hrp0084p1-83 | Growth Hormone | ESPE2015

Genetic Markers Contribute to the PREDICTION of Response to GH in Severe but not Mild GH Deficiency

Stevens Adam , Murray Philip , Wojcik Jerome , Raelson John , Koledova Ekaterina , Chatelain Pierre , Clayton Peter

Background: Single nucleotide polymorphisms (SNPs) associated with the response to GH therapy have previously been identified in growth hormone deficient (GHD) children in the PREDICT long-term follow-up (LTFU) study (NCT00699855).Objective and hypotheses: To assess the effect of GHD severity on the predictive value of genetic markers of growth response.Method: We used pre-pubertal GHD children (peak GH <10 μg/l) from the ...

hrp0084p2-418 | GH &amp; IGF | ESPE2015

Random Forest Classification Predicts Response to Recombinant GH in GH Deficient Children Using Baseline Clinical Parameters and Genetic Markers

Stevens Adam , Murray Philip , Wojcik Jerome , Raelson John , Koledova Ekaterina , Chatelain Pierre , Clayton Peter

Background: Prediction of response to recombinant GH (r-GH) is currently based on regression modelling. This approach generates a prediction equation which can be applied to data from an individual child. However this method can underestimate the effect of inter-dependent variables. Random forest classification (RFC) is an alternative prediction method based on decision trees that is not sensitive to the relationships between variables.Objective and hypo...