Reliability-Based Design Optimization of Hip Prosthesis Jump Distance: Monte Carlo Analysis of 1,617 Design Configurations

Abstract

This study presents a reliability-based design optimization (RBDO) framework for improving the intrinsic stability of total hip arthroplasty through jump-distance-based design selection. The problem was formulated using four design variables: femoral head diameter, cup abduction angle, cup anteversion angle, and femoral offset. Jump distance (JD) was computed from an analytical geometric model, and uncertainty was propagated by Monte Carlo simulation with 10,000 samples per scenario. A total of 1,617 configurations were evaluated in MATLAB R2020b using parallel computing. The results showed a mean reliability index of 2.3945, with values ranging from -2.9677 to 3.8906, and a mean Monte Carlo jump distance of 12.0659 mm. The best-performing configuration within the explored design space was a 36 mm head, 30° abduction, 30° anteversion, and -2 mm offset, yielding a nominal JD of 20.601 mm, a mean simulated JD of 20.568 mm, and zero failures in 10,000 trials. Parametric analysis showed that abduction angle had the strongest negative influence on reliability, followed by femoral offset, whereas head diameter had a positive effect and anteversion had a weaker but favorable contribution. These results confirm that a probabilistic design framework can discriminate robust from fragile implant configurations more effectively than a purely deterministic threshold check and provide a computationally efficient basis for preclinical implant-orientation optimization.