A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer

Objective This study aimed to construct a nomogram based on body composition parameters and the prognostic nutritional index (PNI) using quantitative computed tomography (QCT) to predict early postoperative complications in patients with colorectal cancer (CRC). Materials and Methods We retrospectively analyzed the data of 157 patients who underwent radical resection for CRC between January 2019 and April 2024. All patients underwent QCT 1 month prior to surgery. Body composition was assessed at the level of the third lumbar vertebra, including measurements of the visceral fat area, subcutaneous fat area, and intramuscular fat infiltration (MFI) of the posterior vertebral muscles. The visceral-to-subcutaneous fat ratio (VSR) was calculated. Results Among the 157 patients, 31 (19.7%) experienced early postoperative complications. Univariate analysis revealed that the PNI, albumin level, VSR, and MFI were significantly associated with these complications. Multivariate logistic regression analysis identified the PNI (odds ratio [OR] = 0.801; 95% confidence interval (CI): 0.653–0.983), VSR (OR = 3.084; 95% CI: 1.365–6.968), and MFI (OR = 1.074; 95% CI: 1.009–1.145) as independent risk factors for early postoperative complications in CRC. The areas under the receiver operating characteristic curves for the PNI, VSR, MFI, and nomogram model for predicting postoperative complications were 0.796, 0.798, 0.648, and 0.879, respectively. Based on these three independent risk factors, the nomogram demonstrated good discrimination, calibration, goodness of fit, and clinical utility. Conclusions The nomogram model utilizing QCT-based body composition metrics and the PNI exhibited strong predictive capability for early postoperative complications in patients with CRC.