Development and validation of a novel prognostic prediction system based on GLIM-defined malnutrition for colorectal cancer patients post-radical surgery

Front Nutr. 2024 Oct 22:11:1425317. doi: 10.3389/fnut.2024.1425317. eCollection 2024.

Abstract

Background: Malnutrition often occurs in patients with colorectal cancer. This study aims to develop a predictive model based on GLIM criteria for patients with colorectal cancer who underwent radical surgery.

Methods: From December 2015 to May 2021, patients with colorectal cancer who underwent radical surgery at our center were recruited for this study. We prospectively collected data on GLIM-defined malnutrition and other clinicopathological characteristics. Using Cox regeneration, we developed a novel nomogram for prognostic prediction, which was validated and compared to traditional nutritional factors for predictive accuracy.

Results: Among the 983 patients enrolled in this study, malnutrition was identified in 233 (23.70%) patients. Multivariate analysis indicated that GLIM-defined malnutrition is the independent risk factor for overall survival (HR = 1.793, 95% CI = 1.390-2.313 for moderate malnutrition and HR = 3.485, 95% CI = 2.087-5.818 for severe malnutrition). The novel nomogram based on the GLIM criteria demonstrated a better performance than existing criteria, with AUC of 0.729, 0.703, and 0.683 for 1-year, 3-year, and 5-year OS, respectively, in the validation cohort. In addition, the risk score determined by this system exhibited significantly poorer short-term and long-term clinical outcomes in high-risk groups in both malnourished and well-nourished patients.

Conclusion: Combining handgrip strength, serum albumin level, and TNM stage would help improve the predictive effect of GLIM criteria for colorectal cancer patients post-radical surgery and benefit the individual prognostic prediction of colorectal cancer.

Keywords: clinical nutrition; clinical outcomes; colorectal cancer; global leadership initiative on malnutrition; nomogram.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the National Natural Science Foundation of China (Nos. 82100951 and 81770884), the Wenzhou Municipal Science and Technology Bureau (No. Y2023487), and the Natural Science Foundation of Zhejiang Province (Nos. LQ21H070003 and LQ24H070008).