Prediction of long-term response to recombinant human growth hormone in Turner syndrome: development and validation of mathematical models. KIGS International Board. Kabi International Growth Study.

Abstract

It has become common practice to apply GH treatment in short Turner syndrome patients with the objective of promoting growth. The variability in response and the high costs of this treatment demand the individualization and optimization of therapy. Based on 686 prepubertal Turner patients from the Kabi International Growth Study (KIGS; Pharmacia & Upjohn, Inc. International Growth Database), we undertook a multiple regression analysis of height velocity (centimeters per yr) by using various parameters of potential relevance. Derived prediction models for the first 4 yr of GH treatment were validated with 76 additional KIGS patients and 81 patients from Tuebingen, Germany. Among the 6 predictors identified, the most influential variable for first year growth response was the natural log (ln) of the weekly GH dose. The first year growth response was also correlated with age and distance between height and target height (SD score; both negative) and body weight SD, number of GH injections per week, and oxandrolone treatment given additionally (positive). The first year model explains 46% of the variability, with 1 SD of 1.26 cm. For the second to fourth years, 5 predictors were identified: height velocity during previous years, weekly GH dose (ln), weight SD, oxandrolone therapy (all positive), and age (negative). These models explained 32%, 29%, and 30% of the variability, respectively, with SD scores of 1.1, 1.0, and 1.0 cm, respectively. When the models were applied to the other cohorts, no significant difference was noted between observed and predicted responses. Although the parameters used in our models do not entirely explain the variability in the growth response in Turner syndrome, the parameters themselves were clinically relevant to our present understanding and proved to be of high precision. Some of the tested markers, such as karyotype, do not contribute to the growth response. These variables make the models practical and suitable for planning beneficial and cost-effective therapy.