一种可以提高膝关节感染性关节炎诊断准确性的机器学习算法。
Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee.
机构信息
Department of Orthopaedic Surgery, Chungnam National University School of Medicine, Chungnam National University Hospital, 266 Munhwa-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
Department of Orthopaedic Surgery, Gachon University College of Medicine, Gil Medical Centre, Incheon, Republic of Korea.
出版信息
Knee Surg Sports Traumatol Arthrosc. 2021 Oct;29(10):3142-3148. doi: 10.1007/s00167-020-06418-2. Epub 2021 Jan 15.
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).
目的
及时诊断和治疗膝关节化脓性关节炎至关重要。然而，诊断化脓性关节炎的证据质量较低。本研究旨在使用急诊科的临床数据，建立一种基于机器学习的膝关节原发性化脓性关节炎诊断算法，并验证其诊断准确性。
方法
共纳入 326 例在急诊科因疑似膝关节化脓性关节炎而行关节液分析的患者。根据改良 Newman 标准，其中 164 例（50.3%）患者诊断为化脓性关节炎。比较了化脓性关节炎和炎症性关节炎的临床特征。应用受试者工作特征（ROC）曲线下面积（AUC）评估每个变量对诊断化脓性关节炎的效能。数据集分为独立的训练集和测试集（分别包含数据的 80%和 20%）。应用监督机器学习技术（随机森林和极端梯度提升：XGBoost），使用训练数据集开发诊断模型。随后使用测试数据集验证所开发的模型。比较了机器学习模型和每个变量的 ROC 曲线。
结果
在多变量分析中，化脓性关节炎的关节液白细胞计数（WBC）明显高于炎症性关节炎（P=0.001）。在 ROC 比较分析中，关节液 WBC 计数的 AUC 明显高于所有其他单一变量（P=0.002）。使用 XGBoost 算法的诊断模型的 AUC （0.831，95%置信区间 0.751-0.923）高于关节液 WBC 计数（0.740，95%置信区间 0.684-0.791；P=0.033）。所开发的算法已作为免费访问的网络应用程序（www.septicknee.com）部署。
结论
使用基于机器学习的预测模型可能会提高膝关节化脓性关节炎的诊断。
证据水平
诊断研究 III 级（病例对照研究）。
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