Precision Management of Fludrocortisone-Related Hypertension Risk in Congenital Adrenal Hyperplasia: A Machine Learning Approach to Personalized Dosing

Congenital adrenal hyperplasia (CAH) is a rare inherited disorder requiring lifelong hormone replacement therapy. Excessive hormone replacement poses a significant risk for long-term complications, such as hypertension; however, quantitative approaches for optimizing dosing remain underdeveloped. This study aimed to identify factors associated with hypertension in patients with CAH and to develop a predictive model to support longitudinal fludrocortisone dose adjustment in pediatric patients who were already receiving mineralocorticoid replacement. We first employed generalized linear mixed models (GLMM) to evaluate the relationships among therapeutic agents, biochemical markers, and hypertension. Our results indicated a significant positive association between the dose of fludrocortisone (FC) and diastolic hypertension, whereas no such association was observed for the dose of hydrocortisone (HC). Using expert curated data, we subsequently constructed multiple predictive models, including CatBoost, XGBoost, and LightGBM, to enable individualized adjustment of FC dosage. All models were evaluated on an independent test set, with CatBoost, XGBoost, and LightGBM demonstrating comparably strong performance (R^2: 0.75 to 0.77). Subgroup analyses revealed that predictive accuracy was highest in children aged 0 to 2 years, where the top-performing model achieved a mean ideal prediction rate of 59.6%. This study not only confirms the significant link between FC dosing and hypertension in CAH patients but also provides a machine learning based decision support tool to assist individualized longitudinal dose adjustment. The model shows promise as a clinical decision-support instrument to facilitate personalized and precise management of CAH therapy.