ORIGINAL RESEARCH article
Front. Nutr.
Sec. Clinical Nutrition
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1644583
A logistic regression-based nomogram model for predicting postoperative prognosis in elderly patients of primary hospitals
Provisionally accepted
- Xuzhou Central Hospital, Xuzhou, China
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Objective: To develop and validate a logistic regression model predicting postoperative malnutrition risk in elderly patients using clinical, dietary, and nutritional data. Methods: We analyzed 241 elderly patients (lung cancer lobectomy/esophageal cancer resection) admitted from January 2024 to December 2024. Participants were randomized 7:3 into training (n=168) and validation (n=73) sets. Prognostic factors were identified via univariate analysis and multivariate logistic regression to build a predictive model. Performance was assessed using C-index, calibration curves, and receiver operating characteristic (ROC) analysis. Results: Baseline characteristics were comparable between sets (P>0.05). Multivariate analysis identified number of daily food types, cereal intake, high-quality protein intake, body mass index, serum albumin, and pre-albumin as malnutrition predictors (all P<0.05). The model achieved C-indices of 0.834 (training set) and 0.703 (validation set). The area under the ROC curves were 0.834 (95% CI: 0.760-0.908) and 0.703 (95% CI: 0.539-0.866), respectively, with good calibration curve fit. Conclusion: This validated model effectively predicts postoperative malnutrition risk in elderly surgical patients. Its visualization tools simplify complex nutritional assessment, offering a practical solution for resource-limited settings to improve postoperative care in primary hospitals.
Keywords: Thoracic Surgery, Postoperative malnutrition, Primary hospitals, elderly patients, dietary diversity, Nutritional Status, Nomogram prediction model
Received: 11 Jun 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 An, Wei, Meng, Xu, Zhao and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wen Tong, sunshine6243@163.com
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