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A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics
BACKGROUND: The purpose of this study was to define the features of scapular morphology that are associated with changes in the critical shoulder angle (CSA) by developing the best predictive model for the CSA based on multiple potential explanatory variables, using a completely 3D assessment. METHO...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685918/ https://www.ncbi.nlm.nih.gov/pubmed/36419105 http://dx.doi.org/10.1186/s12891-022-05920-7 |
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author | Smith, Geoffrey C. S. Geelan-Small, Peter Sawang, Michael |
author_facet | Smith, Geoffrey C. S. Geelan-Small, Peter Sawang, Michael |
author_sort | Smith, Geoffrey C. S. |
collection | PubMed |
description | BACKGROUND: The purpose of this study was to define the features of scapular morphology that are associated with changes in the critical shoulder angle (CSA) by developing the best predictive model for the CSA based on multiple potential explanatory variables, using a completely 3D assessment. METHODS: 3D meshes were created from CT DICOMs using InVesalius (Vers 3.1.1, RTI [Renato Archer Information Technology Centre], Brazil) and Meshmixer (3.4.35, Autodesk Inc., San Rafael, CA). The analysis included 17 potential angular, weighted linear and area measurements. The correlation of the explanatory variables with the CSA was investigated with the Pearson’s correlation coefficient. Using multivariable linear regression, the approach for predictive model-building was leave-one-out cross-validation and best subset selection. RESULTS: Fifty-three meshes were analysed. Glenoid inclination (GI) and coronal plane angulation of the acromion (CPAA) [Pearson’s r: 0.535; -0.502] correlated best with CSA. The best model (adjusted R-squared value 0.67) for CSA prediction contained 10 explanatory variables including glenoid, scapular spine and acromial factors. CPAA and GI were the most important based on their distribution, estimate of coefficients and loss in predictive power if removed. CONCLUSIONS: The relationship between scapular morphology and CSA is more complex than the concept of it being dictated solely by GI and acromial horizontal offset and includes glenoid, scapular spine and acromial factors of which CPAA and GI are most important. A further investigation in a closely defined cohort with rotator cuff tears is required before drawing any clinical conclusions about the role of surgical modification of scapular morphology. LEVEL OF EVIDENCE: Level 4 retrospective observational cohort study with no comparison group. |
format | Online Article Text |
id | pubmed-9685918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96859182022-11-25 A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics Smith, Geoffrey C. S. Geelan-Small, Peter Sawang, Michael BMC Musculoskelet Disord Research Article BACKGROUND: The purpose of this study was to define the features of scapular morphology that are associated with changes in the critical shoulder angle (CSA) by developing the best predictive model for the CSA based on multiple potential explanatory variables, using a completely 3D assessment. METHODS: 3D meshes were created from CT DICOMs using InVesalius (Vers 3.1.1, RTI [Renato Archer Information Technology Centre], Brazil) and Meshmixer (3.4.35, Autodesk Inc., San Rafael, CA). The analysis included 17 potential angular, weighted linear and area measurements. The correlation of the explanatory variables with the CSA was investigated with the Pearson’s correlation coefficient. Using multivariable linear regression, the approach for predictive model-building was leave-one-out cross-validation and best subset selection. RESULTS: Fifty-three meshes were analysed. Glenoid inclination (GI) and coronal plane angulation of the acromion (CPAA) [Pearson’s r: 0.535; -0.502] correlated best with CSA. The best model (adjusted R-squared value 0.67) for CSA prediction contained 10 explanatory variables including glenoid, scapular spine and acromial factors. CPAA and GI were the most important based on their distribution, estimate of coefficients and loss in predictive power if removed. CONCLUSIONS: The relationship between scapular morphology and CSA is more complex than the concept of it being dictated solely by GI and acromial horizontal offset and includes glenoid, scapular spine and acromial factors of which CPAA and GI are most important. A further investigation in a closely defined cohort with rotator cuff tears is required before drawing any clinical conclusions about the role of surgical modification of scapular morphology. LEVEL OF EVIDENCE: Level 4 retrospective observational cohort study with no comparison group. BioMed Central 2022-11-22 /pmc/articles/PMC9685918/ /pubmed/36419105 http://dx.doi.org/10.1186/s12891-022-05920-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Smith, Geoffrey C. S. Geelan-Small, Peter Sawang, Michael A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title | A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title_full | A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title_fullStr | A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title_full_unstemmed | A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title_short | A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
title_sort | predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685918/ https://www.ncbi.nlm.nih.gov/pubmed/36419105 http://dx.doi.org/10.1186/s12891-022-05920-7 |
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