Aims. The aim of this study was to identify the optimal lip position for total hip arthroplasties (THAs) using a lipped liner. There is a lack of consensus on the optimal position, with substantial variability in surgeon practice. Methods. A model of a THA was developed using a 20° lipped liner. Kinematic analyses included a physiological range of motion (ROM) analysis and a provocative dislocation manoeuvre analysis. ROM prior to impingement was calculated and, in impingement scenarios, the travel distance prior to dislocation was assessed. The combinations analyzed included nine cup positions (inclination 30-40-50°, anteversion 5-15-25°), three stem positions (anteversion 0-15-30°), and five lip orientations (right hip 7 to 11 o’clock). Results. The position of the lip changes the ROM prior to impingement, with certain combinations leading to impingement within the physiological ROM. Inferior lip positions (7 to 8 o’clock) performed best with cup inclinations of 30° and 40°. Superior lip positions performed best with cup inclination of 50°. When impingement occurs in the plane of the lip, the lip increases the travel distance prior to dislocation. Inferior lip positions led to the largest increase in jump distance in a
Aims. In the native hip, the hip capsular ligaments tighten at the limits of range of hip motion and may provide a passive stabilizing force to protect the hip against edge loading. In this study we quantified the stabilizing force vectors generated by capsular ligaments at extreme range of motion (ROM), and examined their ability to prevent edge loading. Methods. Torque-rotation curves were obtained from nine cadaveric hips to define the rotational restraint contributions of the capsular ligaments in 36 positions. A ligament model was developed to determine the line-of-action and effective moment arms of the medial/lateral iliofemoral, ischiofemoral, and pubofemoral ligaments in all positions. The functioning ligament forces and stiffness were determined at 5 Nm rotational restraint. In each position, the contribution of engaged capsular ligaments to the joint reaction force was used to evaluate the net force vector generated by the capsule. Results. The medial and lateral arms of the iliofemoral ligament generated the highest inbound force vector in positions combining extension and adduction providing anterior stability. The ischiofemoral ligament generated the highest inbound force in flexion with adduction and internal rotation (FADIR), reducing the risk of
Post-traumatic periprosthetic acetabular fractures are rare but serious. Few studies carried out on small cohorts have reported them in the literature. The aim of this work is to describe the specific characteristics of post-traumatic periprosthetic acetabular fractures, and the outcome of their surgical treatment in terms of function and complications. Patients with this type of fracture were identified retrospectively over a period of six years (January 2016 to December 2021). The following data were collected: demographic characteristics, date of insertion of the prosthesis, details of the intervention, date of the trauma, characteristics of the fracture, and type of treatment. Functional results were assessed with the Harris Hip Score (HHS). Data concerning complications of treatment were collected.Aims
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Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.Aims
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