-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

PURPOSE: This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures. METHODS: A machine learning analysis was conducted on a dataset comprising 1,579 patients who underwent surgical fixation for lower extremity fractures to create a predictive model for risk stratification of postoperative surgical site infection. We evaluated different clinical and demographic variables to train four machine learning models (neural networks, boosted generalised linear model, naïve bayes, and penalised discriminant analysis). Performance was measured by the area under the curve score, Youdon's index and Brier score. A multivariate adaptive regression splines (MARS) was used to optimise predictor selection. RESULTS: The final model consisted of five predictors. (1) Operating room time, (2) ankle region, (3) open injury, (4) body mass index, and (5) age. The best-performing machine learning algorithm demonstrated a promising predictive performance, with an area under the ROC curve, Youdon's index, and Brier score of 77.8%, 62.5%, and 5.1%-5.6%, respectively. CONCLUSION: The proposed predictive model not only assists surgeons in determining high-risk factors for surgical site infections but also empowers patients to closely monitor these factors and take proactive measures to prevent complications. Furthermore, by considering the identified predictors, this model can serve as a reference for implementing preventive measures and reducing postoperative complications, ultimately enhancing patient outcomes. However, further investigations involving larger datasets and external validations are required to confirm the reliability and applicability of our model.

Diffuse Pigmented Villonodular Synovitis as a Rare Cause of Graft Failure Following Anterior Cruciate Ligament Reconstruction.

This case report describes a 42-year-old woman who was diagnosed with pigmented villonodular synovitis (PVNS) in the knee. The patient had received a bone-patella tendon-bone autograft reconstruction of her anterior cruciate ligament (ACL) 22 years prior to her diagnosis of PVNS. After a traumatic event that tore her ACL graft, she underwent a second surgery to repair the initial reconstruction. However, her pain and joint instability remained unresolved. When radiolucent lesions in her tibia and femur were identified through a radiographic image, the patient was referred to the authors' orthopedic oncology clinic. Additional imaging, including magnetic resonance imaging, revealed PVNS, and she was scheduled for debridement and a complete synovectomy of the knee. After surgery, the patient's pain decreased dramatically. She continues to maintain an active lifestyle despite a relatively minor decrease in range of motion. In this case, PVNS proved to be an unlikely complication after ACL reconstruction. The patient remains at risk for the development of degenerative arthritis. [Orthopedics. 2018; 41(1):e142-e144.].