Predicting Proximal Femoral Remodeling After Short-Stem Hip Arthroplasty: A Biomechanical Modeling Approach

Background: Short-stem hip replacements are designed to provide improved load distribution and to mimic natural biomechanics. The interplay between implant design, positioning, and resulting bone biomechanics in individual patients remains underexplored, and the relationship between radiographically assessed bone remodeling around short stems and biomechanical predictions has not been previously reported. Methods: This study evaluated three short-stem hip implant designs: Proxima, Collo-MIS, and Minima. Postoperative bone remodeling patterns were analyzed, categorizing remodeling as bone gain, bone loss, or no observable activity, with changes tracked over time. Patient-specific biomechanical models were generated from 6-week postoperative radiographs. Finite element simulations incorporated body weight and gluteal muscle forces to estimate stress and strain distributions within the proximal femur. Strain energy was then applied to a mechanostat-based remodeling algorithm to predict bone remodeling patterns. These biomechanical predictions were compared to observed radiographic remodeling at 2 years post-surgery. A validated biomechanical model was further used to simulate different postoperative positions of the three types of stems. Results: No differences in bone remodeling patterns were observed among the three short-stem designs. Computational modeling demonstrated a statistically significant correlation between predicted remodeling and radiographic measurements at 2 years (p < 0.001). Proxima stems showed a tendency towards increased cortical bone loading under pronounced varus or valgus position in comparison to other two stems, although this observation requires further validation. Conclusions: This exploratory study demonstrates the feasibility of using biomechanical modeling to estimate bone remodeling around short-stem hip implants based on early postoperative radiographs. While the results are promising, they should be interpreted with caution due to the limited cohort size. The proposed modeling approach may offer clinical value in evaluating implant behavior and informing patient-specific treatment strategies. However, further research with larger populations is necessary to refine and validate these predictive tools.