用两阶段模型融合临床与影像数据，提升髋部骨折风险预测敏感性。
An Advanced Two-Stage Model with High Sensitivity and Generalizability for Prediction of Hip Fracture Risk Using Multiple Datasets
- 分两阶段整合临床与双能X线扫描特征，逐步优化风险评估
- 相比传统T值和FRAX，敏感性更高，漏诊率显著降低
- 适用于老年群体早期筛查，尤其适合骨量减少者
髋部骨折是老年人致残、死亡及医疗负担的重要原因，亟需早期风险评估。现有工具如DXA T值和FRAX常因敏感性不足而遗漏高风险人群，尤其是无既往骨折或骨量减少者。为此，我们提出一种序列式两阶段模型，融合临床、人口学及功能变量（第一阶段）与DXA衍生特征（第二阶段），在男性骨质疏松症研究（MrOS）、骨质疏松性骨折研究（SOF）和英国生物样本库（UK Biobank）多数据集上验证。内部与外部测试均显示模型性能稳定且可迁移。相比T值和FRAX，该框架显著提升敏感性，减少漏诊，提供成本可控、个性化的早期风险评估方案。
原文摘要 · Abstract (English)
Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such as the DXA T-score and FRAX often lack sensitivity and miss individuals at high risk, particularly those without prior fractures or with osteopenia. To address this limitation, we propose a sequential two-stage model that integrates clinical and imaging information to improve prediction accuracy. Using data from the Osteoporotic Fractures in Men Study (MrOS), the Study of Osteoporotic Fractures (SOF), and the UK Biobank, Stage 1 (Screening) employs clinical, demographic, and functional variables to estimate baseline risk, while Stage 2 (Imaging) incorporates DXA-derived features for refinement. The model was rigorously validated through internal and external testing, showing consistent performance and adaptability across cohorts. Compared to T-score and FRAX, the two-stage framework achieved higher sensitivity and reduced missed cases, offering a cost-effective and personalized approach for early hip fracture risk assessment. Keywords: Hip Fracture, Two-Stage Model, Risk Prediction, Sensitivity, DXA, FRAX
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