Abstract
PurposePrompt 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.MethodsPatients (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.ResultsSynovial 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).ConclusionThe diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model.Level of evidenceDiagnostic study Level III (Case–control study).
Highlights
The diagnosis of septic arthritis can be challenging [5]
Many studies have revealed that the predictive value of a single examination finding for septic arthritis may be weak [1, 2, 5, 14, 15]
Of the 326 patients, 164 (50.3%) were defined as having septic arthritis according to modified criteria described by Newman
Summary
The diagnosis of septic arthritis can be challenging [5]. No single serological marker has demonstrated sufficient sensitivity, specificity, or predictive value to distinguish septic arthritis from other types of arthritis [2, 7, 22]. Many studies have revealed that the predictive value of a single examination finding for septic arthritis may be weak [1, 2, 5, 14, 15]. The diagnosis of septic arthritis should be determined by the integration of a thorough history, physical examination, and the results of laboratory investigations [5, 16]. Machine learning has been applied to construct prediction models of complicated problems in which many factors are involved, while their respective relevance remains unclear. Machine-learning algorithms have been applied in the field of knee surgery to predict events, such as postoperative acute kidney injury after total knee arthroplasty and the risk of hospital admission following anterior cruciate ligament reconstruction [10, 13]
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