Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models.

Published

Journal Article

Correction of pathologic glenoid retroversion improves gleonhumeral mechanics and reduces glenoid component wear after total shoulder arthroplasty. Determining the amount of correction necessary can be difficult because of the wide range of normal glenoid version. We hypothesize that normal glenoid version can be predicted in a pathologic shoulder based on conserved relationships between the anterior glenoid wall, Resch angle, and the internal structures of the glenoid vault.Three-dimensional (3-D) computer tomography (CT) scan-based measurements of the anterior glenoid wall angle (AGWA), Resch angle (RA), and glenoid version were made in 58 scapulae from the Haeman-Todd Osteological Collection (Museum of Natural History in Cleveland, OH) and 19 paired scapulae from patients with unilateral osteoarthritis. Linear regression equations derived from the AGWA and RA and from a computer-generated vault model were used to predict native (nonpathologic) glenoid version as defined by the 19 nonpathologic scapula.Linear regression equations based on the measured AGWA or RA, as well as the glenoid vault model in the 19 pathologic scapulae, were able to accurately predict native glenoid version in the contralateral nonpathologic shoulder.This study demonstrates the ability to take 3-D CT scan-based measurements in a scapula with pathologic glenoid retroversion and predict the native (nonpathologic) glenoid version in the contralateral shoulder by using linear regression equations or a computer generated vault model. Such tools might assist in preoperative planning and intraoperative decision making to allow correction of pathologic glenoid retroversion.

Full Text

Duke Authors

Cited Authors

  • Ganapathi, A; McCarron, JA; Chen, X; Iannotti, JP

Published Date

  • March 2011

Published In

Volume / Issue

  • 20 / 2

Start / End Page

  • 234 - 244

PubMed ID

  • 20933439

Pubmed Central ID

  • 20933439

Electronic International Standard Serial Number (EISSN)

  • 1532-6500

International Standard Serial Number (ISSN)

  • 1058-2746

Digital Object Identifier (DOI)

  • 10.1016/j.jse.2010.05.024

Language

  • eng