DOI 10.22038/abjs.2025.80846.3689
Predictive Radiographic Factors for Soft Tissue Release and Distal Femoral Cut Angle for Appropriate Biomechanics in Total Knee Arthroplasty.
Аннотация
OBJECTIVES
Total knee arthroplasty (TKA) relies on precise soft tissue releases and bone cuts for balanced weight distribution. Challenges include the lack of predictors to guide the extent and timing of soft tissue release. This study examines the relationship between radiographic parameters and the distal femur valgus cut angle (VCA), and their correlation with the medial soft tissue release (MSTR) stage in TKA patients. The goal is to identify predictors that aid in achieving optimal biomechanics and tissue balance.
METHODS
In this prospective cohort study, we examined preoperative standard lower limb three-joint views of 62 limbs (57 patients) to explore the relationship between radiographic parameters, the stage of MSTR, and VCA. Univariate and multivariate linear regression analyses, along with various statistical tools, were used to identify relationships and determine cut-off values.
RESULTS
A notable positive correlation was observed between VCA and medial hip offset (MHO), as well as between VCA and femoral length (FL), with both correlations yielding P < .001. Patients with shorter femurs and an MHO greater than 4.35 cm required a distal femoral cut angle of 6 degrees or more, while those with longer femurs and an MHO less than 4.35 cm needed an angle of less than 6 degrees (sensitivity: 83%, specificity: 80%). Additionally, the joint line congruency angle (JLCA), varus angle (VA), and lateral distal femoral angle (LDFA) showed significant correlations with the stage of MSTR. Among these variables, the VA emerged as the most accurate predictor, with a sensitivity of 91.7% and a specificity of 100%.
CONCLUSION
Increasing the LDFA to above 93.5°, JLCA to above 7.5°, and the VA to above 19° would heighten the probability of requiring extensive MSTR. Additionally, MHO and FL are the most crucial predictive factors for determining the VCA.
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