Predictive validity of consensus-based MRI definition of osteoarthritis plus radiographic osteoarthritis for the progression of knee osteoarthritis: A longitudinal cohort study.
Authors
Affiliations (5)
Affiliations (5)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
- Department of Biostatistics, School of Public Health, New York University, New York, NY, USA.
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Abstract
Our previous study showed that magnetic resonance imaging (MRI)-defined tibiofemoral osteoarthritis (MRI-OA), based on a Delphi approach, in combination with radiographic OA (ROA) had a strong predictive validity for the progression of knee OA. This study aimed to compare whether the combination using traditional prediction models was superior to the Light Gradient Boosting Machine (LightGBM) models. Data were from the Tasmanian Older Adult Cohort. A radiograph and 1.5T MRI of the right knee was performed. Tibial cartilage volume was measured at baseline, 2.6 and 10.7 years. Knee pain and function were assessed at baseline, 2.6, 5.1, and 10.7 years. Right-sided total knee replacement (TKR) were assessed over 13.5 years. The area under the curve (AUC) was applied to compare the predictive validity of logistic regression with the LightGBM algorithm. For significant imbalanced outcomes, the area under the precision-recall curve (AUC-PR) was used. 574 participants (mean 62 years, 49 % female) were included. Overall, the LightGBM showed a clinically acceptable predictive performance for all outcomes but TKR. For knee pain and function, LightGBM showed better predictive performance than logistic regression model (AUC: 0.731-0.912 vs 0.627-0.755). Similar results were found for tibial cartilage loss over 2.6 (AUC: 0.845 vs 0.701, p < 0.001) and 10.7 years (AUC: 0.845 vs 0.753, p = 0.016). For TKR, which exhibited significant class imbalance, both algorithms performed poorly (AUC-PR: 0.647 vs 0.610). Compared to logistic regression combining MRI-OA, ROA, and common covariates, LightGBM offers valuable insights that can inform early risk identification and targeted prevention strategies.