Development and Validation of Prediction Equations for Spinal Curve Angles Based on Skin Surface Measurements

The purpose of this study was to develop, assess the reliability of, and validate prediction equations that estimate the sagittal curves of the spine from the skin surface.

Forty digital panoramic radiographs were used to develop the prediction equation, and 59 radiographs were used to assess reliability and validate the equations. For evaluation of the thoracic and lumbar curves, anatomical reference points were marked on the vertebral body, spinous process, and skin surface at the C6, C7, T2, T4, T6, T8, T10, T12, L2, L4, and S2 vertebrae. Three third-degree polynomials were obtained, estimated with the least squares method: inner curves from the centroid of the vertebral bodies and from the apex of the spinous processes and external curve from the skin surface. The magnitude of the curves of each region was estimated based on the angle between tangent lines at several vertebral levels. Prediction equations were obtained (simple linear regression) for the vertebral levels that had the best correlation between the inner and surface curves. The validation of the prediction equations was confirmed using Pearson's correlation (r), Student t test, and root mean square error. The reliability of the method was confirmed using the intraclass correlation coefficient, standard error of measurement, and minimal detectable change (α = 0.05).

The best correlations were obtained between the T4-T12 (thoracic) and T10-S2 (lumbar) levels (r > 0.85). For the intrarater and interrater reliability, the correlation was higher than 0.965 and higher than 0.896, respectively. There was a significant and strong correlation between estimated and actual values for the thoracic and lumbar curves, which was confirmed by the t-test results and by the root mean square error inferior to 1°.

Prediction equations can precisely and accurately estimate the angles of the internal sagittal curves of the spine from the skin surface.