Predicting periprosthetic joint infection: external validation of preoperative prediction models

Introduction: Prediction models for periprosthetic joint infections (PJIs) are gaining interest due to their potential to improve clinical decision-making. However, their external validity across various settings remains uncertain. This study aimed to externally validate promising preoperative PJI prediction models in a recent multinational European cohort. Methods: Three preoperative PJI prediction models - by Tan et al. (2018), Del Toro et al. (2019), and Bülow et al. (2022) - that have previously demonstrated high levels of accuracy were selected for validation. A retrospective observational analysis of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) at centers in the Netherlands, Portugal, and Spain between January 2020 and December 2021 was conducted. Patient characteristics were compared between our cohort and those used to develop the models. Performance was assessed through discrimination and calibration. Results: The study included 2684 patients, 60 of whom developed a PJI (2.2 %). Our cohort differed from the models' original cohorts with respect to demographic variables, procedural variables, and comorbidity prevalence. The overall accuracies of the models, measured with the $$  statistic, were 0.72, 0.69, and 0.72 for the Tan, Del Toro, and Bülow models, respectively. Calibration was reasonable, but the PJI risk estimates were most accurate for predicted infection risks below 3 %-4 %. The Tan model overestimated PJI risk above 4 %, whereas the Del Toro model underestimated PJI risk above 3 %. Conclusions: The Tan, Del Toro, and Bülow PJI prediction models were externally validated in this multinational cohort, demonstrating potential for clinical application in identifying high-risk patients and enhancing preoperative counseling and prevention strategies.