Development and evaluation of a nomogram model for predicting malnutrition in patients with colorectal cancer

Malnutrition is a common complication in patients with colorectal cancer (CRC), negatively impacting treatment outcomes and quality of life. Early identification of patients at risk of malnutrition can aid in timely interventions. The objective of this study was to develop and evaluate a nomogram model for predicting malnutrition in CRC patients.

This retrospective study was conducted at our hospital from January 2022 to December 2024. Nutritional assessments were based on parameters such as body mass index (BMI), serum albumin (ALB), hemoglobin (HGB), prognostic nutritional index (PNI), and others. Univariate logistic regression analysis was initially performed to identify potential risk factors for malnutrition. Statistically significant factors (p < 0.05) were included in a multivariate logistic regression model, which was used to construct a nomogram for predicting malnutrition risk. The nomogram's performance was evaluated using the area under the curve (AUC) from receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

Multivariate analysis identified six independent predictors: age ≥65 years (OR = 2.216, 95% CI: 1.312-3.843, p = 0.003), TNM stage IV (OR = 1.886, 95% CI: 1.091-3.278, p = 0.025), Karnofsky Performance Status (KPS) ≤80 (OR = 2.581, 95% CI: 1.525-4.368, p < 0.001), hemoglobin <110 g/L (OR = 0.317, 95% CI: 0.185-0.561, p < 0.001), prealbumin <200 g/L (OR = 0.513, 95% CI: 0.281-0.902, p = 0.020), and prolonged bed rest (OR = 9.739, 95% CI: 2.834-31.187, p < 0.001). The nomogram demonstrated good discrimination with an area under the curve (AUC) of 0.819 (95% CI: 0.731-0.895), sensitivity of 71.3%, specificity of 86.6%, and negative predictive value of 89.6%. Calibration was excellent (Hosmer-Lemeshow p = 0.929; C-index = 0.798). Decision curve analysis confirmed favorable clinical utility.

The nomogram model, incorporating six risk factors, offers a reliable and effective tool for predicting malnutrition in CRC patients. It provides clinicians with an important decision-making aid for early intervention and management of malnutrition.