A Novel Prediction Model for Determining Coronal Length of Calcaneus Using CT: A Guide for Surgery of Calcaneal Fracture.

Journal Article (Journal Article)

The objective of this study was to determine the anatomical relationship between the calcaneus and its neighboring bones. Furthermore we tested a prediction model that enables to estimate safe screw length during the surgery of calcaneus fractures. A total of 169 feet were used for the study based on CT scans. We measured two horizontal and two parallel lines. The coronal length of the cuboid bone (CL) was a horizontal line anterior to the calcaneocuboidal joint, and W1 of calcaneus was a horizontal line posterior to the articular surface of the calcaneocuboidal joint. The subtalar articular length (STA) was a parallel line above the talocalcaneal joint, and W2 of calcaneus was a parallel line below to the talocalcaneal joint. Relationship of each measurement was determined through correlation analysis. A prediction model was developed based on observed correlations and the quality analyzed and validated. The CL and W1 had a significant positive correlation (r = 0.899, p < .001). The STA and W2 also had a significant positive correlation (r = 0.939, p < .001). Based on these correlations, the prediction model was made. In the quality analysis, the values of concordance correlation coefficient (CCC) for W1 and W2 were 0.894, and 0.937 respectively. In the validation analysis, the values of CCC for W1, W2 were 0.79, and 0.8, respectively. This study made it possible to predict the anatomical reference point using preoperative coronal length of the calcaneus to guide safety margin of screw length, and thereby to prevent the iatrogenic injuries on medial neurovascular structures of the calcaneus.

Full Text

Duke Authors

Cited Authors

  • Chun, D-I; Cho, J; Lee, JS; Kang, EM; Kim, J; Yi, Y; Park, S; Kim, JH; Won, SH

Published Date

  • July 2021

Published In

Volume / Issue

  • 60 / 4

Start / End Page

  • 724 - 728

PubMed ID

  • 33773921

Electronic International Standard Serial Number (EISSN)

  • 1542-2224

Digital Object Identifier (DOI)

  • 10.1053/j.jfas.2021.01.008

Language

  • eng

Conference Location

  • United States