A machine learning-based model for “In-time” prediction of periprosthetic joint infection

作者：Weishen Chen, Xuantao Hu, Chen Gu, Zhaohui Zhang, Linli Zheng, Baiqi Pan, Xiaoyu Wu, Wei Sun, Puyi Sheng · 发表于：Digital Health · 年份：2024 · DOI：10.1177/20552076241253531 · 被引用次数：12 · 研究领域：Orthopedic Infections and Treatments、Orthopaedic implants and arthroplasty、Musculoskeletal synovial abnormalities and treatments

Background Previous criteria had limited value in early diagnosis of periprosthetic joint infection (PJI). Here, we constructed a novel machine learning (ML)-derived, “in-time” diagnostic system for PJI and proved its validity. Methods We filtered “in-time” diagnostic indicators reported in the literature based on our continuous retrospective cohort of PJI and aseptic prosthetic loosening patients. With the indicators, we developed a two-level ML model with six base learners including Elastic Net, Linear Support Vector Machine, Kernel Support Vector Machine, Extra Trees, Light Gradient Boosting Machine and Multilayer Perceptron), and one meta-learner, Ensemble Learning of Weighted Voting. The prediction performance of this model was compared with those of previous diagnostic criteria (International Consensus Meeting in 2018 (ICM 2018), etc.). Another prospective cohort was used for internal validation. Based on our ML model, a user-friendly web tool was developed for swift PJI diagnosis in clinical practice. Results A total of 254 patients (199 for development and 55 for validation cohort) were included in this study with 38.2% of them diagnosed as PJI. We included 21 widely accessible features including imaging indicators (X-ray and CT) in the model. The sensitivity and accuracy of our ML model were significantly higher than ICM 2018 in development cohort (90.6% vs. 76.1%, P = 0.032; 94.5% vs. 86.7%, P = 0.020), which was supported by internal validation cohort (84.2% vs. 78...