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Research progress on predictive models for malnutrition in cancer patients

Authors :
Pengcheng Zheng
Bo Wang
Yan Luo
Ran Duan
Tong Feng
Source :
Frontiers in Nutrition, Vol 11 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Disease-related malnutrition is a prevalent issue among cancer patients, affecting approximately 40–80% of those undergoing treatment. This condition is associated with numerous adverse outcomes, including extended hospitalization, increased morbidity and mortality, delayed wound healing, compromised muscle function and reduced overall quality of life. Moreover, malnutrition significantly impedes patients’ tolerance of various cancer therapies, such as surgery, chemotherapy, and radiotherapy, resulting in increased adverse effects, treatment delays, postoperative complications, and higher referral rates. At present, numerous countries and regions have developed objective assessment models to predict the risk of malnutrition in cancer patients. As advanced technologies like artificial intelligence emerge, new modeling techniques offer potential advantages in accuracy over traditional methods. This article aims to provide an exhaustive overview of recently developed models for predicting malnutrition risk in cancer patients, offering valuable guidance for healthcare professionals during clinical decision-making and serving as a reference for the development of more efficient risk prediction models in the future.

Details

Language :
English
ISSN :
2296861X
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Nutrition
Publication Type :
Academic Journal
Accession number :
edsdoj.98b575c9f1c640eab0e02b0b33e5cf03
Document Type :
article
Full Text :
https://doi.org/10.3389/fnut.2024.1438941