Liu Yuwen, Wu Yuhan, Zhang Tao, Chen Jie, Hu Wei, Sun Guixin, Zheng Pengfei
Department of Orthopaedic Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China.
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
Front Pediatr. 2024 Dec 11;12:1398713. doi: 10.3389/fped.2024.1398713. eCollection 2024.
Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.
A retrospective analysis was conducted on pediatric patients diagnosed with osteoarticular infections admitted to three hospitals in China between January 2012 and January 2023. All patients underwent blood and puncture fluid bacterial cultures. Sixteen early available variables were selected, and eight different ML algorithms were applied to construct the model by training on these data. The accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of these models. The Shapley Additive Explanation (SHAP) values were utilized to explain the predictive value of each variable on the output of the model.
The study comprised 181 patients in the BSI group and 420 in the non-BSI group. Random Forest exhibited the best performance, with an AUC of 0.947 ± 0.016. The model demonstrated an accuracy of 0.895 ± 0.023, a sensitivity of 0.847 ± 0.071, a specificity of 0.917 ± 0.007, a precision of 0.813 ± 0.023, and an F1 score of 0.828 ± 0.040. The four most significant variables in both the feature importance matrix plot of the Random Forest model and the SHAP summary plot were procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever days.
The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. Its application could aid in clinical decision-making and potentially mitigate the risk associated with delayed or inaccurate blood culture results.
血流感染（BSI）对患有骨关节炎感染的儿科患者构成重大的生命威胁风险。及时识别BSI对于有效管理和改善患者预后至关重要。本研究旨在开发一种机器学习（ML）模型，用于早期识别患有骨关节炎感染的儿童中的BSI。
对2012年1月至2023年1月期间在中国三家医院住院的诊断为骨关节炎感染的儿科患者进行回顾性分析。所有患者均接受了血液和穿刺液细菌培养。选择了16个早期可用变量，并应用8种不同的ML算法通过对这些数据进行训练来构建模型。使用准确性和受试者操作特征（ROC）曲线下面积（AUC）来评估这些模型的性能。使用Shapley加法解释（SHAP）值来解释每个变量对模型输出的预测价值。
该研究包括181例BSI组患者和420例非BSI组患者。随机森林表现出最佳性能，AUC为0.947±0.016。该模型的准确性为0.895±0.023，敏感性为0.847±0.071，特异性为0.917±0.007，精确度为0.813±0.023，F1评分为0.828±0.040。随机森林模型的特征重要性矩阵图和SHAP汇总图中四个最重要的变量是降钙素原（PCT）、中性粒细胞计数（N）、白细胞计数（WBC）和发热天数。
随机森林模型被证明在早期及时识别患有骨关节炎感染的儿童中的BSI方面是有效的。其应用有助于临床决策，并可能降低与血培养结果延迟或不准确相关的风险。
