Wang Jialin, Kong Chao, Pan Fumin, Lu Shibao
Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing 100000, China.
J Clin Med. 2023 Feb 6;12(4):1292. doi: 10.3390/jcm12041292.
Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis.
Asymptomatic elderly residents in the training ( = 438) and validation groups ( = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves.
The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed.
The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.
鉴于骨质疏松症的高患病率和隐匿性发病，新型早期筛查工具的开发迫在眉睫。因此，本研究试图构建一个用于预测骨质疏松症的列线图临床预测模型。
招募了训练组（n = 438）和验证组（n = 146）中的无症状老年居民。对参与者进行骨密度检查并收集临床数据。进行逻辑回归分析。构建了逻辑列线图临床预测模型和在线动态列线图临床预测模型。通过ROC曲线、校准曲线、DCA曲线和临床影响曲线对列线图模型进行验证。
基于性别、教育程度和体重构建的列线图临床预测模型具有良好的通用性，具有中等预测价值（AUC > 0.7）、更好的校准和更好的临床效益。构建了在线动态列线图。
列线图临床预测模型易于推广，可帮助家庭医生和基层社区医疗机构更好地对普通老年人群进行骨质疏松症筛查，实现疾病的早期发现和诊断。
