SuperLearner方法用于根据中国老年患者的常规血液检测标志物预测新诊断骨质疏松症患者骨折的迫在眉睫的风险
SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.
摘要(中文译文)
目的：本研究旨在基于新诊断骨质疏松症患者的常规血液检测指标，利用机器学习方法开发2年骨折预测模型。方法：我们回顾性分析了2011年至2023年间晋江市医院收治的497例新诊断骨质疏松症且既往无骨折的老年患者（男性121例，女性376例）的数据。数据集被随机分为训练队列和验证队列。利用骨质疏松症初诊时各种易于获取的医学特征，采用线性、非线性和SuperLearner方法开发预测模型。使用曲线下面积（AUC）评估模型性能，并使用SHapley加法解释（SHapley Additive exPlanation）对表现最佳的模型进行解释。结果：在497例参与者中，共有229例（46.1%）在骨质疏松症诊断后2年内发生了骨折（任何部位）。SuperLearner模型在内部验证集中预测目标结局的AUC最高（0.677，95%置信区间：[0.591，0.762]）。诊断时年龄、血清钠水平及其他因素位列最重要的特征。年龄较大且伴有低钠血症、低天冬氨酸转移酶水平和低钙血症的患者，其迫近骨折风险增加。结论：我们成功开发并验证了针对新诊断骨质疏松症患者的骨折风险预测模型。常规血液检测指标与先进机器学习算法的结合已展现出预测骨质疏松症患者骨折的预测价值。
Abstract
PURPOSE: In this study, we aimed to develop 2-year fracture prediction models using machine learning approaches based on routine blood test markers in patients newly diagnosed with osteoporosis. METHODS: We retrospectively analyzed data from 497 elderly patients (121 men and 376 women) newly diagnosed with osteoporosis without prior fractures at Jinjiang Municipal Hospital between 2011 and 2023. The dataset was randomly divided into training and validation cohorts. Various readily available medical characteristics at the initial diagnosis of osteoporosis were used to develop the prediction models employing the linear, nonlinear, and SuperLearner approaches. Model performance was assessed using the area under the curve (AUC) and the SHapley Additive exPlanation was used to interpret the best-performing models. RESULTS: In total, 229 (46.1%) of the 497 participants experienced fractures (at any site) within 2 years of osteoporosis diagnosis. The SuperLearner model demonstrated the highest AUC (0.677, 95% confidence interval: [0.591, 0.762]) for predicting the outcome of interest in the internal validation set. Age at diagnosis, serum sodium levels, and other factors emerged as the top-ranked features. Older patients with hyponatremia, low aspartate transferase levels, and hypocalcemia have an increased imminent risk of fracture. CONCLUSIONS: We successfully developed and validated a fracture risk prediction model for patients newly diagnosed with osteoporosis. The combination of routine blood test markers and advanced machine learning algorithms has demonstrated a predictive value for forecasting fractures in individuals with osteoporosis.
如何引用
AMA
Bailu Chen, Jinshan Zhang, Yangzhen Fang, Chao Liu, Ming Chen. SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.. BMC musculoskeletal disorders. 2026; doi:10.1186/s12891-026-09768-z.
APA
Bailu Chen, Jinshan Zhang, Yangzhen Fang, Chao Liu, Ming Chen (2026). SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.. BMC musculoskeletal disorders. https://doi.org/10.1186/s12891-026-09768-z
GB/T 7714
Bailu Chen, Jinshan Zhang, Yangzhen Fang, Chao Liu, Ming Chen. SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.[J]. BMC musculoskeletal disorders, 2026 doi:10.1186/s12891-026-09768-z.
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