Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative.

Abstract

OBJECTIVE:

By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee.

METHODS:

Features were from the Osteoarthritis Initiative (OAI) cohort at baseline. Using the lasso method for variable selection in the Cox regression model, we identified the 10 most important characteristics among 1,107 features. The prognostic power of the selected features was assessed by the Kaplan-Meier method and applied to 7 machine learning methods: Cox, DeepSurv, random forests algorithm, linear/kernel support vector machine (SVM), and linear/neural multi-task logistic regression models. As some of the 10 first-found features included similar radiographic measurements, we further looked at using the least number of features without compromising the accuracy of the model. Prediction performance was assessed by the concordance index, Brier score, and time-dependent area under the curve (AUC).

RESULTS:

Ten features were identified and included radiographs, bone marrow lesions of the medial condyle on magnetic resonance imaging, hyaluronic acid injection, performance measure, medical history, and knee-related symptoms. The methodologies Cox, DeepSurv, and linear SVM demonstrated the highest accuracy (concordance index scores of 0.85, Brier score of 0.02, and an AUC of 0.87). DeepSurv was chosen to build the prediction model to estimate the time to TKR for a given knee. Moreover, we were able to decrease the features to only 3 and maintain the high accuracy (concordance index of 0.85, Brier score of 0.02, and AUC of 0.86), which included bone marrow lesions, Kellgren/Lawrence grade, and knee-related symptoms, to predict risk and time of a TKR event.

CONCLUSION:

For the first time, we developed a model using the OAI cohort to predict with high accuracy if a given osteoarthritic knee would require TKR, when a TKR would be required, and who would likely progress fast toward this event.

• Authors: Afshin Jamshidi, Jean-Pierre Pelletier, Aurelie Labbe, François Abram, Johanne Martel-Pelletier, Arnaud Droit
• Journal: Arthritis care & research (Arthritis Care Res (Hoboken)) Vol. 73 Issue 10 Pg. 1518-1527 (10 2021) ISSN: 2151-4658 [Electronic] United States
• PMID: 33749148 (Publication Type: Journal Article, Multicenter Study, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
• Copyright: © 2021, American College of Rheumatology.

Topics

• Arthroplasty, Replacement, Knee (instrumentation)
• Decision Support Techniques
• Disease Progression
• Humans
• Knee Prosthesis
• Machine Learning
• Osteoarthritis, Knee (diagnosis, epidemiology, surgery)
• Predictive Value of Tests
• Risk Assessment
• Risk Factors
• Support Vector Machine
• Time Factors
• United States (epidemiology)