Development and validation of a generalisable survival model to predict osteoarthritis progression.

ABSTRACT:

Objective

To develop and validate a transfer-learning survival model to predict progression end-stage knee osteoarthritis (esKOA) or receive knee replacement (KR).

Method

A DeepSurv model was trained on the Osteoarthritis Initiative (OAI) dataset with baseline clinical variables, including age, sex, BMI, comorbidities, smoking status, prior knee injury and surgery, pain medication use, use of walking aids, and activity level (9560 knees). A generalisable model was then developed by fine-tuning the OAI-derived model with data from the Multicenter Osteoarthritis Study (MOST) Centre 1 (3002 knees). This model was validated on an independent dataset from MOST Centre 2 (2972 knees). Model performance was evaluated using the concordance index from 1000 bootstrap resamples. SHapley

SUBMITTER: Li HHT 

PROVIDER: S-EPMC12517096 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature