Experimental assessment of pelvis slipping during postless traction for orthopaedic applications.

BACKGROUND: The application of lower limb traction during hip arthroscopy and femur fractures osteosynthesis is commonplace in orthopaedic surgeries. Traditional methods utilize a perineal post on a traction table, leading to soft tissue damage and nerve neuropraxia. A postless technique, using high-friction pads, has been considered as a potential damage-free alternative. However, whether these pads sufficiently prevent patient displacement remains unknown. Thus, this study systematically assesses the efficacy of commercial high-friction pads (PinkPad and CarePad) in restraining subject displacement, for progressively increasing traction loads and different Trendelenburg angles. METHODS: Three healthy male subjects were recruited and tested in supine and Trendelenburg positions (5° and 10°), using a customized boot-pulley system. Ten load disks (5 kg) were dropped at 15s intervals, increasing gradually the traction load up to 50 kg. Pelvis displacement along the traction direction was measured with a motion capture system. The displacement at 50 kg of traction load was analyzed and compared across various pads and bed inclinations. Response to varying traction loads was statistically assessed with a quadratic function model. RESULTS: Pelvis displacement at 50 kg traction load was below 60 mm for all conditions. Comparing PinkPad and CarePad, no significant differences in displacement were observed. Finally, similar displacements were observed for the supine and Trendelenburg positions. CONCLUSIONS: Both PinkPad and CarePad exhibited nearly linear behavior under increasing traction loads, limiting displacement to 60 mm at most for 50 kg loads. Contrary to expectations, placing subjects in the Trendelenburg position did not increase adhesion.

Personalised 3D Assessment of Trochanteric Soft Tissues Improves HIP Fracture Classification Accuracy.

Passive soft tissues surrounding the trochanteric region attenuate fall impact forces and thereby control hip fracture risk. The degree of attenuation is related to Soft Tissue Thickness (STT). STT at the neutral hip impact orientation, estimated using a regression relation in body mass index (BMI), was previously shown to influence the current absolute risk of hip fracture (ARF0) and its fracture classification accuracy. The present study investigates whether fracture classification using ARF0 improves when STT is determined from the subject's Computed-Tomography (CT) scans (i.e. personalised) in an orientation-specific (i.e. 3D) manner. STT is calculated as the shortest distance along any impact orientation between a semi-automatically segmented femur surface and an automatically segmented soft tissue/air boundary. For any subject, STT along any of the 33 impact orientations analysed always exceeds the value estimated using BMI. Accuracy of fracture classification using ARF0 improves when using personalised 3D STT estimates (AUC = 0.87) instead of the BMI-based STT estimate (AUC = 0.85). The improvement is smaller (AUC = 0.86) when orientation-specificity of CT-based STT is suppressed and is nil when personalisation is suppressed instead. Thus, fracture classification using ARF0 improves when CT is used to personalise STT estimates and improves further when, in addition, the estimates are orientation specific.

Combining shape and intensity dxa-based statistical approaches for osteoporotic HIP fracture risk assessment.

Aiming to improve osteoporotic hip fracture risk detection, factors other than the largely adopted Bone Mineral Density (BMD) have been investigated as potential risk predictors. In particular Hip Structural Analysis (HSA)-derived parameters accounting for femur geometry, extracted from Dual-energy X-ray Absorptiometry (DXA) images, have been largely considered as geometric risk factors. However, HSA-derived parameters represent discrete and cross-correlated quantities, unable to describe proximal femur geometry as a whole and tightly related to BMD. Focusing on a post-menopausal cohort (N = 28), in this study statistical models of bone shape and BMD distribution have been developed to investigate their possible role in fracture risk. Due to unavailable retrospective patient-specific fracture risk information, here a surrogate fracture risk based on 3D computer simulations has been employed for the statistical framework construction. When considered separately, BMD distribution performed better than shape in explaining the surrogate fracture risk variability for the analysed cohort. However, the combination of BMD and femur shape quantities in a unique statistical model yielded better results. In detail, the first shape-intensity combined mode identified using a Partial Least Square (PLS) algorithm was able to explain 70% of the surrogate fracture risk variability, thus suggesting that a more effective patients stratification can be obtained applying a shape-intensity combination approach, compared to T-score. The findings of this study strongly advocate future research on the role of a combined shape-BMD statistical framework in fracture risk determination.