Risk factors analysis and nomogram model construction of refeeding syndrome after esophageal cancer surgery

TL;DR

This study creates a predictive model to identify patients at high risk of refeeding syndrome after esophageal cancer surgery, helping with early detection and personalized nutrition strategies.

Contribution

The study introduces a novel nomogram model integrating key risk factors for refeeding syndrome after esophageal cancer surgery.

Findings

Age, diabetes, pre-feeding albumin, nutritional support, and rapid feeding are significant risk factors for refeeding syndrome.

The nomogram model demonstrated strong predictive performance with AUCs of 0.813 and 0.800 in internal and external validation.

The model provides a reliable tool for early identification and individualized management of high-risk patients.

Abstract

To construct and verify the risk prediction nomogram model of postoperative refeeding syndrome (RFS) in patients with esophageal cancer, and to provide a basis for early identification of high-risk groups and development of individualized nutritional intervention strategies.

A retrospective observational study design was used. Patients who underwent esophageal cancer surgery in a Grade A tertiary hospital in Shanghai from January 2023 to August 2024 were selected as the modeling group, and patients who underwent esophageal cancer surgery from September 2024 to June 2025 were selected as the validation group. RFS was diagnosed according to the criteria of the American Society of Parenteral and Enteral Nutrition. Independent risk factors were screened by univariate analysis and multivariate logistic regression, and a nomogram model was constructed based on significant variables. Bootstrap method was used for internal validation, and independent validation group was used for external validation. The area under the receiver operating characteristic curve (AUC), Brier score, Hosmer-Lemeshow test and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical effectiveness of the model.

The total incidence of RFS was 21.74% (100/460), 22.26% (69/310) in the modeling group and 20.67% (31/150) in the validation group. Multivariate analysis showed that age, diabetes mellitus, prealbumin before feeding, albumin before feeding, parenteral nutrition support and rapid enteral feeding were independent risk factors (P< 0.05). Additional albumin supplementation was a protective factor (P< 0.05). The AUC of internal validation was 0.813 (95% CI: 0.756–0.869), and the AUC of external validation was 0.800 (95% CI: 0.709–0.890). The calibration curve fitted well, and the Hosmer-Lemeshow test P >0.05. DCA shows that net income can be obtained in a wide range.

The nomogram model constructed in this study integrates core predictive factors such as age, history of diabetes, pre-feeding albumin level, nutritional support method and speed. It shows good predictive efficacy in both the internal and external validation cohorts and can provide a quantitative tool for the early screening and individualized nutritional management of patients at high risk of RFS after esophageal cancer surgery.

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