Predicting total knee replacement at 2 and 5 years in osteoarthritis patients using machine learning

Abstract

ObjectivesKnee osteoarthritis is a major cause of physical disability and reduced quality of life, with end-stage disease often treated by total knee replacement (TKR). We set out to develop and externally validate a machine learning model capable of predicting the need for a TKR in 2 and 5 years time using routinely collected health data.DesignA prospective study using datasets Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST). OAI data were used to train the models while MOST data formed the external test set. The data were preprocessed using feature selection to curate 45 candidate features including demographics, medical history, imaging assessments, history of intervention and outcome.SettingThe study was conducted using two multicentre USA-based datasets of participants with or at high risk of knee OA.ParticipantsThe study excluded participants with at least one existing TKR. OAI dataset included participants aged 45–79 years of which 3234 were used for training and 809 for internal testing, while MOST involved participants aged 50–79 and 2248 were used for external testing.Main outcome measuresThe primary outcome of this study was prediction of TKR onset at 2 and 5 years. Performance was evaluated using area under the curve (AUC) and F1-score and key predictors identified.ResultsFor the best performing model (gradient boosting machine), the AUC at 2 years was 0.913 (95% CI 0.876 to 0.951), and at 5 years 0.873 (95% CI 0.839 to 0.907). Radiographic-derived features, questionnaire-based assessments alongside the patient’s educational attainment were key predictors for these models.ConclusionsOur approach suggests that routinely collected patient data are sufficient to drive a predictive model with a clinically acceptable level of accuracy (AUC>0.7) and is the first such tool to be externally validated. This level of accuracy is higher than previously published models utilising MRI data, which is not routinely collected.