Liao Jiali,Li Gefang,He Bo,et al.Construction of a machine learning-based prediction model for Staphylococcus aureus infection in children with septic arthritis[J].Journal of Clinical Pediatric Surgery,,():477-483.[doi:10.3760/cma.j.cn101785-202507035]
Construction of a machine learning-based prediction model for Staphylococcus aureus infection in children with septic arthritis
- Keywords:
- Staphylococcus Aureus; Arthritis; Infectious; Machine Learning; Surgical Procedures; Operative; Child
- Abstract:
- Objective Toconstruct predictive models for Staphylococcus aureus (S.aureus) infection using 4 machine learning algorithms based on clinical characteristics and laboratory indicators at admission in children with septic arthritis,aiming to determine the infection status of S.aureus before the availability of etiological test results.Methods A retrospective analysis was conducted on the clinical data of 463 children with septic arthritis admitted to the Children’s Hospital of Chongqing Medical University between January 2014 and December 2024.Among them,380 cases from January 2014 to December 2023 were used for model construction (internal modeling cohort),and 83 cases from January 2024 to December 2024 were used for external validation.In the modeling cohort,the least absolute shrinkage and selection operator (LASSO) regression algorithm was used for variable selection.A total of 25 variables were included,covering demographic characteristics,admission symptoms,pre-admission management,and laboratory test indicators.Five-fold cross-validation was used to compare the performance of four machine learning algorithms-extreme gradient boosting (XGBoost),random forest (RF),gradient boosting decision tree (GBDT),and decision tree (DT).In the external validation dataset,model performance was evaluated using the area under the receiver operating characteristic curve (AUC),specificity,accuracy,precision,recall,F1 score,decision curve analysis,and calibration curves.Results The predictive models constructed using the RF and XGBoost algorithms showed good performance in both five-fold cross-validation and external validation,with an AUC of 0.83,specificity of 0.82,accuracy of 0.76,and sensitivity of 0.65.The most important predictive feature was the neutrophil-to-lymphocyte count ratio.Conclusion Among the prediction models constructed using four machine learning algorithms,the RF and XGBoost models based on clinical manifestations and laboratory indicators showed superior predictive performance and good predictive capability.These models may provide supportive decision-making for the early diagnosis of S.aureus infection in children with septic arthritis before the availability of pathogen culture results.
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Memo
收稿日期:2025-7-16。
基金项目:重庆市科卫联合疾控科研项目(2026KWJK1008)
通讯作者:李各芳,Email:642772059@qq.com