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

Publication ,  Journal Article
Ganapathi, A; McCarron, JA; Chen, X; Iannotti, JP
Published in: Journal of shoulder and elbow surgery
March 2011

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.

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Published In

Journal of shoulder and elbow surgery

DOI

EISSN

1532-6500

ISSN

1058-2746

Publication Date

March 2011

Volume

20

Issue

2

Start / End Page

234 / 244

Related Subject Headings

  • Tomography, X-Ray Computed
  • Shoulder Joint
  • Scapula
  • Osteoarthritis
  • Orthopedics
  • Models, Anatomic
  • Linear Models
  • Imaging, Three-Dimensional
  • Humans
  • Four-Dimensional Computed Tomography
 

Citation

APA
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ICMJE
MLA
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Ganapathi, A., McCarron, J. A., Chen, X., & Iannotti, J. P. (2011). Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models. Journal of Shoulder and Elbow Surgery, 20(2), 234–244. https://doi.org/10.1016/j.jse.2010.05.024
Ganapathi, Asvin, Jesse A. McCarron, Xi Chen, and Joseph P. Iannotti. “Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models.Journal of Shoulder and Elbow Surgery 20, no. 2 (March 2011): 234–44. https://doi.org/10.1016/j.jse.2010.05.024.
Ganapathi A, McCarron JA, Chen X, Iannotti JP. Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models. Journal of shoulder and elbow surgery. 2011 Mar;20(2):234–44.
Ganapathi, Asvin, et al. “Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models.Journal of Shoulder and Elbow Surgery, vol. 20, no. 2, Mar. 2011, pp. 234–44. Epmc, doi:10.1016/j.jse.2010.05.024.
Ganapathi A, McCarron JA, Chen X, Iannotti JP. Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models. Journal of shoulder and elbow surgery. 2011 Mar;20(2):234–244.
Journal cover image

Published In

Journal of shoulder and elbow surgery

DOI

EISSN

1532-6500

ISSN

1058-2746

Publication Date

March 2011

Volume

20

Issue

2

Start / End Page

234 / 244

Related Subject Headings

  • Tomography, X-Ray Computed
  • Shoulder Joint
  • Scapula
  • Osteoarthritis
  • Orthopedics
  • Models, Anatomic
  • Linear Models
  • Imaging, Three-Dimensional
  • Humans
  • Four-Dimensional Computed Tomography