Volume 23 Issue 12
Dec.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(12): 2143-2147
HOU Hui, WAN Yan, ZHU Congyan, LI Bowen, CHEN Li. A prediction model for enteral nutrition interruption after gastric cancer surgery established using machine learning algorithms[J]. Chinese Journal of General Practice, 2025, 23(12): 2143-2147. doi: 10.16766/j.cnki.issn.1674-4152.004311
Citation: HOU Hui, WAN Yan, ZHU Congyan, LI Bowen, CHEN Li. A prediction model for enteral nutrition interruption after gastric cancer surgery established using machine learning algorithms[J]. Chinese Journal of General Practice, 2025, 23(12): 2143-2147. doi: 10.16766/j.cnki.issn.1674-4152.004311
shu

A prediction model for enteral nutrition interruption after gastric cancer surgery established using machine learning algorithms

doi: 10.16766/j.cnki.issn.1674-4152.004311

  • Department of Gastric Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China

Funds:

 82473055

  • Received Date: 2025-01-16
    Available Online: 2026-03-13
    Abstract
  •   Objective  Gastric cancer patients face a high risk of enteral nutrition interruption (ENI) after surgery. However, no consensus has been reached regarding the factors influencing postoperative ENI. This study aims to develop an individualized risk prediction model for postoperative ENI in gastric cancer patients using machine learning algorithms based on clinical data, thereby providing guidance for the assessment, prevention, and management of ENI.  Methods  A total of 190 patients with gastric cancer were recruited from Ward 1 and Ward 2 of the Department of General Surgery (Gastric Surgery Division) at the First Affiliated Hospital of Nanjing Medical University between May 2023 and April 2025. Patients were randomly divided into a training set (n=133) and a validation set (n=57) in a ratio of 7∶3. Three machine learning algorithms, namely logistic regression (LR), random forest (RF), and support vector machine (SVM), were adopted to construct prediction models, and model performance were analyzed. The optimal model performance was verified using the validation set data.  Results  LASSO regression identified the following variables associated with ENI after gastric cancer surgery: age, VAS score at 24 hours after surgery, time to first postoperative ambulation, distance of ambulation on postoperative day 1, postoperative complications, tumor stage, and types of antibiotics. The AUC values of the LR, RF, and SVM models were 0.768, 0.893, and 0.861, respectively, with the RF model demonstrating superior predictive performance. The risk threshold range of the RF model was 0.03 to 0.95, within which implementing targeted interventions based on the prediction results of the RF model can provide substantial net clinical benefits for ENI prevention. Using the validation set, the RF model constructed with LASSO-selected variables achieved an AUC of 0.853.  Conclusion  The prediction performances of the three models established based on machine learning algorithms were all satisfactory. In particular, the RF model demonstrated superior predictive efficiency, enabling effective assessment of ENI risk after gastric cancer surgery and reliable identification of high-risk patients.

     

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