ArticleOsteoarthritis and cartilage2020
Risk scoring for time to end-stage knee osteoarthritis: data from the Osteoarthritis Initiative.
Article in Osteoarthritis and cartilage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).European radiology · 2022Pooled it
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH OA biomarkers consortium project.Arthritis research & therapy · 2024Article
- Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach.Journal of digital imaging · 2023Article
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5 authors.
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Abstract
objectiveThis study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years.
designThe Osteoarthritis Initiative (OAI) was a longitudinal study of the onset and progression of knee OA. Using a recent definition of end-stage knee OA, we implement interval-censored survival forests to select predictors of this endpoint. We fit an interval-censored Cox model for time to end-stage knee OA, using the selected predictors. The risk score is the Cox model's fitted linear combination of the nine selected baseline structural and symptomatic knee OA variables.
resultsWe fit our models on a training set of 2,701 patients, and we evaluate on an independent test set of 1,436 patients. On the test sample, we observe a concordance index of 0.86 between risk score and time to end-stage, AUC of 0.87 for predicting end-stage within 24, 36, and 48 months, and positive predictive values that increase with the risk score. This risk stratification algorithm could enrich clinical trial patient enrollment. By enrolling test sample patients with scores above a threshold, a trial could have included 91% of test set patients who reach end-stage within 4 years while only enrolling 45% of the test sample.
conclusionUsing statistical methods, we construct and validate an interpretable risk score for time to end-stage knee OA. This score can help disease-modifying OA treatment developers to select candidates with the highest risk of fast-progressing knee OA.
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