Height Estimation from Vertebral Parameters on Routine Computed Tomography in a Contemporary Elderly Australian Population: A Validation of Existing Regression Models

Abstract

The aim of this study is to compare previously published height estimation formulae in a contemporary Australian population using vertebral measurements readily available on abdominal CT. Retrospective analysis of patients undergoing a planning CT prior to transcatheter aortic valve implantation in a 12-month period was conducted; 96 participants were included in the analysis from a total of 137, with 41 excluded due to incomplete data. Seven vertebral measurements were taken from the CT images and height estimates were made for each participant using multiple regression equations from the published literature. Paired sample t-tests were used to compare actual height to estimated height. Many of the models failed to accurately predict patient height in this cohort, with only three equations for each sex resulting in a predicted height that was not statistically significantly different to actual height. The most accurate model in female participants was based on posterior sacral length and resulted in a mean difference between an actual and calculated height of 0.7 cm (±7.4) (p = 0.520). The most accurate model in male participants was based on anterior sacrococcygeal length and resulted in a mean difference of −0.6 ± 6.9 cm (p = 0.544). Height estimation formulae can be used to predict patient height from common vertebral parameters on readily available CT data. This is important for the calculation of anthropometric measures for a variety of uses in clinical medicine. However, more work is needed to generate accurate prediction models for specific populations.

Keywords

• computed tomography
• height estimation
• artificial intelligence