Patients (2.7M in EU) with positive cancer prognosis frequently develop metastases (≈1M) in their remaining lifetime. In 30-70% cases, metastases affect the spine, reducing the strength of the affected vertebrae. Fractures occur in ≈30% patients. Clinicians must choose between leaving the patient exposed to a high fracture risk (with dramatic consequences) and operating to stabilise the spine (exposing patients to unnecessary surgeries). Currently, surgeons rely on their sole experience. This often results in to under- or over-treatment. The standard-of-care are scoring systems (e.g. Spine Instability Neoplastic Score) based on medical images, with little consideration of the spine biomechanics, and of the structure of the vertebrae involved. Such scoring systems fail to provide clear indications in ≈60% patients. The HEU-funded METASTRA project is implemented by biomechanicians, modellers, clinicians, experts in verification, validation, uncertainty quantification and certification from 15 partners across Europe. METASTRA aims to improve the stratification of patients with vertebral metastases evaluating their risk of fracture by developing dedicated reliable computational models based on Explainable Artificial Intelligence (AI) and on personalised Physiology-based biomechanical (VPH) models.Introduction
Method
Preclinical testing of implants considers THR patients a homogenous group; in reality, patients are heterogeneous and previous large cohort studies have explored stratification and identified that THR patients function differently [1]. The wide- spread failure of the ASR hip highlighted the potential importance of patient characteristics [2], and a more robust pre- clinical testing procedure may have improved prediction of outcome. Therefore this study aimed to identify differences in hip contact force (HCF) in THR patients stratified by their functional ability. 133 THR patients, >12 months post-surgery, underwent 3D kinematic (Vicon, UK) and kinetic (AMTI, USA) analysis whilst walking at self-selected speed. HCF's, normalized by body weight, were computed through multibody modeling (AnyBody Technology, Denmark) during gait and a mean for each patient was calculated from three to five walking trials. Patients were stratified into three functionality groups by distribution around the mean gait speed for the full cohort of 1.1m/s. The low functioning group (LF) comprised cases with a gait speed ≤0.93 m/s (i.e. 1.1m/s ≤1SD), the mid functioning group (MF) comprised cases with a gait speed between 0.94 m/s and 1.25 m/s (cohort mean ± 1SD), while the high functioning group (HF) included cases walking ≥1.26 m/s. Differences between groups were analyzed using one- dimensional statistical parametric mapping [3]. Linear regression was used to test for significant differences across groups. The test statistic SPM{t} was evaluated at each point in the normalized time series, and a critical threshold corresponding to an error rate of α= 0.05 was calculated based on random field theory. Supra-threshold clusters with their associated p-values were then identified.Introduction
Methods
UKA is functionally superior to TKA, with kinematics similar to native knees, nevertheless, UKA implants are used in less than 10% of cases. While advantages of UKA are recognized, ACL-deficiency is generally considered a contraindication. The hypothesis of this study was that fix bearing UKA in ACL-deficient knees, with appropriate adaptation of implant placement, would result in similar kinematic trends to conventional UKA with an intact ACL. Ten conventional UKA patients were compared to eight patients with the same implant but a deficient ACL. A 50% tibial slope reduction was applied to compensate for instability resulting from the deficient ACL. Knee kinematics were evaluated using a moving fluoroscope allowing to track the knee joint during deep knee bend, level walking, ramp descent and stair descent. The results were further compared to six TKA patients.BACKGROUND
METHODS
Soft tissue artefact (STA) affects the kinematics retrieved with skin marker-based motion capture, and thus influences the outcomes of biomechanical models that rely on such kinematics. To date, compensation for STA remains an unsolved challenge due to its complexity. Factors include its dependency on subject, on motion activity and on skin-marker configuration, its non-linearity over the movement cycle, and the scarcity of reference in-vivo estimations. The objective of this study was extending the existing knowledge of the effects of STA on the kinematics of the hip joint and on the hip joint center location, by quantifying them for a sample total hip arthroplasty (THA) population, for a broader range of activities of daily living (ADLs). Four activities of daily living (overground gait, stairs descent, chair rise and putting on socks) were measured simultaneously with optical motion capture (MC) at 100 Hz and with a movable single-plane video-fluoroscopy system (VF) at 25 Hz, for fifteen patients with successful total hip arthroplasty (THA). The joint segment positions were computed by least-square fitting for MC and by semi-automatic 2D/3D registration for VF. Anatomical coordinate systems were defined for each joint segment based on skin markers location at a reference standing position. Errors induced by STA on the retrieved joint motion were computed as the difference between MC-based kinematics and the reference VF-based kinematics. Statistical analysis was carried out to determine the whether the differences between the kinematics obtained with the two methods were significant.Introduction
Methods
