Prosthesis, Volume 6, Issue 4 (August 2024) – 2 articles

Registries have significant roles in assessing the comparative performance of devices. Ideally, early identification of outliers should use a time-to-event outcome while reducing the confounding effects of other components in the device and patient characteristics. Machine learning (ML), which contains self-learning algorithms, is one approach to consider many variables simultaneously to reduce the impact of confounding. The principal objective of this study was to investigate the effectiveness of using either random survival forest (RSF) or regularised/unregularised Cox regression to account for patient and associated device confounding factors in comparison with current standard techniques. This study evaluated RSF and regularised/unregularised Cox regression using data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) to detect outlier devices among 213 individual primary total hip components performed in 163,356 primary procedures from 1 January 2015 to the end of 2019. Device components and patient characteristics were the inputs, and time to first revision surgery was the primary outcome treated as a censored case for death. The effectiveness of the ML approaches was assessed based on the ability to detect the outliers identified by the AOANJRR standard approach. In the study cohort, the standardised AOANJRR approach identified three acetabular components and seven femoral stems as outliers. The ML approaches identified some but not all the outliers detected by the AOANJRR. Both the methods identified three of the same femoral stems, and the RSF identified the other five components, including two of the same acetabular cups and three of the same femoral stems. In addition, both the RSF and Cox techniques detected a number of additional device components that were not previously identified by the standard approach. The results showed that ML may be able to offer a supplementary approach to enhance the early identification of outlier devices. Random survival forest was a more comparable technique to the AOANJRR standard than the Cox regression, but further studies are required to better understand the potential of ML to improve the early identification of outliers. Full article

The ankle joint is pivotal in prosthetic feet, especially in Energy-Storing-and-Releasing feet, favoured by individuals with moderate to high mobility (K3/K4) due to their energy efficiency and simple construction. ESR feet, mainly designed for sagittal-plane motion, often exhibit high stiffness in other planes, leading to difficulties in adapting to varied ground conditions, potentially causing discomfort or pain. This study aims to present a systematic methodology for modifying the ankle joint’s stiffness properties across its three motion planes, tailored to individual user preferences, and to decouple the sagittal-plane behaviour from the frontal and transverse ones. To integrate the multi-axial ankle inside the MyFlex-$\eta$, the designing of experiments using finite element analysis was conducted to explore the impact of geometric parameters on the joint’s properties with respect to design constraints and to reach the defined stiffness targets on the three ankle’s motion planes. A prototype of the multi-axial ankle joint was then manufactured and tested under FEA-derived load conditions to validate the final configuration chosen. Composite elastic elements and complementary parts of the MyFlex-$\eta$, incorporating the multi-axial ankle joint, were developed, and the prosthesis was biomechanically tested according to lower limb prosthesis ISO standards and guidelines from literature and the American Orthotic and Prosthetic Association (AOPA). Experimental tests showed strong alignment with numerical predictions. Moreover, implementing the multi-axial ankle significantly increased frontal-plane compliance by 414% with respect to the same prosthesis with only one degree of freedom on the sagittal plane without affecting the main plane of locomotion performance. Full article