Machine Learning Strategies for Preoperative PJI Diagnosis: Integrative Analysis of Serum and Synovial Fluid Markers

Abstract

Background: Preoperative diagnosis of periprosthetic joint infection (PJI) is crucial for guiding treatment strategies and improving patient outcomes. This study aims to develop a new diagnostic model for the preoperative diagnosis of PJI based on serum and synovial fluid markers and further validate its effectiveness.

Methods: We retrospectively collected data from patients admitted for joint revision surgery between January 2018 and October 2022, selecting serum and synovial fluid markers as variables for the study. The most suitable diagnostic markers were selected using LASSO regression, and eight machine learning (ML) models were constructed based on the selected markers. The diagnostic performance and clinical utility of the ML models were assessed using receiver operating characteristic curves, calibration curves, decision curve analysis, and clinical impact analysis. Finally, the best model was compared to existing diagnostic standards using an external validation cohort.

Results: A total of 376 cases were analyzed (263 in the training cohort and 113 in the validation cohort), with 111 cases (29.52%) diagnosed as PJI. The ML models included SE-IL6, SE-CRP, ESR, SF-IL6, PMN%, DD, and ALB. The eXtreme Gradient Boosting model was the optimal model, achieving an area under the curve of 0.998 (95% CI 0.993-1) in the test set, outperforming other models. It also recorded the lowest Brier score of 0.062 and the highest F1 score of 0.985. In the external validation cohort, the accuracy, sensitivity, and specificity of the ML diagnostic model were higher than those of the MSIS 2013 and ICM 2018 diagnostic criteria.

Conclusion: Our newly developed ML diagnostic model can assist clinicians in rapidly and accurately diagnosing PJI before surgery and has potential value for timing decisions regarding two-stage revisions. It has high economic value and clinical applicability.

Keywords: diagnostic model; inflammatory markers; machine learning; periprosthetic joint infection; preoperative diagnosis; synovial fluid.

Figure 1

Flowchart of the study.

Figure 2

Demographics and variable characteristics selection using LASSO regression. (A) Binomial bias versus log (Lambda) in the LASSO regression model. The dashed line on the left indicates Lambda.min (the minimum value of Lambda) and the dashed line on the right indicates Lambda.1st (the maximum value within one standard error of Lambda.min). The numbers at the top of the figure indicate the number of variables included in the model at each Lambda value. (B) LASSO coefficient trajectories for selected variables under log (Lambda). Unimportant coefficients shrink to zero as Lambda increases. Y-axis denotes the coefficient of the variable and x-axis denotes log (Lambda).

Figure 3

Evaluating the diagnostic performance of different models. (A and B) ROC curves of the training and test sets for evaluating the ability of the eight machine learning models to discriminate between PJI and aseptic failures. The AUC values and their 95% confidence intervals are displayed in the figure. (C) Calibration curves showing how well the predicted probabilities of the eight models match the actual observed events. The Brier scores obtained by each model are shown on the right side of the figure. XGB obtained better Brier scores 0.062. (D) Decision Curve Analysis evaluates the clinical net benefit of each model at different risk thresholds.

Figure 4

SHAP interpretation of XGB machine learning model. (A) A scatter plot based on SHAP values shows the contribution of each variable to PJI diagnosis. The X-axis represents SHAP values, the Y-axis represents variable names, and the color indicates the contribution range of the variables (from low to high). (B) The average SHAP values of each variable reflect their relative importance in the PJI diagnosis model. (C and D) Individual SHAP force plots for a PJI patient (C) and an aseptic failure patient (D). The SHAP values represent the diagnostic features of individual patients and the contribution of each variable to diagnosing PJI. f(x) represents the predicted probability of being diagnosed with PJI, with values closer to 1 indicating a higher likelihood of PJI diagnosis; a negative f(x) indicates that the patient’s features are pushing the model’s diagnosis toward aseptic failure. E[f(x)] is the baseline value of the model when no sample input is provided. The length of the arrows indicates the degree to which the diagnosis is influenced by each variable—the longer the arrow, the greater the impact.