Development and Validation of a Clinical Prediction Model for Growth Hormone Deficiency in Children with Short Stature: A Retrospective Study in China.

BACKGROUND

A multitude of congenital and acquired conditions can result in short stature, each with distinctive clinical presentations and treatment options. We aimed to develop and validate a prediction model to identify GHD among children with short stature using clinical and laboratory parameters.

METHODS

This retrospective observational study included 1120 children with short stature from a hospital in China. The data were randomly split into a derivation set and a validation set. Features were selected based on clinical relevance and statistical significance to construct a multivariate logistic regression model in the derivation set. Discrimination, calibration, and prediction accuracy were evaluated on both sets.

RESULTS

Of the 1120 children, 278 (25%) were diagnosed with GHD, 694 (62%) were male, and the mean age was 6.97 ± 2.97 years. The derivation set comprises 785 (70%) children. The model incorporates four predictors: age (OR=0.761; 95% CI 0.660, 0.873), delayed bone age (OR=1.841; 95% CI 1.365, 2.537), IGF-1 SDS (OR=0.148; 95% CI 0.095, 0.220), and IGF-1/IGFBP-3 ratio (OR=0.901; 95% CI 0.870, 0.930). The model exhibits good discriminative ability, with an AUC of 0.952 (0.937, 0.967) in the derivation set and 0.950 (0.927, 0.973) in the validation set. Furthermore, it shows high accuracy with sensitivity and specificity of 0.895 in the derivation set, which was 0.946 and 0.851 in the validation set. The model also demonstrates reliable calibration.

CONCLUSION

We have developed a prediction model for accurate screening of GHD in children with short stature.