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

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...

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Autores principales: Hardwick-Morris, Max, Twiggs, Joshua, Miles, Brad, Al-Dirini, Rami M. A., Taylor, Mark, Balakumar, Jitendra, Walter, William L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The British Editorial Society of Bone & Joint Surgery 2023
Materias:
Hip
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887341/
https://www.ncbi.nlm.nih.gov/pubmed/36598093
http://dx.doi.org/10.1302/2633-1462.41.BJO-2022-0147.R1
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author Hardwick-Morris, Max
Twiggs, Joshua
Miles, Brad
Al-Dirini, Rami M. A.
Taylor, Mark
Balakumar, Jitendra
Walter, William L.
author_facet Hardwick-Morris, Max
Twiggs, Joshua
Miles, Brad
Al-Dirini, Rami M. A.
Taylor, Mark
Balakumar, Jitendra
Walter, William L.
author_sort Hardwick-Morris, Max
collection PubMed
description 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.
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spelling pubmed-98873412023-02-07 Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning Hardwick-Morris, Max Twiggs, Joshua Miles, Brad Al-Dirini, Rami M. A. Taylor, Mark Balakumar, Jitendra Walter, William L. Bone Jt Open Hip 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. The British Editorial Society of Bone & Joint Surgery 2023-01-05 /pmc/articles/PMC9887341/ /pubmed/36598093 http://dx.doi.org/10.1302/2633-1462.41.BJO-2022-0147.R1 Text en © 2023 Author(s) et al. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND 4.0) licence, which permits the copying and redistribution of the work only, and provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Hip
Hardwick-Morris, Max
Twiggs, Joshua
Miles, Brad
Al-Dirini, Rami M. A.
Taylor, Mark
Balakumar, Jitendra
Walter, William L.
Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title_full Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title_fullStr Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title_full_unstemmed Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title_short Iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
title_sort iliopsoas tendonitis after total hip arthroplasty: an improved detection method with applications to preoperative planning
topic Hip
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887341/
https://www.ncbi.nlm.nih.gov/pubmed/36598093
http://dx.doi.org/10.1302/2633-1462.41.BJO-2022-0147.R1
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