New paradigms for growth hormone treatment in the 21st century: prediction models

Inconsistent and sometimes disappointing final height outcomes in studies in which exogenous growth hormone (GH) was given to children with short stature resulting from various causes have led to attempts to determine the factors that influence responsiveness to GH. Many such factors have been identified, including the genetically determined height potential of the child, the height deficit, current and perinatal auxological and biological factors, and, importantly, GH treatment modalities. These factors vary depending on the cause of the growth failure and during the course of childhood and treatment with GH. Data from well-defined large cohorts can be entered into multiple regression analyses to derive algorithms describing the variation in growth response during a defined period and the influence on this of various factors. Pharmacoepidemiologic surveys have been particularly useful in this regard. Algorithms with low-error SD values, which have included the dose of GH as a variable, can be used to predict the response to a putative GH dose in a similar cohort or in an individual over an equivalent period. A sequential series of such algorithms can be integrated to form a predictive model. Such a model can be used for planning a course of treatment, but the patient data required for entry into the model should be easily obtainable if the model is to have widespread utility. The development of such models will allow GH treatment to be better individualized and optimized for growth and cost outcomes. Growth prediction models will facilitate realistic expectations and will permit stepwise goals to be set and monitored.