Sampling strategies for approximating patient variability in population-based finite element studies of total hip replacement.

Abstract

Total hip replacements must be robust to patient variability for long-term success in the population. The challenge during the design process is evaluating an implant in a diverse population but the computational cost of simulating a population of subject-specific finite element (FE) models is not practical. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models to approximate the variability in output metrics. Comparing with the median and distribution of the 95th percentile composite peak micromotion (CPM) and polar gap in the full cohort (CPM median: 136μm, interquartile range [IQR]: 74-230μm) (Polar Gap median: 467μm, IQR: 434-548μm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169μm, IQR: 78-236μm) (Polar Gap median: 469μm, IQR: 448-537μm). Convex hull sampling (41 subjects) more closely captured the output metrics (CPM median: 99μm, IQR: 70-191μm) (Polar Gap median: 456μm, IQR: 418-533μm) but required over three times the number of subjects. Volume reduction of the convex hull captured the extremes of variability with subsets of 5 to 20 subjects, while the largest minimum-distance strategy captured the variability toward the middle of the cohort. These strategies can estimate the level of variability in FE model output metrics with a low computational cost when evaluating implants during the design process.