Exploratory Machine Learning Identification of Preoperative Laboratory Patterns Associated with Periprosthetic Joint Infection After Total Joint Arthroplasty

Background

Periprosthetic joint infection (PJI) remains one of the most severe complications following total hip and knee arthroplasty. Early identification of patients at increased risk remains challenging, particularly when routine laboratory biomarkers are within normal reference ranges. This study investigated whether routine preoperative laboratory biomarkers could reveal interpretable patterns associated with subsequent PJI using medically guided feature engineering and transparent machine-learning methods.