Sun Li, Gu Hai-Yan, Xu Guan-Hua, Jiang Jia-Wei, Wang Ting-Ting, Li Dan-Dan, Cui Bai-Hong
Department of Orthopedics, Affiliated Hospital 2 of Nantong University, Nantong, China.
Department of Nursing, Affiliated Hospital 2 of Nantong University, Nantong, China.
Front Public Health. 2025 Jan 15;12:1526660. doi: 10.3389/fpubh.2024.1526660. eCollection 2024.
The aim of this study is to develop and validate a prediction model for fall risk factors in hospitalized older adults with osteoporosis.
A total of 615 older adults with osteoporosis hospitalized at a tertiary (grade 3A) hospital in Nantong City, Jiangsu Province, China, between September 2022 and August 2023 were selected for the study using convenience sampling. Fall risk factors were identified using univariate and logistic regression analyses, and a predictive risk model was constructed and visualized through a nomogram. Model performance was evaluated using the area under the receiver operator characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and clinical decision curve analysis, assessing the discrimination ability, calibration, and clinical utility of the model.
Based on logistic regression analysis, we identified several significant fall risk factors for older adults with osteoporosis: gender of the study participant, bone mineral density, serum calcium levels, history of falls, fear of falling, use of walking aids, and impaired balance. The AUC was 0.798 (95% CI: 0.763-0.830), with a sensitivity of 80.6%, a specificity of 67.9%, a maximum Youden index of 0.485, and a critical threshold of 121.97 points. The Hosmer-Lemeshow test yielded a χ value of 8.147 and = 0.419, indicating good model calibration. Internal validation showed a C-index of 0.799 (95% CI: 0.768-0.801), indicating the model's high discrimination ability. Calibration curves showed good agreement between predicted and observed values, confirming good calibration. The clinical decision curve analysis further supported the model's clinical utility.
The prediction model constructed and verified in this study was to predict fall risk for hospitalized older adults with osteoporosis, providing a valuable tool for clinicians to implement targeted interventions for patients with high fall risks.
本研究旨在开发并验证一个针对患有骨质疏松症的住院老年患者跌倒风险因素的预测模型。
采用便利抽样法，选取2022年9月至2023年8月期间在中国江苏省南通市一家三级甲等医院住院的615名患有骨质疏松症的老年患者进行研究。通过单因素和逻辑回归分析确定跌倒风险因素，并构建预测风险模型并通过列线图进行可视化。使用受试者工作特征曲线下面积（AUC）、Hosmer-Lemeshow拟合优度检验和临床决策曲线分析评估模型性能，评估模型的辨别能力、校准度和临床实用性。
基于逻辑回归分析，我们确定了患有骨质疏松症的老年患者的几个重要跌倒风险因素：研究参与者的性别、骨密度、血清钙水平、跌倒史、害怕跌倒、使用助行器以及平衡功能受损。AUC为0.798（95%可信区间：0.763 - 0.830），灵敏度为80.6%，特异度为67.9%，最大约登指数为0.485，临界阈值为121.97分。Hosmer-Lemeshow检验得出χ值为8.147，P = 0.419，表明模型校准良好。内部验证显示C指数为0.799（95%可信区间：0.768 - 0.801），表明模型具有较高的辨别能力。校准曲线显示预测值与观察值之间具有良好的一致性，证实校准良好。临床决策曲线分析进一步支持了该模型的临床实用性。
本研究构建并验证的预测模型可预测患有骨质疏松症住院老年患者的跌倒风险，为临床医生对跌倒风险较高的患者实施针对性干预提供了有价值的工具。
