Adult height predictionmodels

Abstract

We review seven methods for adult height prediction (AHP) based on bone age, ranging from the Bayley-Pinneau method, published in 1952, to the BoneXpert method, published in 2009. These models are based on four different methods for bone age assessment including Greulich-Pyle, Tanner-Whitehouse, Fels, and the automated BoneXpert method. The aim of this chapter is to convey an understanding of the various parameters which contribute to AHP and how to best incorporate them into the AHP methods. The starting point is the Bayley-Pinneau method which predicts the fraction of adult height achieved from the bone age. Children with advanced bone age (early maturers) tend to have a stronger growth spurt, and late maturers have a weaker growth spurt. Accordingly, Bayley and Pinneau provided special AHP tables for early, average, and late maturers. The other five AHP methods reviewed are the three variants of the Tanner-Whitehouse method, TW Mark I (1975), TW Mark II (1983), and TW3 (2001), and the RWT methods of 1975 and 1993. They all model the expected adult height of children at each age using a linear model of height and bone age, and for the RWT models, also by using terms with midparental height and body weight. The main shortcoming of these models is that the linear bone age dependence is unable to describe children with constitutional delay of growth and puberty or precocious puberty. The recently developed automated BoneXpert method improves the Bayley-Pinneau method by modelling the growth potential (the fraction of adult height left to grow) as a non-linear function of two variables, bone age and bone age delay. The BoneXpert AHP method was based on the original images from the First Zürich Longitudinal Study and was subsequently validated on the more recent Third Zürich Longitudinal Study of 198 Swiss children. An additional validation study on 164 Danish children is also presented. The main advantage of the BoneXpert method is that it is based on an automated bone age which removes rater variability.