Pre-treatment growth and IGF-I deficiency as main predictors of response to growth hormone therapy in neural models

• Published in: Endocrine Connections 2018-01, Vol.7 (1), p.239-249
• Main Authors: Smyczyńska, Urszula, Smyczyńska, Joanna, Hilczer, Maciej, Stawerska, Renata, Tadeusiewicz, Ryszard, Lewiński, Andrzej
• Format: Article
• Language: English
• Subjects: artificial neural networks; growth hormone deficiency; growth hormone therapy; insulin-like growth factor-I; prediction models
• ISSN: 2049-3614; 2049-3614
• DOI: 10.1530/EC-17-0277

Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique – artificial neural networks – to analyse this problem. This new methodology, contrary to previous ones, allows detection of both linear and non-linear dependencies without assuming their character a priori. The aims of this work included: development of models predicting separately growth during 1st year of treatment and final height as well as identification of important predictors and in-depth analysis of their influence on treatment’s effectiveness. The models were derived on the basis of clinical data of 272 patients treated for at least 1 year, 133 of whom have already attained final height. Starting from models containing 17 and 20 potential predictors, respectively for 1st year and final height model, we were able to reduce their number to 9 and 10. Basing on the final models, IGF-I concentration and earlier growth were indicated as belonging to most important predictors of response to GH therapy, while results of GH secretion tests were automatically excluded as insignificant. Moreover, majority of the dependencies were observed to be non-linear, thus using neural networks seems to be reasonable approach despite it being more complex than previously applied methods.