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Updated: Jul 14, 2026
In Vivo Mouse Model of Spinal Implant Infection
Published on: June 23, 2020
Identification of Staphylococcus aureus spondylitis in patients with spinal infections using machine learning based
Junbao Chen1,2, Kaile Feng1,2, Qianfei Liu1,2
1Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.
Abstract:
Rapid etiological identification of Staphylococcus aureus in spinal infections can be challenging, often delaying targeted therapy. We developed a machine learning model leveraging XGBoost to predict S. aureus etiology in spinal infections directly from routine laboratory indicators. The XGBoost model demonstrated superior predictive performance (AUC 0.812; 95% CI: 0.728-0.896) among four algorithms, with SHAP analysis identifying D-dimer, Monocyte Percentage, Albumin, and Alanine Aminotransferase as crucial predictors.
