Predicting growth in response to growth hormone treatment

The use of growth hormone (GH) to treat children who have disturbances of growth is complicated by variability both within and across diagnostic groups, and at the start of and throughout treatment. Growth prediction models are important tools in the effort to account for these sources of variability and tailor GH treatment to each patient’s needs. This review considers the methodological approach taken to the development of models from data in large databases such as the Pfizer International Growth Database (KIGS); it also assesses the limitations of these models and their data sources, and the potential for improvements. While all aspects of model development bear continued scrutiny and improvement, the incorporation of more predictors is key if treatment outcomes are to be optimized in terms of efficacy, safety and cost.