Growth Hormone Treatment Response and Machine Learning-Based Prediction in Idiopathic GHD and ISS: Analysis of the Korean LG Growth Study.

objectiveIndividual responses to recombinant human growth hormone (rhGH) therapy vary widely among children with idiopathic growth hormone deficiency (iGHD) and idiopathic short stature (ISS), making accurate prediction of treatment outcomes clinically important. This study aimed to develop and compare machine learning (ML)-based and conventional statistical models to predict short-term growth response and mid-parental height (MPH) attainment following rhGH therapy in iGHD and ISS patients.

designRetrospective observational cohort study using a nationwide, real-world registry. PATIENTS: A total of 2215 children (1877 with iGHD and 338 with ISS) treated with rhGH were identified from the Korean LG Growth Study database. All included patients had at least 1 year of follow-up with available clinical data. MEASUREMENTS: Primary outcomes were 1- and 2-year changes in height SDS (ΔHSDS) and achievement of MPH SDS. Predictive models included multiple linear regression, logistic regression, Random Forest, eXtreme Gradient Boosting, and Elastic Net. Model performance was evaluated using R², error metrics and area under the receiver operating characteristic curve. Model interpretability was assessed using SHAP values.

resultsIn the iGHD group, ensemble ML models modestly outperformed linear regression for predicting 1-year ΔHSDS (R² ≈ 0.19 vs. 0.16), but predictive performance declined at 2 years across all models. Prediction of MPH attainment showed high specificity but very low sensitivity at 1 year, with no clear advantage of ML over logistic regression. In ISS patients, all models demonstrated poor predictive performance for both ΔHSDS and MPH attainment, reflecting substantial clinical heterogeneity.

conclusionsML approaches provided limited but clinically meaningful improvements in predicting short-term growth response in iGHD, while offering no clear benefit in ISS. These findings highlight both the potential and limitations of ML models based solely on routine clinical variables and underscore the need for integrating multimodal data to improve growth prediction, particularly in heterogeneous ISS populations.