-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.

Review of bone deficiency in total elbow arthroplasty revision.

Total elbow arthroplasty revision rates have been increasing over time due to the increasing use of the procedure with the accompanying increase in complications. The most common complications that typically require revision surgery include aseptic loosening, periprosthetic fractures, infection, and component failure. The associated instability has an overall revision rate reported to be as high as 13%. One important factor when performing a revision surgery is bone quality and bone loss; this represents a challenge during the clinical decision-making process. Currently, there are several strategies used to address bone loss such as arthrodesis, resection arthroplasty, impaction grafting, allograft-prosthetic composite reconstruction, and custom prostheses. The aim of this review article is to provide a comprehensive review of the current strategies to improve diagnosis of failed total elbow arthroplasty and improve management and outcomes of this patient population.

Awake Percutaneous Fixation for Unstable Fractures of the Spine in High-risk Patients: A Retrospective Study.

INTRODUCTION: Unstable fractures in sick or elderly patients are on the rise. These patients who are at high risk for surgery present a challenge for surgeons and anesthesiologists. In patients with American Society of Anesthesiologists (ASA) scores 3 to 4, the risk is even higher because of the high rate of intraoperative complications. METHODS: All patients with ASA scores 3 to 4 who presented with unstable fractures of the spine to a level-one trauma center were assessed, and they underwent awake spinal percutaneous fixation, with mild sedation and local anesthesia. Demographics, radiology, and the outcome were collected. RESULTS: Nineteen patients were operated between the years 2019 and 2021. Average follow-up was 12 months (range 8 to 24 months); six patients were female and 13 males. The average age was 77.7 years; the ASA score was 3 to 4 in all patients. There were 10 extension-type injuries, six unstable burst injuries, two chance fractures, and one teardrop fracture. All patients underwent unilateral fixation, and just one patient underwent bilateral fixation; cement augmentation was done in 16 of the patients. No neurologic complication was observed. One case of infection presented 4 months after surgery. All patients were discharged ambulating. CONCLUSIONS: Awake fixation in extreme cases is safe and feasible; a dedicated team including an anesthesiologist and radiologist is needed to treat these cases safely and quickly.