A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment

Background: Risk stratification of individual patients who are prone to infection would allow surgeons to monitor high-risk patients more closely and intervene early when needed. This could reduce infection-related consequences such as increased health-care costs. The purpose of this study was to develop a machine learning (ML)-derived risk-stratification tool using the SPRINT (Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures) and FLOW (Fluid Lavage of Open Wounds) trial databases to estimate the probability of infection in patients with operatively treated tibial shaft fractures (TSFs). Methods: Patients with unilateral TSFs from the SPRINT and FLOW trials were randomly split into derivation (80%) and validation (20%) cohorts. Random forest algorithms were used to select features that are relevant to predicting infection. These features were included for algorithm training. Five ML algorithms were trained in recognizing patterns associated with infection. The performance of each ML algorithm was evaluated and compared based on (1) the area under the ROC (receiver ope