Iliopsoas tendonitis after total hip arthroplasty : an improved detection method with applications to preoperative planning

Abstract

Aims: Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation.

Methods: This was a retrospective cohort study of patients who underwent THA surgery that included 23 symptomatic patients diagnosed with iliopsoas tendonitis, and 23 patients not diagnosed with iliopsoas tendonitis. All patients received postoperative CT imaging, postoperative standing radiography, and had minimum six months' follow-up. 3D models of each patient's prosthetic and bony anatomy were generated, landmarked, and simulated in a novel iliopsoas impingement detection model in supine and standing pelvic positions. Logistic regression models were implemented to determine if the probability of pain could be significantly predicted. Receiver operating characteristic curves were generated to determine the model's sensitivity, specificity, and area under the curve (AUC).

Results: Highly significant differences between the symptomatic and asymptomatic cohorts were observed for iliopsoas impingement. Logistic regression models determined that the impingement values significantly predicted the probability of groin pain. The simulation had a sensitivity of 74%, specificity of 100%, and an AUC of 0.86.

Conclusion: We developed a computational model that can quantify iliopsoas impingement and verified its accuracy in a case-controlled investigation. This tool has the potential to be used preoperatively, to guide decisions about optimal cup placement, and postoperatively, to assist in the diagnosis of iliopsoas tendonitis.Cite this article: Bone Jt Open 2023;4(1):3-12.

Keywords: Iliopsoas impingement; Iliopsoas tendonitis; Logistic regression models; Soft tissue; THA; Total hip arthroplasty; acetabular components; bony anatomy; femoral heads; groin pain; prosthetic; retrospective cohort study; standing radiography; tendonitis; total hip arthroplasty (THA).

Fig. 1

Flowchart of the patient selection process for the symptomatic cohort, which includes patients who were diagnosed with iliopsoas tendonitis, and asymptomatic cohort, which includes patients who were not diagnosed with iliopsoas tendonitis. THA, total hip arthroplasty.

Fig. 2

Illustration of the landmarks taken for the simulation. The anterior superior iliac spine (ASIS) points and pubic symphysis form the anterior pelvic plane (APP) and allow calculation of the supine pelvic tilt. Psoas Superior 1 to 3 and Psoas Inferior represent the ‘attachment sites’ of the iliopsoas. The femoral head centre was used as the point at which the pelvis rotates around.

Fig. 3

Schematic of the iliopsoas impingement simulation in a patient’s standing pelvic position. Three segments were chosen as they approximate the width of the iliopsoas and the location it passes over the acetabular margin. These segments are composed of two paths: a green and a red path. The green path does not include the cup and the red path does. The difference between these paths is equal to the impingement, and could be considered the ‘stretch’ of the iliopsoas due to the cup.

Fig. 4

Cup prominence results for the symptomatic and asymptomatic cohorts. The symptomatic cohort had significantly greater cup prominence values. The edges of the box represent the 25^th and 75^th percentiles, the solid line within the box represents the median, the dashed line represents the mean, the lines represent the ranges, and the dot represents an outlier

Fig. 5

Mean and maximum impingement results for the symptomatic and asymptomatic cohorts in supine and standing positions. The symptomatic cohort had significantly greater mean and maximum impingement values in both standing and supine.

Fig. 6

Receiver operating characteristic (ROC) curve for the cup prominence logistic regression model. AUC, area under the curve.

Fig. 7

Logistic regression models for standing mean and standing maximum impingement to predict iliopsoas tendonitis. Both significantly predicted the probability of iliopsoas tendonitis (p = 0.019 and p = 0.018, respectively).

Fig. 8

Receiver operating characteristic (ROC) curves for the standing mean impingement and standing maximum impingement logistic regression models. AUC, area under the curve.