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Machine Learning Better Predicts Mortality in Patients Undergoing Revision Total Hip Arthroplasty for Periprosthetic
Nora Galoustian1, Jeffrey Balian2, Christopher David Hamad1
1Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Summary
Machine learning and SMOTE identified fluid/electrolyte disorders, age, and cardiac arrhythmia as key mortality risks after revision hip surgery for infection. Optimizing electrolytes may reduce patient deaths.
Area of Science:
- Orthopedic Surgery
- Biostatistics
- Machine Learning
Background:
- Periprosthetic joint infection (PJI) after total hip arthroplasty (THA) carries a significant mortality risk.
- Machine learning (ML) techniques like Synthetic Minority Oversampling Technique (SMOTE) have potential for outcome prediction but are underutilized in orthopedics.
Purpose of the Study:
- To be the first study to apply ML and SMOTE to predict mortality risk factors in revision THA for PJI.
- To develop a novel risk score for identifying high-risk patients.
Main Methods:
- Retrospective analysis of the Nationwide Readmissions Database for patients undergoing revision THA for PJI.
- Utilized logistic regression, gradient boosting, and random forest models, assessing performance with AUROC, Brier score, and F1 score.
- Applied SHapley Additive exPlanation (SHAP) for predictor identification and SMOTE for model improvement.
Main Results:
- Gradient boosting achieved the highest performance (AUROC 0.862, F1 0.257) among initial models.
- SMOTE application enhanced model calibration, AUROC, and F1 scores across all models.
- SHAP analysis identified fluid and electrolyte disorders, advancing age, and cardiac arrhythmia as the strongest predictors of mortality.
Conclusions:
- Fluid and electrolyte disorders are the primary predictors of mortality in revision THA for PJI.
- The study introduces a novel, streamlined risk score using ML and SMOTE for high-risk patient identification.
- Perioperative optimization of electrolyte levels may be a crucial intervention to reduce mortality.
