Kinematic and Kinetic Changes after Total Hip Arthroplasty during Sit-To-Stand Transfers: Systematic Review

Small Random Angular Variations in Pelvic Tilt and Lower Extremity Can Cause Error in Static Image-based Preoperative Hip Arthroplasty Planning: A Computer Modeling Study

BACKGROUND

Many THA simulation models rely on a limited set of preoperative static radiographs to replicate sagittal pelvic tilt during functional positions and to recommend an implant orientation that minimizes the risk of prosthetic impingement. However, possible random changes in pelvic or lower extremity angular motions and the effect of coronal and axial pelvic tilt are not included in these preoperative models.

QUESTIONS/PURPOSES

(1) Can prosthetic impingement occur if the pelvic tilt or lower extremity alignment randomly varies up to ± 5° from what is measured on a single preoperative static radiographic image? (2) Do changes in coronal and axial pelvic tilt or lower extremity alignment angles have a similar effect on the risk of prosthetic impingement?

METHODS

A de-identified pelvis and lower-body CT image of a male patient without previous THA or lower extremity surgery was used to import the pelvis, femur, and tibia into a verified MATLAB computer model. The motions of standing, pivoting, sitting, sit-to-stand, squatting, and bending forward were simulated. THA implant components included a full hemispherical acetabular cup without an elevated rim, polyethylene liner without an elevated rim, femoral head (diameter: 28 mm, 32 mm, 36 mm, or 40 mm), and a triple-taper cementless stem with three different neck shaft angles (127°, 132°, or 135°) with a trapezoidal neck were used in this model. A static model (cup anatomical abduction 40°, cup anatomical anteversion 20°, stem anatomical anteversion 10°) with a predefined range of sagittal pelvic tilt and hip alignment (0° coronal or axial tilt, without random ± 5° change) was used to simulate each motion. We then randomly varied pelvic tilt in three different pelvic planes and hip alignments (flexion, extension, abduction, adduction, rotation) up to ± 5° and assessed the same motions without changing the implant's anatomical orientation. Prosthetic impingement as the endpoint was defined as mechanical abutment between the prosthetic neck and polyethylene liner. Multiple logistic regression was used to investigate the effect of variation in pelvic tilt and hip alignment (predictors) on prosthetic impingement (primary outcome).

RESULTS

The static-based model without the random variation did not result in any prosthetic impingement under any conditions. However, with up to ± 5° of random variation in the pelvic tilt and hip alignment angles, prosthetic impingement occurred in pivoting (18 possible combinations), sit-to-stand (106 possible combinations), and squatting (one possible combination) when a 28-mm or a 32-mm head was used. Variation in sagittal tilt (odds ratio 4.09 [95% CI 3.11 to 5.37]; p < 0.001), axial tilt (OR 3.87 [95% CI 2.96 to 5.07]; p < 0.001), and coronal tilt (OR 2.39 [95% CI 2.03 to 2.83]; p < 0.001) affected the risk of prosthetic impingement. Variation in hip flexion had a strong impact on the risk of prosthetic impingement (OR 4.11 [95% CI 3.38 to 4.99]; p < 0.001).

CONCLUSION

The combined effect of 2° to 3° of change in multiple pelvic tilt or hip alignment angles relative to what is measured on a single static radiographic image can result in prosthetic impingement. Relying on a few preoperative static radiographic images to minimize the risk of prosthetic impingement, without including femoral implant orientation, axial and coronal pelvic tilt, and random angular variation in pelvis and lower extremity alignment, may not be adequate and may fail to predict prosthetic impingement-free ROM.

CLINICAL RELEVANCE

Determining a safe zone for THA implant positioning with respect to impingement may require a dynamic computer simulation model to fully capture the range of possible impingement conditions. Future work should concentrate on devising simple and easily available methods for dynamic motion analysis instead of using a few static radiographs for preoperative planning.

An Embodied Sonification Model for Sit-to-Stand Transfers

Interactive sonification of biomechanical quantities is gaining relevance as a motor learning aid in movement rehabilitation, as well as a monitoring tool. However, existing gaps in sonification research (issues related to meaning, aesthetics, and clinical effects) have prevented its widespread recognition and adoption in such applications. The incorporation of embodied principles and musical structures in sonification design has gradually become popular, particularly in applications related to human movement. In this study, we propose a general sonification model for the sit-to-stand (STS) transfer, an important activity of daily living. The model contains a fixed component independent of the use-case, which represents the rising motion of the body as an ascending melody using the physical model of a flute. In addition, a flexible component concurrently sonifies STS features of clinical interest in a particular rehabilitative/monitoring situation. Here, we chose to represent shank angular jerk and movement stoppages (freezes), through perceptually salient pitch modulations and bell sounds. We outline the details of our technical implementation of the model. We evaluated the model by means of a listening test experiment with 25 healthy participants, who were asked to identify six normal and simulated impaired STS patterns from sonified versions containing various combinations of the constituent mappings of the model. Overall, we found that the participants were able to classify the patterns accurately (86.67 ± 14.69% correct responses with the full model, 71.56% overall), confidently (64.95 ± 16.52% self-reported rating), and in a timely manner (response time: 4.28 ± 1.52 s). The amount of sonified kinematic information significantly impacted classification accuracy. The six STS patterns were also classified with significantly different accuracy depending on their kinematic characteristics. Learning effects were seen in the form of increased accuracy and confidence with repeated exposure to the sound sequences. We found no significant accuracy differences based on the participants' level of music training. Overall, we see our model as a concrete conceptual and technical starting point for STS sonification design catering to rehabilitative and clinical monitoring applications.