Understanding load distribution in high tibial osteotomies: An inverse Discrete Element Analysis approach

With up to 40% of knee osteotomies yielding suboptimal results, improved preoperative planning is needed. This study aimed to develop an inverse Discrete Element Analysis (iDEA) approach to calculate and compare patient-specific pressure patterns against traditional clinical metrics, pre- and postoperatively.

Eight varus knee osteotomy patients and eight matched controls were analyzed. Personalized knee geometries were generated from Weight-Bearing Computed Tomography (WBCT) scans using Statistical Shape Modeling. Automated angular measurements were conducted and Joint Space Widths (JSW) were calculated as the Euclidean distance between the femoral and tibial bones. The iDEA approach calculated preoperative, postoperative and control pressure patterns. Group comparisons utilized one-way ANOVA or Kruskal-Wallis testing, depending on normality. A Spearman rank test was performed to assess the correlation between changes in joint space width and mean stress distribution.

The preoperative group showed significant differences compared to the controls across all angular measurements (Hip-Knee-Ankle angle (HKA): p < 0.001, Joint Line Conversion Angle: p < 0.001, lateral Proximal Tibial Angle: p = 0.044) and the medial-to-lateral JSW ratio (p = 0.017). Postoperatively, only the HKA remained significantly different from controls (p = 0.044). Pressure calculations revealed significantly higher peak stress ratios preoperatively than in controls (p = 0.029). Two subjects showed postoperative worsening of JSW and pressure patterns while angular measurements improved. JSW increases were correlated with decreases in mean stress (correlation coefficient = -0.54,p = 0.033).

The iDEA approach quantified intra-articular pressure distributions and revealed a general normalization trend postoperatively, supporting the effectiveness of knee osteotomy. The identification of postoperative deteriorations not captured by traditional metrics, underscores the added value of the iDEA framework.