Predicting total knee replacement in knee osteoarthritis using a machine learning-guided approach in patients of the Osteoarthritis Initiative (OAI).

ABSTRACT:

Objective

To develop a pragmatic model to predict total knee replacement (TKR) in knee osteoarthritis using non-imaging clinical, genetic and lifestyle data with machine learning (ML)-guided feature selection.

Methods

We analysed 3790 Osteoarthritis Initiative participants. Nested ML feature selection on the training set identified 15 informative variables. Classifiers were benchmarked, then a multivariable logistic regression was fit on the full cohort. Performance was summarised by discrimination (area under the curve (AUC) with 95% CI) and calibration (Brier score). To assess the incremental value of genetics, we refit an otherwise identical clinical model excluding the Polygenic Risk Score (PRS) and compared specificity at fixed sensitivities using Bonferroni-adjusted Mc

SUBMITTER: Blanco FJ 

PROVIDER: S-EPMC12911737 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature