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Development and internal validation of machine learning algorithms for predicting complications after primary total
Kyle N Kunze1,2, Aditya V Karhade3, Evan M Polce4
1Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA. kylekunze7@gmail.com.
Archives of Orthopaedic and Trauma Surgery
|May 4, 2022
Summary
Machine learning models can predict total hip arthroplasty complications. Key predictors include age, allergies, prior surgery, smoking, and opioid use, aiding risk stratification.
Area of Science:
- Orthopedic Surgery
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Complications following total hip arthroplasty (THA) lead to significant healthcare costs and patient morbidity.
- Identifying at-risk patients preoperatively can enable targeted interventions to optimize health and reduce complication rates.
Purpose of the Study:
- To develop and internally validate machine learning (ML) algorithms for predicting patient-specific, all-cause complications within two years of primary THA.
Main Methods:
- A retrospective study analyzed clinical registry data from 616 primary THA patients.
- Recursive feature elimination identified key preoperative predictors.
- Five ML algorithms were developed and validated using tenfold cross-validation, assessed by discrimination, calibration, and Brier score.
Main Results:
- The observed complication rate was 16.6%.
- A stochastic gradient boosting model demonstrated strong performance (AUC=0.88, Brier score=0.09).
- Predictive factors included age, drug allergies, prior hip surgery, smoking, and opioid use.
Conclusions:
- The developed ML algorithm effectively identifies patients at high risk for THA complications.
- This demonstrates the potential for ML-driven, office-based applications for real-time risk prediction.
- Further external validation on larger datasets is required to assess clinical utility for risk stratification.
