancer Hospital of Guangxi Medical University, Nanning, Guangxi, China [11]Department of Nutrition and Metabolism of Oncology, Affiliated Provincial Hospital of Anhui Medical University, Hefei, Anhui, China [12]Department of Clinical Nutrition, Chongqing General Hospital, Chongqing, China [13]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 [14]Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
出处：
DOI： 10.1016/j.clnu.2024.04.001
ISSN：

关键词： Colorectal cancer GLIM Malnutrition Machine learning Weight loss

摘要：
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.This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance.NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/).Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.Copyright © 2024 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

基金：
National Key Research and Development Program (No. 2022YFC2009600, 2022YFC2009601).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2025]版：
无
最新[2023]版：
大类 | 2 区 医学
小类 | 1 区 营养学
JCR分区：
出版当年[2024]版：
无
最新[2023]版：
Q1 NUTRITION & DIETETICS

影响因子： 最新[2023版] 最新五年平均 出版当年[2024版] 出版当年五年平均 出版前一年[2023版]

第一作者：
第一作者机构： [1]Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
通讯作者：
通讯机构： [1]Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China [14]Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China [*1]Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, China [*2]Department of Gastrointestinal Surgery|Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Tieyi Road 10#, Haidian District, Beijing 100038, China
推荐引用方式(GB/T 7714)：
Wu Tiantian,Xu Hongxia,Li Wei,et al.The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information[J].CLINICAL NUTRITION.2024,43(5):1151-1161.doi:10.1016/j.clnu.2024.04.001.
APA：
Wu Tiantian,Xu Hongxia,Li Wei,Zhou Fuxiang,Guo Zengqing...&Song Chunhua.(2024).The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.CLINICAL NUTRITION,43,(5)
MLA：
Wu Tiantian,et al."The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information".CLINICAL NUTRITION 43..5(2024):1151-1161

相关文献

[1]Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge [2]Ensemble learning system to identify nutritional risk and malnutrition in cancer patients without weight loss information [3]Regulatory effect of wee1 on proliferation of colorectal cancer cells [4]Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning [5]Classification Tree-Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients. [6]Hereditary nonpolyposis colorectal cancer(Open Access) [7]Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study [8]Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson's disease [9]Machine Learning-Based Integration of Metabolomics Characterisation Predicts Progression of Myopic Retinopathy in Children and Adolescents [10]Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population

资源点击量：56918 今日访问量：0 总访问量：1772 更新日期：2025-01-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 昆明医科大学第一附属医院 技术支持：重庆聚合科技有限公司 地址：云南省昆明市西昌路295号(650032)