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Development and Validation of a Clinical Prediction Model for Growth Hormone Deficiency in Children with Short
Mali Li1, Chao Liu1, Yuan Yang1
1Department of Endocrinology, Genetics and Metabolism, Xi'an Children's Hospital, Xi'an, Shaanxi, People's Republic of China.
Insights
This study developed a prediction model to accurately identify growth hormone deficiency (GHD) in children experiencing short stature. The model uses clinical and lab data for reliable screening, improving diagnostic accuracy.
Area of Science:
- Pediatrics
- Endocrinology
- Medical Diagnostics
Background:
- Short stature in children can stem from various congenital and acquired conditions.
- Accurate diagnosis is crucial for effective treatment of underlying causes like growth hormone deficiency (GHD).
Purpose of the Study:
- To develop and validate a predictive model for identifying GHD in pediatric patients with short stature.
- The model aims to utilize readily available clinical and laboratory parameters for efficient screening.
Main Methods:
- A retrospective observational study involving 1120 children with short stature.
- Data were split into derivation (70%) and validation sets for model construction and testing.
- A multivariate logistic regression model was built using clinical relevance and statistical significance.
Main Results:
- The final model incorporated age, delayed bone age, IGF-1 SDS, and IGF-1/IGFBP-3 ratio.
- The model demonstrated high discriminative ability (AUC > 0.95) and accuracy (sensitivity/specificity > 0.85) in both sets.
- Reliable calibration was observed, indicating consistent performance across different patient groups.
Conclusions:
- A validated prediction model for GHD screening in children with short stature has been successfully developed.
- This tool can aid clinicians in the accurate and efficient identification of GHD.
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.
