骨折预测在治疗骨质疏松症患者中至关重要,并且是许多骨折预防指南的组成部分。我们旨在通过培训和验证两个队列中的短期和长期骨折风险预测模型来确定当代人群中最相关的临床骨折风险因素。我们使用传统和机器学习生存模型来预测椎体的风险,基于临床危险因素的髋部和任何骨折,瑞士全国骨质疏松症登记处参与者的T评分和治疗史(N=5944名绝经后妇女,2015年1月至2022年10月的中位随访时间为4.1年;随访期间共1190例骨折).独立验证队列包括来自英国生物银行的5474名绝经后妇女,随访期间发生290例骨折。计算了Uno\'sC指数和接收器工作特性曲线下的时间依赖性面积,以评估不同机器学习模型(随机生存森林和极限梯度提升)的性能。在独立验证集中,椎体骨折的C指数为0.74[0.58,0.86],在第2年,髋部骨折为0.83[0.7,0.94],任何骨折为0.63[0.58,0.69],并且这些值在长达7年的更长时间估计中进一步增加。相比之下,FRAX®Switzerland计算的10年骨折概率对于严重骨质疏松性骨折为0.60[0.55,0.64],对于髋部骨折为0.62[0.49,0.74].用Shapley加性解释(SHAP)值确定的最重要的变量是年龄,T评分和先前的骨折,而跌倒次数是髋部骨折的重要预测指标。传统和机器学习模型的性能都显示出相似的C指数。我们得出结论,骨折风险可以通过包括腰椎T评分来改善,骨小梁评分,跌倒次数和最近的骨折,和处理信息对骨折预测有显著影响。
骨折预测对于治疗骨质疏松症患者至关重要。我们开发并验证了传统和机器学习模型,以预测短期和长期骨折风险,并确定最相关的临床骨折风险因素。臀部,以及当代人群中的任何骨折。我们在瑞士骨质疏松症登记处使用了5944名绝经后妇女的数据,并在英国生物银行的5474名妇女中验证了我们的发现。我们的机器学习模型表现良好,椎体骨折的C指数值为0.74[0.58,0.86],在第2年,髋部骨折为0.83[0.7,0.94],任何骨折为0.63[0.58,0.69],并且这些值在长达7年的更长时间估计中进一步增加。相比之下,瑞士FRAX®具有较低的C指数值(主要骨折为0.60[0.55,0.64],髋部骨折概率为0.62[0.49,0.74]超过10年)。确定的关键预测因素包括年龄,T分数,先前的骨折,和跌倒次数。我们得出的结论是,结合了更广泛的临床因素,以及腰椎T评分,秋天的历史,近期骨折,和治疗信息,可以改善骨质疏松管理中的骨折风险评估。传统和机器学习模型在预测骨折方面表现出相似的效果。
Fracture prediction is essential in managing patients with osteoporosis, and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training and validating short- and long-term fracture risk prediction models in two cohorts. We used traditional and machine learning survival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1 years between January 2015 and October 2022; a total of 1190 fractures during follow-up). The independent validation cohort comprised 5474 postmenopausal women from the UK Biobank with 290 incident fractures during follow-up. Uno\'s C-index and the time-dependent area under the receiver operating characteristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 years. In comparison, the 10-year fracture probability calculated with
FRAX® Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both traditional and machine learning models showed similar C-indices. We conclude that fracture risk can be improved by including the lumbar spine T-score, trabecular bone score, numbers of falls and recent fractures, and treatment information has a significant impact on fracture prediction.
Fracture prediction is essential in managing patients with osteoporosis. We developed and validated traditional and machine learning models to predict short- and long-term fracture risk and identify the most relevant clinical fracture risk factors for vertebral, hip, and any fractures in contemporary populations. We used data from 5944 postmenopausal women in a Swiss osteoporosis registry and validated our findings with 5474 women from the UK Biobank. Our machine learning models performed well, with C-index values of 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 years. In contrast,
FRAX® Switzerland had lower C-index values (0.60 [0.55, 0.64] for major fractures and 0.62 [0.49, 0.74] for hip fracture probabilities over 10 years). Key predictors identified included age, T-scores, prior fractures, and number of falls. We conclude that incorporating a broader range of clinical factors, as well as lumbar spine T-scores, fall history, recent fractures, and treatment information, can improve fracture risk assessments in osteoporosis management. Both traditional and machine learning models showed similar effectiveness in predicting fractures.