Predictive anthropometric measurements for humeral head curvature

Background tion of size, shape, and curvature of the humeral head is important for shoulder replacement procedures and allograft transplantation, especially as we try to recreate normal anatomy. The purpose of this study was to investigate the value of various anthropometric measurements for predicting humeral head curvature. als and methods ric humeri were scanned with a 3-dimensional laser scanner. Length of the humerus, epicondylar breadth, and humeral head curvature were determined using data from the scans. A linear regression was performed for the length of the humerus, epicondylar breadth, gender, age, height, and weight. A stepwise linear regression with forward and backward substitution (α = 0.15) was performed for the most predictive variables from the initial linear regression. An equation for the prediction of humeral head radius of curvature was generated using this data. s st predictive factors (R2 > 0.5) were epicondylar breadth, height, sex, and humeral length. These 4 factors were included in a forward and backward stepwise regression. The resulting equation had an R2 value of 0.812. sion predicted measurements evaluated, patient height, maximum humeral length, epicondylar breadth, and gender were most correlated with humeral head curvature. Including these 4 factors in a linear regression model increased the R2 value to 0.812. If only a single measurement can be used to size the humeral head curvature, patient height will give the same accuracy as epicondylar breadth and can more easily be obtained. A patientʹs height can help accurately predict the patientʹs humeral head anatomy.