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. Methods: We conducted a retrospective case–control study including 152 patients undergoing primary total hip or knee arthroplasty, comprising 75 PJI cases and 77 aseptic controls. PJI was defined according to International Consensus Meeting criteria, and aseptic controls had at least 12 months of infection-free follow-up. Thirty-eight routine preoperative laboratory biomarkers obtained one day before surgery were harmonized and analyzed. Original markers, medically derived ratios, clinically guided interaction and polynomial terms, and LASSO-based penalized selection were evaluated using 20 repetitions of nested stratified 5-fold cross-validation, with all data-dependent preprocessing and model selection confined to the corresponding training partitions. Results: Original laboratory markers showed modest discrimination (ROC-AUC 0.554; 95% CI, 0.461–0.646), while medically derived ratios performed similarly (ROC-AUC 0.556; 95% CI, 0.464–0.650). Clinically guided interaction features achieved the highest discrimination (ROC-AUC 0.643; 95% CI, 0.555–0.730), with a Brier score of 0.235 and a calibration slope of 0.349. Compared with the original-marker model, the interaction model improved ROC-AUC by 0.089 (95% CI, 0.017–0.159; p = 0.016), although this difference did not remain statistically significant after Holm adjustment (adjusted p = 0.066). Direct LASSO showed lower discrimination (ROC-AUC 0.582; 95% CI, 0.491–0.674). Conclusions: Routine preoperative laboratory biomarkers contained a modest PJI-associated signal, with clinically guided interactions yielding the highest discrimination among the evaluated laboratory representations. Overall performance remained modest, and penalized feature selection did not improve discrimination, supporting its interpretation primarily as a stability-assessment procedure. These findings should be considered exploratory and require external validation in larger, independent, preferably multicenter cohorts before clinical application.