开发和验证预测类风湿性关节炎骨质疏松症风险的列线图
Development and validation of a nomogram predicting osteoporosis risk in rheumatoid arthritis.
摘要(中文译文)
类风湿关节炎（RA）会增加骨质疏松症的风险，但整合RA特异性临床和代谢因素以预测骨质疏松症风险的工具仍然有限。我们旨在开发并验证一个实用的RA患者骨质疏松症风险预测列线图。在这项单中心回顾性研究中，分析了349例具有可用DXA数据的RA患者；132例（37.8%）患有骨质疏松症。研究使用了训练队列（n=250；骨质疏松症=92）和时间验证队列（n=99；骨质疏松症=40，在研究期间较晚入组）。候选预测因素包括临床、功能和实验室变量。逐步后退法逻辑回归确定了独立预测因素，并将其纳入列线图。通过区分度（AUROC）、校准度（校准曲线和Hosmer-Lemeshow检验）、决策曲线分析（DCA）和风险分层评估模型性能。女性、较高的健康评估问卷-残疾指数（HAQ-DI）、碱性磷酸酶（ALP）升高、ApoA1/ApoB比值升高、游离脂肪酸（FFA）升高和体重指数（BMI）较低是骨质疏松症的独立预测因素，并被纳入列线图。该模型的AUROC分别为0.812（训练队列）和0.788（验证队列），显示出良好的校准度（Hosmer-Lemeshow P > 0.05），并在DCA中于一系列阈值概率范围内提供了正向净获益。基于列线图的风险分层（低/中/高）可区分骨质疏松症风险，中危组和高危组的比值比具有统计学显著性。所提出的列线图基于易于获取的临床和实验室指标构建，在识别骨质疏松症风险升高的RA患者方面表现出良好的区分度、校准度和临床实用性，可能有助于靶向筛查和早期干预。然而，该模型在不同人群中的性能仍不清楚，在任何临床应用之前，前瞻性多中心外部验证至关重要。
Abstract
Rheumatoid arthritis (RA) increases the risk of osteoporosis, but tools that integrate RA-specific clinical and metabolic factors to predict osteoporosis risk are limited. We aimed to develop and validate a practical risk prediction nomogram for osteoporosis in RA patients. In this single-center retrospective study, 349 RA patients with available DXA data were analyzed; 132 (37.8%) had osteoporosis. A training cohort ( = 250; osteoporosis = 92) and a temporal validation cohort ( = 99; osteoporosis = 40, enrolled later in the study period) were used. Candidate predictors included clinical, functional, and laboratory variables. Stepwise backward logistic regression identified independent predictors that were incorporated into a nomogram. Model performance was assessed by discrimination (AUROC), calibration (calibration curve and Hosmer-Lemeshow test), decision curve analysis (DCA), and risk stratification. Female sex, higher health assessment questionnaire-disability index (HAQ-DI), elevated alkaline phosphatase (ALP), increased ApoA1/ApoB ratio, higher free fatty acids (FFA), and lower body mass index (BMI) were independent predictors of osteoporosis and were included in the nomogram. The model yielded AUROCs of 0.812 (training) and 0.788 (validation), showed good calibration (Hosmer-Lemeshow > 0.05), and provided positive net benefit across a range of threshold probabilities in DCA. Nomogram-based risk strata (low/medium/high) discriminated osteoporosis risk with statistically significant odds ratios for medium and high groups. The proposed nomogram, built from readily available clinical and laboratory measures, demonstrates good discrimination, calibration, and clinical utility for identifying RA patients at elevated risk of osteoporosis, and may facilitate targeted screening and early intervention. However, the model's performance in diverse populations remains unknown, and prospective multicenter external validation is essential before any clinical application.
如何引用
AMA
Lujing Wang, Yueting Gu, Xiaochun Zhang, Xinglan Bao, Yan Chen, Hongjian Yuan. Development and validation of a nomogram predicting osteoporosis risk in rheumatoid arthritis.. Frontiers in medicine. 2026; doi:10.3389/fmed.2026.1747090.
APA
Lujing Wang, Yueting Gu, Xiaochun Zhang, Xinglan Bao, Yan Chen, Hongjian Yuan (2026). Development and validation of a nomogram predicting osteoporosis risk in rheumatoid arthritis.. Frontiers in medicine. https://doi.org/10.3389/fmed.2026.1747090
GB/T 7714
Lujing Wang, Yueting Gu, Xiaochun Zhang, Xinglan Bao, Yan Chen, Hongjian Yuan. Development and validation of a nomogram predicting osteoporosis risk in rheumatoid arthritis.[J]. Frontiers in medicine, 2026 doi:10.3389/fmed.2026.1747090.
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