ArticleFrontiers in medicine2026
Population characteristics of children with short stature and construction of a predictive model for growth hormone treatment efficacy.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: This study aims to analyze clinical characteristics and treatment patterns among children with short stature, and to develop a predictive model for growth hormone treatment efficacy for clinical reference. Methods: This was a retrospective cohort study including 400 children diagnosed with short stature. Patients were divided into three groups: growth hormone intervention group, nutritional support group, and untreated (watchful waiting) group, based on parental choice after clinical evaluation. Annualized growth velocity was compared between the growth hormone intervention and nutritional support groups. Within the growth hormone intervention group, 70% of subjects were randomly assigned to a training set and 30% to an internal validation set. Multivariate logistic regression was used to identify factors associated with growth hormone treatment efficacy. A nomogram prediction model was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Lemeshow test, and decision curve analysis. Results: A total of 293 children were included after excluding 107 cases (76 with missing data, 11 with skeletal dysplasia, 16 with hypothyroidism, 4 with chromosomal abnormalities). The growth hormone intervention group ( Conclusion: This study confirms that growth hormone treatment improves growth velocity in children with short stature. A predictive model was established and internally validated. Since treatment duration was the significant predictor and is observed during treatment, the model is more suitable for intra-treatment efficacy evaluation rather than pure pre-treatment decision-making. This model has the potential to support clinicians with individualized dynamic efficacy assessment and subsequent patient management, facilitating the optimization of follow-up treatment regimens.
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