Nomogram in Knee Instability: 3D Gait Analysis of Knee Osteoarthritis Patients

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Abstract
Background Measures of knee stability by symptoms, physical examination, and imaging do not accurately reflect the condition of knee movement. Therefore, this study aimed to introduce a model for assessing knee stability during walking in patients with knee osteoarthritis (OA). Aims Three dimensional(3D) gait analysis system was used to quantify the gait of patients and display the clinical diagnosis model of knee instability with nomogram to guide clinical diagnosis and treatment. Methods This cross-sectional study performed a 3D gait analysis in 93 participants with knee OA and 40 healthy control subjects. Multiple linear regression analysis investigated the correlation between gait parameters and knee extension/flexion stability. The predicting models were built applied multinomial logistic regression analysis and calibration plot, C-index, decision curve analysis, bootstrapping validation were used to assess the predicting nomograms' clinical usefulness and internal validation. Results Multiple linear regression analysis indicated knee extension stability was correlated with walking speed (β = 0.256, P = 0.006), knee extensor strength (β = −0.196, P = 0.03), static HKA (β = 0.218, P = 0.016), width of the femoral diaphysis (β = −0.282, P = 0.002) and WOMAC score (β = 0.281, P = 0.002); however, knee flexion stability was correlated with walking speed (β = 0.340, P < 0.001), knee flexor strength (β = −0.327, P < 0.001), posterior tibial slope (PTS) (β = 0.291, P < 0.001), knee flexion/extension range of motion (ROM) (β = 0.177, P = 0.018) and HSS score (β = −0.173, P = 0.028). We developed and internally validated a knee instability risk nomogram in patients with knee OA. Conclusions These results indicated that using the 3D motion analysis system is feasible to quantify knee instability. The current prediction models could serve as a reliable tool to quantify the possibility of knee instability in OA patients. Trial registration number (TRN) ChiCTR2100051302; Date of registration: Sep 18, 2021; retrospectively registered.
Funding Information
  • Natural Science Foundation of China (81974343)
  • Nature Science Foundation of Guangdong Province (2020A1515010394)

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