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.

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

“…Prognosis (i) Estimated future knee OA incidence (i) Risk stratification (i) Random forest classifier [64,65] (a) 30 months [64] (ii) Selection of data from suitable time points to indicate short-term and long-term OA changes (ii) Logistic regression classifier [66][67][68][69] (b) 48 months [70] (iii) OA feature change detection (iii) Support vector machine classifier [66] (c) 8 years (iv) Discovery of pain-associated imaging features (iv) XGBoost model [49] (ii) Predicted medial JSN progression [66] (v) Multilayer perceptron [67,71] (iii) Predicted radiographic joint space loss progression [67] (vi) LASSO regression [39] (iv) Predicted knee OA onset and knee OA deterioration [71] (vii) Artificial neural network [70] (v) Discriminated between progressors and nonprogressors [72] (viii) Deep CNN [44,72,73] (vi) Predicted pain [73,74] (ix) DenseNet CNN [68] (vii) Predicted risk of progressive pain and structural change [65] (x) Gradient boosting machine [44,70] (viii) Predicted total knee replacement (TKR) incidence [68,75] (xi) Duo classifier [65] (xii) DeepSurv [75] (xiii) Dynamic functional mixedeffects model [54] femur and proximal tibia. Res-U-Net gave the best segmentation outcome with the highest mean intersection over union score at 0.989.…”

“…Multiclass classification was developed with the expansion of progressors' groups [39,65]. In addition, some studies focused on the prediction of total knee replacement (TKR) as future event [68,75]. None of the knee OA assessment methods alone could provide highly comprehensive information to make robust predictions or prognoses.…”

“…Seven machine learning methods, namely Cox, Deep-Surv, random forest, linear/kernel support vector machine, and linear/neural multitask logistic regression, were used by Jamshidi et al [75] to build a prediction model to prophesy risk and time to TKR for an OA-affected knee. At the beginning of the study, ten most important features, including X-rays, MRI feature bone marrow lesions (BMLs) in medial condyle, hyaluronic acid injection, performance measure, medical history, and knee symptoms, were identified by Lasso's Cox among a total of 1107 features.…”

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration

“…The results of this study showed that the most involved features included radiographs, bone marrow lesions of the medial condyle on MRI, hyaluronic acid injection, performance measure, medical history and knee-related symptoms. 28 A similar study also used a subset of the OAI cohort database to develop ML prediction models and to identify important risk factors which contribute to the prediction of knee OA. A robust feature selection was provided and SVM, K-Nearest Neighbor, eXtreme Gradient Boosting, LR, Decision Tree and Random Forest algorithms were applied on this set of chosen features (that include patient symptoms, medical history and medical imaging outcome like the presence of osteophytes and joint space narrowing).…”

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

“…An objective endpoint of total joint replacement (TJR) can be used to overcome some of the challenges of subjective pain outcomes and discordance with imaging in OA, although this is complicated by issues of preference, practice variability, and access to care. Jamshidi et al utilized baseline data from the publicly available Osteoarthritis Initiative (OAI) dataset to predict TJR at 96 months 28 . Utilizing a LASSO method to select features followed by multiple ML models, they could predict time to TJR with high accuracy (AUC 0.9).…”