ORIGINAL RESEARCH article

Front. Oncol.

Sec. Surgical Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1533368

Construction of a Risk Prediction Model for Falls in Elderly Lung Cancer Patients with Sarcopenia

Provisionally accepted
Qing  WangQing Wang1Xiao  HanXiao Han2Jun  ZhangJun Zhang1Mengying  HuMengying Hu1Jiaojiao  XuJiaojiao Xu3Qiongqiong  AiQiongqiong Ai3Hequn  WeiHequn Wei3Jiao  YuJiao Yu3Haiping  MaHaiping Ma3*
  • 1School of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, China
  • 2The Affiliated Stomatological Hospital of Nanchang University, Nanchang, China
  • 3Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China

The final, formatted version of the article will be published soon.

Background: To explore the risk factors associated with falls in elderly lung cancer patients with sarcopenia, construct a predictive model, and validate its performance.This cross-sectional study involved 316 lung cancer patients with sarcopenia who were hospitalized in the oncology, thoracic surgery, and respiratory medicine departments of a tertiary hospital in Jiangxi Province between January 2023 and December 2023. Data were collected through questionnaires and physical measurements. A logistic regression predictive model was developed on the basis of independent risk factors.The incidence of falls among elderly lung cancer patients with sarcopenia was 19.94%. Multivariate logistic regression analysis identified multiple metastases, nocturia (≥3 times per night), sleep disorders, frailty, and malnutrition as independent risk factors for falls. The Hosmer -Lemeshow test indicated good model fit (X 2 =5.353, P=0.719), with an overall predictive accuracy of 83.7%. The area under the ROC curve (AUC) was 0.832, and the Youden index reached a maximum of 0.577, corresponding to a sensitivity of 74.7%, specificity of 83.0%, and an optimal cut-off value of 0.221.The risk prediction model for falls in elderly lung cancer patients with sarcopenia, which is based on independent predictors, demonstrated good predictive performance. This model facilitates the timely identification of high-risk patients, providing scientific evidence to support the development of precise clinical management strategies.

Keywords: Elderly, lung cancer, Sarcopenia, falls, predictive model

Received: 23 Nov 2024; Accepted: 03 Jun 2025.

Copyright: © 2025 Wang, Han, Zhang, Hu, Xu, Ai, Wei, Yu and Ma. 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: Haiping Ma, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.