Development and validation of a nomogram to predict poor short-term response to recombinant human growth hormone treatment in children with growth disorders
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Published on: January 7, 2016
Y D Feng1, J Wang2,3, Z B Tao4
1Department of Pediatrics, The First Affiliated Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110000, Liaoning Province, China.
Insights
This study developed a clinical model to predict poor response to recombinant human growth hormone (rhGH) treatment in children with growth disorders, aiding physician decision-making for better therapeutic outcomes.
Area of Science:
- Pediatric Endocrinology
- Clinical Prediction Modeling
- Growth Hormone Therapy
Background:
- Recombinant human growth hormone (rhGH) treatment is crucial for pediatric growth disorders.
- Predicting therapeutic response is essential for optimizing treatment outcomes.
- A validated clinical model can improve patient management and resource allocation.
Purpose of the Study:
- To develop and validate a clinical predictive model for identifying children likely to have a poor response to rhGH treatment within the first year.
- To identify key predictors of rhGH treatment response in pediatric growth disorders.
- To provide a tool for physicians to aid in clinical decision-making.
Main Methods:
- Utilized data from 627 pediatric patients with growth disorders from The LG Growth Study cohort.
- Employed restricted cubic splines, LASSO regression, and multivariate logistic regression to identify predictors and build models.
- Assessed model accuracy and clinical utility using ROC curves, calibration curves, DCA, and CIC, with validation on a testing set.
Main Results:
- Developed two predictive models incorporating 8 baseline predictors and 1 post-treatment predictor (height SDS gain at 6 months).
- Constructed a nomogram based on the multivariate model, demonstrating good discrimination and fit in both training and testing sets.
- Decision curve analysis and clinical impact curves indicated good clinical usability.
Conclusions:
- The developed clinical predictive model is effective in predicting poor short-term response to rhGH treatment in pediatric patients.
- The model can assist physicians in making informed clinical decisions regarding rhGH therapy.
- This tool has the potential to enhance the management of children with growth disorders receiving rhGH treatment.
Purpose:
The purpose of this study was to develop and validate a clinical predictive model for predicting the likelihood of a poor therapeutic response during the first year of recombinant human growth hormone (rhGH) treatment in children with growth disorders.
Methods:
A total of 627 pediatric patients with growth disorders (GHD, ISS, TS, SGA) from The LG Growth Study cohort were evaluated. Restricted cubic splines (RCS) were utilized to investigate the association between predictors and the risk of poor rhGH response. Variables were selected using LASSO regression, and multivariate logistics regression models were established. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the predictive model's accuracy and clinical value. The predictive accuracy of the model was validated on the testing set.
Results:
Two predictive models containing 8 baseline predictors (diagnosis, age, height SDS, bone age minus chronological age, rhGH dosage, distance from mid-parental height in SDS, weight SDS, IGF-1 SDS) and 1 post-treatment predictor (height SDS gain at 6 months) were constructed by multivariate logistic regression analyses. The nomogram was built based on the multivariate predictive model and showed good discrimination and model fit effects in both the training set and the testing set. DCA and CIC analyses presented good clinical usability.
Conclusion:
The clinical predictive model for predicting the probability of poor short-term response of rhGH treatment in pediatric patients with growth disorders is useful and can assist physicians in making clinical decisions.
