Construction of a risk prediction model for falls in elderly lung cancer patients with sarcopenia
BackgroundTo explore the risk factors associated with falls in elderly lung cancer patients with sarcopenia, construct a predictive model, and validate its performance.MethodsThis cross-sectional study involved 316 lung cancer patients with sarcopenia who were hospitalized in the oncology, thoracic...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1533368/full |
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| Summary: | BackgroundTo explore the risk factors associated with falls in elderly lung cancer patients with sarcopenia, construct a predictive model, and validate its performance.MethodsThis 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.ResultsThe 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 (X2 = 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.ConclusionThe 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. |
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| ISSN: | 2234-943X |