Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

• Published in: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA Vol. 29; no. 10; pp. 3142 - 3148
• Main Authors: Choi, Eun-Seok, Sim, Jae Ang, Na, Young Gon, Seon, Jong- Keun, Shin, Hyun Dae
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Arthritis; Arthritis, Infectious - diagnosis; Case-Control Studies; Confidence intervals; Datasets; Diagnosis; Emergency medical care; Emergency medical services; Humans; Inflammation; Knee; Learning algorithms; Leukocytes; Machine Learning; Medical diagnosis; Medicine; Medicine & Public Health; Multivariate analysis; Orthopedics; Patients; Prediction models; Retrospective Studies; ROC Curve; Sports Medicine; Statistical analysis; Synovial Fluid; Training; Knee; Diagnosis; Infectious arthritis; Machine learning
• ISSN: 0942-2056; 1433-7347; 1433-7347
• DOI: 10.1007/s00167-020-06418-2

Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients ( n  = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis ( P  = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables ( P  = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P  = 0.033). The developed algorithm was deployed as a free access web-based application ( www.septicknee.com ). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).