Integrative Metabolomics and Pharmacology Reveal Guzhi Zengsheng Zhitongwan's Mechanism in Knee Osteoarthritis.

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Abstract

[Indexed for MEDLINE] Conflict of interest statement: Declaration of competing interest We declare that all authors have no conflicts of interest that could affect the objectivity of the research. We have fully disclosed any potential conflicts of interest and ensure that these conflicts do not influence the design, execution, or reporting of the study. 13. J Orthop Translat. 2025 Jul 17;54:51-64. doi: 10.1016/j.jot.2025.06.016. eCollection 2025 Sep. Predicting periprosthetic joint Infection: Evaluating supervised machine learning models for clinical application. Dragosloveanu S(1)(2), Vulpe DE(1)(2), Andrei CA(2), Nedelea DG(2), Garofil ND(1)(3), Anghel C(4), Dragosloveanu CDM(1)(5), Cergan R(1)(2), Scheau C(1)(2). Author information: (1)The "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania. (2)"Foisor" Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, Bucharest, Romania. (3)"Dr. Carol Davila" Clinical Hospital of Nephrology, Bucharest, Romania. (4)Department of Computer Science and Information Technology, "Dunărea de Jos" University of Galati, Galati, Romania. (5)Clinical Hospital for Ophthalmological Emergencies, Bucharest, Romania. BACKGROUND AND OBJECTIVES: Periprosthetic joint infection (PJI) is a serious complication that can occur after joint arthroplasty, such as hip or knee replacement surgeries. It involves the invasion of the periprosthetic space by pathogens, leading to severe inflammation and often requiring complex medical intervention. PJI is associated with significant morbidity, increased healthcare costs, and a reduced quality of life for patients. This study aims to evaluate the performance of multiple supervised machine learning models in predicting PJI using clinical and demographic data collected from patients who underwent joint arthroplasty. METHODS: Eight supervised machine learning models-Logistic Regression, Random Forest, XGBoost, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), AdaBoost, Gaussian Naive Bayes (GNB), and Stochastic Gradient Descent (SGD)-were trained and tested on a dataset of 27,854 patients. Models were evaluated using accuracy, precision, recall, specificity, F1 score, and area under the ROC curve (AUC). RESULTS: Random Forest and XGBoost showed the best overall performance, with high accuracy and balanced metrics across all evaluation criteria. KNN also performed strongly, particularly in minimizing misclassifications. GNB and SGD yielded weaker results, with higher error rates. CONCLUSION: Random Forest, XGBoost, and KNN are the most promising models for clinical implementation in PJI prediction. Their robust performance may support earlier diagnosis and improved patient outcomes in orthopedic care. TRANSLATIONAL POTENTIAL STATEMENT: This study demonstrates that machine learning models-particularly Random Forest and XGBoost-can accurately predict periprosthetic joint infection (PJI) using structured electronic health record data. By integrating these models into preoperative assessment workflows, clinicians may be able to identify high-risk patients earlier, personalize prophylactic strategies, and reduce infection-related morbidity. The implementation of these predictive tools has the potential to enhance clinical decision-making, improve surgical outcomes, and optimize the use of healthcare resources in orthopedic practice. © 2025 The Authors. DOI: 10.1016/j.jot.2025.06.016 PMCID: PMC12284488