| Autor: |
F. Saxer, D. Demanse, A. Brett, D. Laurent, L. Mindeholm, P.G. Conaghan, M. Schieker |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Osteoarthritis and Cartilage Open, Vol 6, Iss 2, Pp 100458- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2665-9131 |
| DOI: |
10.1016/j.ocarto.2024.100458 |
| Popis: |
Objective: Developing new therapies for knee osteoarthritis (KOA) requires improved prediction of disease progression. This study evaluated the prognostic value of clinical clusters and machine-learning derived quantitative 3D bone shape B-score for predicting total and partial knee replacement (KR). Design: This retrospective study used longitudinal data from the Osteoarthritis Initiative. A previous study used patients' clinical profiles to delineate phenotypic clusters. For these clusters, the distribution of B-scores was assessed (employing Tukey's method). The value of both cluster allocation and B-score for KR-prediction was then evaluated using multivariable Cox regression models and Kaplan-Meier curves for time-to-event analyses. The impact of using B-score vs. cluster was evaluated using a likelihood ratio test for the multivariable Cox model; global performances were assessed by concordance statistics (Harrell's C-index) and time dependent receiver operating characteristic (ROC) curves. Results: B-score differed significantly for the individual clinical clusters (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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