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Predicting periprosthetic joint Infection: Evaluating supervised machine learning models for clinical application
Serban Dragosloveanu1,2, Diana Elena Vulpe1,2, Constantin Adrian Andrei2
1The "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
Background And Objectives:
Periprosthetic joint infection (PJI) is a serious complication that can occur after joint arthroplasty, such as hip or knee replacement surgeries. It involves the invasion of the periprosthetic space by pathogens, leading to severe inflammation and often requiring complex medical intervention. PJI is associated with significant morbidity, increased healthcare costs, and a reduced quality of life for patients. This study aims to evaluate the performance of multiple supervised machine learning models in predicting PJI using clinical and demographic data collected from patients who underwent joint arthroplasty.
Methods:
Eight supervised machine learning models-Logistic Regression, Random Forest, XGBoost, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), AdaBoost, Gaussian Naive Bayes (GNB), and Stochastic Gradient Descent (SGD)-were trained and tested on a dataset of 27,854 patients. Models were evaluated using accuracy, precision, recall, specificity, F1 score, and area under the ROC curve (AUC).
Results:
Random Forest and XGBoost showed the best overall performance, with high accuracy and balanced metrics across all evaluation criteria. KNN also performed strongly, particularly in minimizing misclassifications. GNB and SGD yielded weaker results, with higher error rates.
Conclusion:
Random Forest, XGBoost, and KNN are the most promising models for clinical implementation in PJI prediction. Their robust performance may support earlier diagnosis and improved patient outcomes in orthopedic care.
Translational Potential Statement:
This study demonstrates that machine learning models-particularly Random Forest and XGBoost-can accurately predict periprosthetic joint infection (PJI) using structured electronic health record data. By integrating these models into preoperative assessment workflows, clinicians may be able to identify high-risk patients earlier, personalize prophylactic strategies, and reduce infection-related morbidity. The implementation of these predictive tools has the potential to enhance clinical decision-making, improve surgical outcomes, and optimize the use of healthcare resources in orthopedic practice.
Insights
Machine learning models, particularly Random Forest and XGBoost, can accurately predict periprosthetic joint infection (PJI) after joint arthroplasty. These tools may help identify high-risk patients for improved orthopedic care and outcomes.
Area of Science:
- Orthopedic surgery
- Medical informatics
- Machine learning in healthcare
Background:
- Periprosthetic joint infection (PJI) is a severe complication following joint arthroplasty.
- PJI leads to significant patient morbidity, increased costs, and reduced quality of life.
- Accurate prediction of PJI is crucial for effective patient management.
Purpose of the Study:
- To evaluate the predictive performance of various supervised machine learning models for PJI.
- To identify the most effective models for clinical application in predicting PJI using patient data.
Main Methods:
- Trained and tested eight supervised machine learning models (Logistic Regression, Random Forest, XGBoost, ANN, KNN, AdaBoost, GNB, SGD).
- Utilized a dataset of 27,854 patients who underwent joint arthroplasty.
- Evaluated models using accuracy, precision, recall, specificity, F1 score, and AUC.
Main Results:
- Random Forest and XGBoost demonstrated superior performance with high accuracy and balanced metrics.
- k-Nearest Neighbors (KNN) also showed strong results, particularly in minimizing misclassifications.
- Gaussian Naive Bayes (GNB) and Stochastic Gradient Descent (SGD) exhibited weaker performance with higher error rates.
Conclusions:
- Random Forest, XGBoost, and KNN are promising for clinical implementation in PJI prediction.
- These models can potentially support earlier diagnosis and improve patient outcomes in orthopedic surgery.
- Machine learning models offer a valuable tool for enhancing clinical decision-making and reducing infection-related morbidity.
