Hancox Zoe, Kingsbury Sarah R, Conaghan Philip G, Clegg Andrew, Relton Samuel D
School of Computing, University of Leeds, Leeds, UK.
Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.
Rheumatology (Oxford). 2025 Aug 1;64(8):4589-4598. doi: 10.1093/rheumatology/keaf185.
This study aimed to predict the risk of requiring a primary hip or knee replacement 1 and 5 years in advance, using clinical codes.
Primary care clinical codes, sourced from ResearchOne Electronic Health Records between 1999 and 2014, were used to represent patient pathways prior to hip or knee replacement. Patient records were used to train and test models for hip or knee replacement, 1 and 5 years in advance. Temporal graphs were constructed from clinical codes to predict hip and knee replacement risk, where nodes are clinical codes, and edges are the time between primary care visits. Hip and knee replacement cases were matched to controls by age, sex and Index of Multiple Deprivation. The model was validated on unseen data, with performance measured using area under the receiver operator curve (AUROC), calibration and area under the precision recall curve (AUPRC), recalibrating for class imbalance.
For knee replacement prediction, AUROC was 0.915 (95% CI 0.914, 0.916) (1 year) and 0.955 (95% CI 0.954, 0.956) (5 years) with AUPRCs of 0.353 (95% CI 0.302, 0.403) and 0.442 (95% CI 0.382, 0.503), respectively. For hip replacement prediction, AUROC was 0.919 (95% CI 0.918, 0.920) (1 year) and 0.967 (95% CI 0.966, 0.968) (5 years), with AUPRCs of 0.409 (95% CI 0.366, 0.451) and 0.879 (95% CI 0.833, 0.924), respectively.
Hip and knee replacement risk can be predicted up to 5 years in advance, with a temporal-graph based artificial intelligence model achieving the best performance. This may be used for planning preventative treatment or triaging patients.
本研究旨在利用临床编码提前1年和5年预测初次髋关节或膝关节置换的风险。
从1999年至2014年的ResearchOne电子健康记录中获取的初级保健临床编码，用于代表髋关节或膝关节置换术前的患者诊疗路径。利用患者记录对提前1年和5年的髋关节或膝关节置换模型进行训练和测试。根据临床编码构建时间图以预测髋关节和膝关节置换风险，其中节点为临床编码，边为初级保健就诊之间的时间。髋关节和膝关节置换病例按年龄、性别和多重剥夺指数与对照组进行匹配。该模型在未见过的数据上进行验证，使用受试者工作特征曲线下面积（AUROC）、校准和精确召回率曲线下面积（AUPRC）来衡量性能，并针对类别不平衡进行重新校准。
对于膝关节置换预测，1年时AUROC为0.915（95%CI 0.914，0.916），5年时为0.955（95%CI 0.954，0.956），AUPRC分别为0.353（95%CI 0.302，0.403）和0.442（95%CI 0.382，0.503）。对于髋关节置换预测，1年时AUROC为0.919（95%CI 0.918，0.920），5年时为0.967（95%CI 0.966，0.968），AUPRC分别为0.409（95%CI 0.366，0.451）和0.879（95%CI 0.833，0.924）。
基于时间图的人工智能模型能提前5年预测髋关节和膝关节置换风险，且性能最佳。这可用于规划预防性治疗或对患者进行分类。
