LAUSR

BACKGROUND Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR. METHODS A comprehensive review of patients who underwent IBR from January 2018 to December 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets. RESULTS We identified 481 patients (694 reconstructions) with a mean (± SD) age of 50.0 ± 11.5 years, mean (± SD) body mass index of 26.7 ± 4.8 kg/m 2, and median follow-up time of 16.1 months (11.9-23.2 months). Periprosthetic infection developed with 16.3% (n = 113) of the reconstructions, and explantation was required with 11.8% (n = 82) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified 9 and 12 significant predictors of periprosthetic infection and explantation, respectively. CONCLUSIONS ML algorithms trained using readily available perioperative clinical data accurately predicts periprosthetic infection and explantation following IBR. Our findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.