An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study
An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study
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DOI：
10.1016/j.joca.2019.09.005
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发表时间：
2020-01-01
影响因子：
7
通讯作者：
Lindner, C.
中科院分区：
文献类型：
--
作者：
Gielis, W. P.;Weinans, H.;Lindner, C.
目的：设计一种以关节形状为重点的髋关节x线片自动化工作流程，并测试其对未来髋关节骨关节炎的预后价值。设计：我们使用了1002名CHECK-study参与者的基线和8年随访数据。在8年的随访中，主要终点是明确的髋关节骨关节炎（rHOA） （kelgrelawrence分级>= 2或关节置换术）。我们设计了一种从x线片中自动分割髋关节的方法。随后，我们应用机器学习算法（带有自动参数优化的弹性网络）来提供shape - score，这是一个描述未来基于关节形状的rHOA风险的单一值。我们使用基线人口统计学、体格检查和放射科医生评分建立并内部验证了预测模型，并使用曲线下面积（AUC）测试了Shape-Score的附加预后价值。缺失数据通过链式方程的多次拟合进行拟合。只包括相应腿部疼痛的髋部。结果：84%为女性，平均年龄56（±5.1）岁，平均BMI 26.3（±4.2）。在基线和完全随访时有疼痛的1044个髋关节中，143个显示放射学骨关节炎，42个被替换。91.5%的髋部有随访数据。Shape-Score是rHOA的显著预测因子（比值比每十进制增加5.21,95% ci(3.74-7.24)）。使用人口统计学、体格检查和放射科医师评分的预测模型显示AUC为0.795,95% ci（0.757-0.834）。加入Shape-Score后，AUC上升至0.864,95% ci（0.833-0.895）。结论：我们的Shape-Score，使用一种新的机器学习工作流程自动从x线片中提取，可以大大提高髋关节骨关节炎的风险预测。(C) 2019作者。由Elsevier Ltd代表国际骨关节炎研究学会出版。
Objective: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis.Design: We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (KellgreneLawrence grade >= 2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included.Results: 84% were female, mean age was 56 (+/- 5.1) years, mean BMI 26.3 (+/- 4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74-7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757-0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833-0.895).Conclusions: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis. (C) 2019 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.