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

• Published in: International orthopaedics Vol. 48; no. 7; pp. 1887 - 1896
• Main Authors: Gutierrez-Naranjo, Jose M., Moreira, Alvaro, Valero-Moreno, Eduardo, Bullock, Travis S., Ogden, Liliana A., Zelle, Boris A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024
• Subjects: Adult; Aged; Algorithms; Female; Fractures, Bone - surgery; Humans; Lower Extremity - injuries; Lower Extremity - surgery; Machine Learning; Male; Medicine; Medicine & Public Health; Middle Aged; Original Paper; Orthopedics; Retrospective Studies; Risk Assessment - methods; Risk Factors; Surgical Wound Infection - diagnosis; Surgical Wound Infection - epidemiology; Surgical Wound Infection - etiology; Surgical Wound Infection - prevention & control; Young Adult; Risk score; Postoperative infection; Lower extremity; Machine learning
• DOI: 10.1007/s00264-024-06194-5
• ISSN: 0341-2695; 1432-5195

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.