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Abstract
Background: The creation of an accurate growth prediction method for human stature at a stage of growth has been an interesting challenge in medical science and human biology.
Aim: The aim of this study was to develop a non-radiographic final stature prediction method that is applicable in the early pubertal growth period.
Subjects and methods: Randomly selected 12-year serial stature growth data for 400 Koreans were fitted with two nonlinear growth curves: Preece and Baines model 1 (PB1) and Jolicoeur–Pontier–Pernin–Sempe (JPPS) functions. Five biological parameters, including take-off (TO) related parameters, were derived by differentiation of the two curves, respectively. Those five variables were composed into a multiple linear regression equation for final stature prediction. In the cross-validation subjects, TO-related variables were estimated by linear interpolation from the partial growth data prior to estimation age, then incorporated into the prediction equation.
Results: The final stature prediction model had excellent validity and accuracy when applied to the cross-validation samples. Prediction accuracy increased according to increasing years after take-off.
Conclusions: This study suggests that a final stature prediction method using multiple regression analysis that includes biological parameters can predict stature growth with sufficient validity and accuracy. Incorporation of TO-related parameters allowed us to develop earlier growth evaluation and prediction methods compared with other previous methods.