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A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis
Rongjie Wu1,2, Yuanchen Ma1, Yuhui Yang1
1Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China.
Background:
Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.
Methods:
Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.
Results:
A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.
Conclusion:
Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.
Key Points:
• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.
