Objectives. There are few reports on
Musculoskeletal loading plays an important role in the primary stability of THA. There are about 210,000 primary THA interventions p.a. in Germany. Consideration of biomechanical aspects during computer-assisted orthopaedic surgery is recommendable in order to obtain satisfactory long-term results. For this purpose simulation of the pre- and post-operative magnitude of the resultant hip joint force R and its orientation is of interest. By means of simple 2D-models (Pauwels, Debrunner, Blumentritt) or more complex 3D-models (Iglič), the magnitude and orientation of R can be computed patient-individually depending on their geometrical and anthropometrical parameters. In the context of developing a planning module for computer-assisted THA, the objective of this study was to evaluate the mathematical models. Therefore, mathematical model computations were directly compared to in-vivo measurements obtained from instrumented hip implants. With patient-specific parameters the magnitude and orientation of R were model-based computed for three patients (EBL, HSR, KWR) of the OrthoLoad-database. Their patient-specific parameters were acquired from the original patient X-rays. Subsequently, the computational results were compared with the corresponding in-vivo telemetric measurements published in the OrthoLoad-database. To obtain the maximum hip joint load, the static single-leg-stance was considered. A reference value for each patient for the maximum hip load under static conditions was calculated from OrthoLoad-data and related to the respective body weights (BW). On average there are large deviations of the results for the magnitude (Ø=147%) and orientation (Ø=14.35° too low) of R obtained by using Blumentritt's model from the in-vivo results/measurements. The differences might be partly explained by the supplemental load of 20% BW within Blumentritt's model which is added to the input parameter BW in order to consider dynamic gait influences. Such a dynamic supplemental load is not applied within the other static single-leg-stance models. Blumentritt's model assumptions have to be carefully reviewed due to the deviations from the in-vivo measurement data. Iglič's 3D-model calculates the magnitude (Ø17%) and the orientation (Ø49%) of R slightly too low. For the magnitude one explanation could be that his model considers nine individual 3D-sets of muscle origins and insertion points taken from literature. This is different from other mathematical models. The patient-individual muscle origin and insertion points should be used. Pauwels and Debrunner's models showed the best results. They are in the same range compared to in-vivo data. Pauwels's model calculates the magnitude (Ø5%) and the orientation (Ø28%) of R slightly higher. Debrunner's model calculates the magnitude (Ø1%) and the orientation (Ø14%) of R slightly lower. In conclusion, for the orientation of R, all the computational results showed variations which tend to depend on the used model. There are limitations coming along with our study: as our previous studies showed, an unambiguous identification of most landmarks in an X-ray (2D) image is hardly possible. Among the study limitations there is the fact that the OrthoLoad-database currently offers only three datasets for direct comparison of static single leg stance with in-vivo measurement data of the same patient. Our ongoing work is focusing on further validation of the different mathematical models.
Patient medical comorbidities are well-established risk modifiers of THA patient outcomes. Patient's mental state preoperatively may influence postoperative functional outcomes though just like any medical comorbidity. This study sought to determine if patient confidence in attaining post-operative functional goals was associated with objective and subjective outcomes following THA. Patients undergoing primary or revision THA at a single institution between 2008 and 2010 were administered a questionnaire consisting of demographics, body mass index, Hip Dysfunction Osteoarthritis and Outcomes Score (HOOS), SF-12 scores, the level of functionality they hoped to gain postoperatively and their confidence in attaining that goal (0–10 scale) preoperatively and postoperatively at last follow-up (minimum 12 months). Measured outcomes included length of stay, 30-day readmission, HOOS, and SF-12 physical component scores. Correlation of patient confidence in attaining treatment goals and the outcomes collected was established using multiple linear and logistic regression models that were adjusted for all variables, including baseline mental and functional scores.Introduction:
Methods:
Subluxation and dislocation are frequently cited reasons for THA revision. For patients who cannot accommodate a larger femoral head, an offset liner may enhance stability. However, this change in biomechanics may impact the mechanical performance of the bearing surface. To our knowledge, no studies have compared wear rates of offset and neutral liners. Herein we radiographically compare the in-vivo wear performance of 0mm and 4mm offset acetabular liners. Two cohorts of 40 individuals (0mm, 4mm offset highly crosslinked acetabular liners, respectively) were selected from a single surgeon's consecutive caseload. All patients received the same THA system via the posterior approach. AP radiographs were taken at 6-week (‘pre’) and 5-year (‘post’) postoperative appointments. Patients with poor radiograph quality were excluded (n0mm=5, n4mm=4). Linear and volumetric wear were quantified according to Patent US5610966A. Briefly, images were processed in computer aided design (CAD) software. Differences in vector length between the center of the femoral head and the acetabular cup (pre- and post-vector, Figure 1) allow for calculation of linear wear and wear rate. The angle (β) between the linear wear vector and the cup inclination line was quantified (Figure 1). Patients with negative β were excluded from volumetric analyses (n0mm=11, n4mm=7). Volumetric wear was accordingly calculated accounting for wear vector direction. The results from three randomly selected patients were compared to results achieved using the “Hip Analysis Suite” software package (UChicagoTech).Introduction
Methods
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