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ABSTRACT: For cementless femoral stems, there is debate as to whether a collar enhances primary stability and load transfer compared to collarless designs. Finite Element (FE) analysis has the potential to compare stem designs within the same cohort, allowing for subtle performance differences to be identified, if present. Subject‐specific FE models of intact and implanted femora were run for a diverse cohort (21 males, 20 females; BMI 16.4–41.2 kg/m2, age 50–80 yrs). Collared and collarless versions of Corail® (DePuy Synthes, Warsaw, IN) were sized and positioned using an automated algorithm that aligns the femoral/stem axes, preserves the head‐center location, and maximizes metaphyseal fit. Joint contact and muscle forces simulating peak forces in level gait and stair climbing and were scaled to the body mass and applied to each subject. Three failure scenarios were assessed: Potential for peri‐prosthetic fibrous tissue formation (stem micromotion), potential for peri‐prosthetic bone damage (equivalent strains), and calcar bone remodeling (changes in strain‐energy density). Comparisons were performed using paired t‐tests. Only subtle differences were found (mean 90th percentile micromotion: Collared = 86 µm, collarless = 92.5 µm, mean 90th percentile interface strains: Collared = 733 µϵ, collarless = 767 µϵ, and similar remodeling stimuli were predicted). The slight differences observed were small in comparison with the inter‐patient variability. Statement of clinical significance: Our results suggest that the presence/absence of a collar is unlikely to substantially alter the bone‐implant biomechanics nor the initial mechanical environment. Hence, a collar is likely to have minimal clinical impact. Analysis using different femoral stem designs is recommended before generalising these findings. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1185–1195, 2018. [ABSTRACT FROM AUTHOR]

ABSTRACT: The primary stability achieved during total hip arthroplasty determines the long‐term success of cementless acetabular cups. Pre‐clinical finite element testing of cups typically use a model of a single patient and assume the results can be extrapolated to the general population. This study explored the variability in predicted primary stability of a Pinnacle® cementless acetabular cup in 103 patient‐specific finite element models of the hemipelvis and examined the association between patient‐related factors and the observed variability. Cups were inserted by displacement‐control into the FE models and then a loading configuration simulating a complete level gait cycle was applied. The cohort showed a range of polar gap of 284–1112 μm and 95th percentile composite peak micromotion (CPM) of 18–624 μm. Regression analysis was not conclusive on the relationship between patient‐related factors and primary stability. No relationship was found between polar gap and micromotion. However, when the patient‐related factors were categorised into quartile groups, trends suggested higher polar gaps occurred in subjects with small and shallow acetabular geometries and cup motion during gait was affected most by low elastic modulus and high bodyweight. The variation in primary stability in the cohort for an acetabular cup with a proven clinical track record may provide benchmark data when evaluating new cup designs. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1012–1023, 2018. [ABSTRACT FROM AUTHOR]

For clinically predictive testing and design-phase evaluation of prospective total knee replacement (TKR) implants, devices should ideally be evaluated under physiological loading conditions which incorporate population-level variability. A challenge exists for experimental and computational researchers in determining appropriate loading conditions for wear and kinematic knee simulators which reflect in vivo joint loading conditions. There is a great deal of kinematic data available from fluoroscopy studies. The purpose of this work was to develop computational methods to derive anterior-posterior (A-P) and internal-external (I-E) tibiofemoral (TF) joint loading conditions from in vivo kinematic data. Two computational models were developed, a simple TF model, and a more complex lower limb model. These models were driven through external loads applied to the tibia and femur in the TF model, and applied to the hip, ankle and muscles in the lower limb model. A custom feedback controller was integrated with the finite element environment and used to determine the external loads required to reproduce target kinematics at the TF joint. The computational platform was evaluated using in vivo kinematic data from four fluoroscopy patients, and reproduced in vivo A-P and I-E motions and compressive force with a root-mean-square (RMS) accuracy of less than 1 mm, 0.1°, and 40 N in the TF model and in vivo A-P and I-E motions, TF flexion, and compressive loads with a RMS accuracy of less than 1 mm, 0.1°, 1.4°, and 48 N in the lower limb model. The external loading conditions derived from these models can ultimately be used to establish population variability in loading conditions, for eventual use in computational as well as experimental activity simulations. [ABSTRACT FROM AUTHOR]

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: 136 μm, interquartile range [IQR]: 74‐230 μm) (Polar Gap median: 467 μm, IQR: 434‐548 μm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169 μm, IQR: 78‐236 μm) (Polar Gap median: 469 μm, IQR: 448‐537 μm). Convex hull sampling (41 subjects) more closely captured the output metrics (CPM median: 99 μm, IQR: 70‐191 μm) (Polar Gap median: 456 μm, IQR: 418‐533 μm) 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. The challenge during designing new implants in total hip replacement is evaluating their performance in a diverse population before a clinical trial. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models in order to approximate the patient variability in output metrics of primary stability. The strategies presented could grossly indicate the level of patient variability in FE model output metrics of the full cohort with a fraction of the subjects. [ABSTRACT FROM AUTHOR]