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Utility of Machine Learning in Predicting Catastrophic Cardiac Complications Following Hip and Knee Periprosthetic
William T Sampson1, Isaiah A Freeman1, Michelle R Shimizu1
1Department of Orthopaedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
The Journal of Arthroplasty
|April 12, 2026
Summary
Machine learning models accurately predict rare cardiac complications like myocardial infarction and cardiac arrest after periprosthetic fracture surgery. This aids in identifying high-risk patients and developing interventions to improve outcomes.
Area of Science:
- Orthopedic Surgery
- Cardiology
- Data Science
Background:
- Rising prevalence of total hip (THA) and knee arthroplasties (TKA) leads to increased periprosthetic fractures (PPF).
- Myocardial infarction (MI) and cardiac arrest (CA) are severe complications following PPF surgery.
- Limited research exists on using machine learning (ML) for predicting rare outcomes in revision arthroplasty.
Purpose of the Study:
- Develop and evaluate ML models to predict cardiac complications (MI and CA) after PPF surgery.
- Assess the accuracy and utility of different ML algorithms for rare event prediction in orthopedic surgery.
- Identify key predictors of cardiac complications in patients undergoing PPF surgery.
Main Methods:
- Extracted data from a national quality improvement database for patients with PPF undergoing revision arthroplasty or open reduction internal fixation (ORIF).
- Developed four ML models: artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor.
- Assessed model performance using Area Under the Curve (AUC), calibration slopes, intercepts, and Brier scores.
Main Results:
- Included 3,805 PPF patients (1,869 revision, 1,936 ORIF).
- Reported low incidence of MI (1.81-1.97%) and CA (0.67-0.92%) post-surgery.
- All ML models demonstrated excellent predictive power (AUCs 0.76-0.94) and accuracy (Brier scores 0.013-0.044).
- Key predictors included elevated serum creatinine, white blood cell count, sodium, and lower body mass index.
Conclusions:
- ML models show high accuracy in predicting rare postoperative cardiac complications after PPF surgery.
- Demonstrates the effectiveness of ML in identifying high-risk patients for adverse cardiac events.
- Suggests potential for targeted interventions to mitigate cardiac risks in PPF patients.