Healthy knee kinematic phenotypes identification based on a clustering data analysis

The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population

Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase

Objective: To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. Methods: 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. Results: Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. Conclusion: The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial com- partment knee OA

Femoral neck narrowing following hip resurfacing using posterior and Ganz approaches at two years

We report a retrospective review of femoral head/neck ratios on post-operative and two year follow-up radiographs following hip resurfacing arthroplasty. The patients were in two matched groups, having had surgery through a posterior approach or via a Ganz trochanteric flip. There was no significant difference in femoral neck narrowing at follow up between the two surgical approaches. However, we found significant narrowing of the femoral neck in both groups by the time of the two year follow-up radiograph

A fatigue assessment technique for modular and pre-stressed orthopaedic implants

Orthopaedic implants experience large cyclic loads, and pre-clinical analysis is conducted to ensure they can withstand millions of loading cycles. Acetabular cup developments aim to reduce wall thickness to conserve bone, and this produces high pre-stress in modular implants. As part of an implant development process, we propose a technique for preclinical fatigue strength assessment of modular implants which accounts for this mean stress, stress concentrating features and material processing.A modular cup’s stress distributions were predicted computationally, under assembly and in-vivo loads, and its cyclic residual stress and stress amplitude were calculated. For verification against damage initiation in low-cycle-fatigue (LCF), the peak stress was compared to the material’s yield strength. For verification against failure in high-cycle-fatigue (HCF) each element’s reserve factor was calculated using the conservative Soderberg infinite life criterion.Results demonstrated the importance of accounting for mean stress. The cup was predicted to experience high cyclic mean stress with low magnitude stress amplitude: a low cyclic load ratio (Rl = 0.1) produced a high cyclic stress ratio (Rs = 0.80). Furthermore the locations of highest cyclic mean stress and stress amplitude did not coincide. The minimum predicted reserve factor Nf was 1.96 (HCF) and 2.08 (LCF). If mean stress were neglected or if the stress ratio were assumed to equal the load ratio, the reserve factor would be considerably lower, potentially leading to over-engineering, reducing bone conservation.Fatigue strength evaluation is only one step in a broader development process, which should involve a series of verifications with the full range of normal and traumatic physiological loading scenarios, with representative boundary conditions and a representative environment. This study presents and justifies a fatigue analysis methodology which could be applied in early stage development to a variety of modular and pre-stressed prosthesis concepts, and is particularly relevant as implant development aims to maximise modularity and bone conservation