Chong Yuk Yee, Lau Chun Man Lawrence, Jiang Tianshu, Wen Chunyi, Zhang Jiang, Cheung Amy, Luk Michelle Hilda, Leung Ka Chun Thomas, Cheung Man Hong, Fu Henry, Chiu Kwong Yuen, Chan Ping Keung
Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
BMC Musculoskelet Disord. 2025 Mar 11;26(1):241. doi: 10.1186/s12891-025-08296-6.
Periprosthetic joint infection leads to significant morbidity and mortality after total knee arthroplasty. Preoperative and perioperative risk prediction and assessment tools are lacking in Asia. This study developed the first machine learning model for individualized prediction of periprosthetic joint infection following primary total knee arthroplasty in this demographic.
A retrospective analysis was conducted on 3,483 primary total knee arthroplasty (81 with periprosthetic joint infection) from 1998 to 2021 in a Chinese tertiary and quaternary referral academic center. We gathered 60 features, encompassing patient demographics, operation-related variables, laboratory findings, and comorbidities. Six of them were selected after univariate and multivariate analysis. Five machine learning models were trained with stratified 10-fold cross-validation and assessed by discrimination and calibration analysis to determine the optimal predictive model.
The balanced random forest model demonstrated the best predictive capability with average metrics of 0.963 for the area under the receiver operating characteristic curve, 0.920 for balanced accuracy, 0.938 for sensitivity, and 0.902 for specificity. The significant risk factors identified were long operative time (OR, 9.07; p = 0.018), male gender (OR, 3.11; p < 0.001), ASA > 2 (OR, 1.68; p = 0.028), history of anemia (OR, 2.17; p = 0.023), and history of septic arthritis (OR, 4.35; p = 0.030). Spinal anesthesia emerged as a protective factor (OR, 0.55; p = 0.022).
Our study presented the first machine learning model in Asia to predict periprosthetic joint infection following primary total knee arthroplasty. We enhanced the model's usability by providing global and local interpretations. This tool provides preoperative and perioperative risk assessment for periprosthetic joint infection and opens the potential for better individualized optimization before total knee arthroplasty.
人工关节周围感染会导致全膝关节置换术后出现严重的发病率和死亡率。亚洲缺乏术前和围手术期风险预测及评估工具。本研究针对该人群开发了首个用于个体化预测初次全膝关节置换术后人工关节周围感染的机器学习模型。
对中国一家三级和四级转诊学术中心1998年至2021年的3483例初次全膝关节置换术（81例发生人工关节周围感染）进行回顾性分析。我们收集了60个特征，包括患者人口统计学特征、手术相关变量、实验室检查结果和合并症。经过单因素和多因素分析后，从中选择了6个特征。使用分层10折交叉验证训练了5种机器学习模型，并通过区分度和校准分析进行评估，以确定最佳预测模型。
平衡随机森林模型显示出最佳预测能力，受试者操作特征曲线下面积的平均指标为0.963，平衡准确度为0.920，灵敏度为0.938，特异度为0.902。确定的显著危险因素为手术时间长（OR，9.07；p = 0.018）、男性（OR，3.11；p < 0.001）、美国麻醉医师协会身体状况分级>2（OR，1.68；p = 0.028）、贫血病史（OR，2.17；p = 0.023）和脓毒性关节炎病史（OR，4.35；p = 0.030）。脊髓麻醉是一个保护因素（OR，0.55；p = 0.022）。
我们的研究展示了亚洲首个用于预测初次全膝关节置换术后人工关节周围感染的机器学习模型。我们通过提供全局和局部解释增强了模型的可用性。该工具可为人工关节周围感染提供术前和围手术期风险评估，并为全膝关节置换术前更好的个体化优化开辟了潜力。
