Abstract
A total hip replacement (THR) patient's spinopelvic mobility might predispose them to an increased risk of impingement, instability and edge-loading. This risk can be minimised by considering their preoperative movement during planning of component alignment. However, the question of whether the preoperative, arthritic motion is representative of the postoperative mobility has been raised. We aimed to determine the change in functional pelvic tilt in a series of THR patients at one-year.
Four-hundred and eleven patients had their pelvic tilt and lumbar lordotic angle (LLA) measured in the standing and flexed-seated (position when patients initiate rising from a seat) positions as part of routine planning for THR. All measurements were performed on lateral radiographs. At 12-months postoperatively, the same two lateral images were taken and pelvic tilt measured. Pearson correlation was used to investigate the linear relationship between pre-and post-op pelvic tilt. Furthermore, a predictive model of post-op pelvic tilt was developed using machine learning algorithms. The model incorporating four preoperative inputs – standing pelvic tilt, seated pelvic tilt, standing LLA and seated LLA.
In the standing position, there was a mean 2° posterior rotation after THR, with a maximum posterior change of 13°. The Pearson correlation coefficient between pre-and post-op standing pelvic tilt was 0.84. This prediction of post-op standing tilt improved to 0.91 when the three further inputs were incorporated to the predictive model.
In the flexed-seated position, there was a mean 7° anterior rotation after THR, with a maximum anterior change of 45°. The Pearson correlation coefficient between pre-and post-op seated pelvic tilt was 0.54. This prediction of post-op seated tilt improved to 0.71 when the three further inputs were incorporated to the predictive model.
The best predictor of post-operative spinopelvic mobility, is the patients pre-operative spinopelvic mobility, and this should routinely be measured when planning THR
The predictive model will continue to improve in accuracy as more data and more variables (contralateral hip pathology, pelvic incidence, age and gender) are incorporated into the model.
