Xuexing Wang ¹ Jinsong Xu ^1* Rong Zhang ^2* Jie Chu ^3* Chunmei Chen ^1* Chunmei Wei ^1*

• ¹ First People’s Hospital of Anning City (Jinfang Branch), Anning, China
• ² Yunnan Cancer Hospital, Kunming, Yunnan Province, China
• ³ Ziyang People's Hospital, Ziyang, China

Objective The aim of this study is to investigate the relationship and prognostic significance of serum neutrophil-lymphocyte ratio (NLR), systemic immune-inflammatory index (SII), platelet-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) in colorectal cancer (CRC) patients with malnutrition, as well as to construct a nomogram for predicting the onset of malnutrition.The clinical data of 391 inpatients who were hospitalised from December 1,2021 to January 31, 2023 the diagnosis of CRC were selected and divided into a malnutrition group (121 cases) and a well-nourished group (270 cases) according to whether they were malnourished or not. Focusing on comparing the differences in serum NLR, PLR, SII index, PNI index and general information between the two groups, the Binary logistics regression analysis was used to analyse the factors affecting malnutrition, and receiver operating characteristic (ROC) curves were established to assess the predictive value of serum NLR, PLR, SII index, and PNI index individually and jointly for malnutrition, and to calculate the optimal predictive thresholds.Finally a highly accurate clinical predictive nomogram was constructed. Results Compared with the well-nourished group, the malnourished group had higher serum NLR, SII index, PLR and lower PNI index levels, with statistically significant differences( P<0.001). The area under the curve of NLR, SII index,PLR, and PNI index alone and in combination predicted a poor prognosis of 0.705, 0.665, 0.636,0.773, and 0.784, respectively.After conducting Logistic regression analysis, the nomogram, which included BMI, NRS2002, long-term bed rest, and PNI, demonstrated strong predictive capabilities.Decision curves highlighted the clinical utility of the predictive nomograms. The receiver operating characteristic curve revealed strong discrimination (area under the curve [AUC] = 0.958, 95% CI: 0.937-0.979).Additionally, the ROC analysis indicated a sensitivity of 0.843 and specificity of 0.937. Calibration curves exhibited excellent concordance between nomogram predictions and observed outcomes.Decision curves highlighted the clinical utility of the predictive nomograms. Conclusion Serum NLR,SII index , PLR, and PNI are significant predictive factors for the development of malnutrition in patients with CRC. These indices, whether considered individually or collectively, possess clinical relevance in forecasting malnutrition. Furthermore, the creation of an innovative nomogram prediction model offers considerable clinical utility.