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Survival analysis on subchondral bone length for total knee replacement

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Abstract

Objective

Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR).

Methods

We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan–Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk.

Results

The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML’s 0.759 and slightly below JSN’s 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML’s 0.073 and a minor difference from JSN’s 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML’s 0.802 and slightly lower than JSN’s 0.827.

Conclusion

SBL’s capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

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Data availability

This study used data available in the public domain (https://nda.nih.gov/oai/).

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Acknowledgements

We thank Dr. Jonathan Scalera, Boston University Chobanian & Avedisian School of Medicine, for his assistance with MRI annotations.

Funding

This work was supported in part by a grant from the Karen Toffler Charitable trust, grants from the American Heart Association (17SDG33670323 and 20SFRN35460031), and grants from the National Institutes of Health (R01-AR070139, RF1-AG062109, R01-HL159620, R21-CA253498, R43-DK134273, U01-AG018820 and P30-AR072571).

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Authors and Affiliations

  1. Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA

    Tejus Surendran, Lisa K. Park, Meagan V. Lauber, Ray S. Jhun, David T. Felson & Vijaya B. Kolachalama

  2. Sargent College, Boston University, Boston, MA, USA

    Baekdong Cha & Deepak Kumar

  3. Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Terence D. Capellini

  4. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Terence D. Capellini

  5. Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA

    Vijaya B. Kolachalama

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  1. Tejus Surendran
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Correspondence to Vijaya B. Kolachalama.

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Surendran, T., Park, L.K., Lauber, M.V. et al. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiol (2024). https://doi.org/10.1007/s00256-024-04627-1

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