Development and validation of a nomogram to predict poor short-term response to recombinant human growth hormone treatment in children with growth disorders.

Abstract

The purpose of this study was to develop and validate a clinical predictive model for predicting the likelihood of a poor therapeutic response during the first year of recombinant human growth hormone (rhGH) treatment in children with growth disorders. A total of 627 pediatric patients with growth disorders (GHD, ISS, TS, SGA) from The LG Growth Study cohort were evaluated. Restricted cubic splines (RCS) were utilized to investigate the association between predictors and the risk of poor rhGH response. Variables were selected using LASSO regression, and multivariate logistics regression models were established. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the predictive model's accuracy and clinical value. The predictive accuracy of the model was validated on the testing set. Two predictive models containing 8 baseline predictors (diagnosis, age, height SDS, bone age minus chronological age, rhGH dosage, distance from mid-parental height in SDS, weight SDS, IGF-1 SDS) and 1 post-treatment predictor (height SDS gain at 6months) were constructed by multivariate logistic regression analyses. The nomogram was built based on the multivariate predictive model and showed good discrimination and model fit effects in both the training set and the testing set. DCA and CIC analyses presented good clinical usability. The clinical predictive model for predicting the probability of poor short-term response of rhGH treatment in pediatric patients with growth disorders is useful and can assist physicians in making clinical decisions.