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Updated: Aug 13, 2025
Comparative Analysis of Human Growth Hormone in Serum Using SPRi, Nano-SPRi and ELISA Assays
Published on: January 7, 2016
Comparison of two prediction models in a clinical setting to predict growth in prepubertal children on recombinant
Helena-Jamin Ly1, Anders Lindberg2, Hans Fors1
1Göteborg Pediatric Growth Research Center, Institute of Clinical Sciences, Department of Pediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Västra Götalands Region, Queen Silvia Children's Hospital, Vitaminvägen 21, 416 50 Gothenburg, Sweden.
Insights
Two growth hormone prediction models, KIGS and Gothenburg, demonstrated equivalent accuracy in predicting treatment response in children. Both models are precise and can be used interchangeably based on clinical accessibility.
Area of Science:
- Pediatric Endocrinology
- Growth Hormone Therapy
- Biostatistics
Background:
- Accurate prediction of growth response to recombinant human growth hormone (rhGH) is crucial for managing growth disorders and identifying GH deficiency.
- Existing prediction models aid in treatment decisions, but their comparative performance requires evaluation.
Purpose of the Study:
- To compare the predictive accuracy of the KIGS (Pfizer International Growth Study) and Gothenburg prediction models for growth response in children receiving rhGH therapy.
- To assess the clinical utility and equivalence of these two widely used models.
Main Methods:
- A retrospective study included 123 prepubertal children treated with rhGH.
- Growth predictions from the KIGS and Gothenburg models were compared against observed height outcomes after one year of treatment.
- Correlation analyses and residual analysis were used to evaluate model performance.
Main Results:
- Both the Gothenburg and KIGS models showed strong correlations with observed growth (r=0.990 and r=0.991, respectively).
- The models exhibited high precision, with minimal studentized residuals (0.10 for Gothenburg, 0.03 for KIGS).
- No significant difference in predictive accuracy was found between the two models.
Conclusions:
- The Gothenburg and KIGS prediction models are equivalent in predicting growth response in a clinical cohort of children on rhGH therapy.
- Clinicians can confidently use either model, selecting based on accessibility and ease of implementation.
- These findings support the reliable application of both models in pediatric endocrinology practice.
Objective:
Prediction models that calculate the growth response in children on recombinant growth hormone (GH) have shown to be helpful tools in deciding who should start treatment, as identifying GH deficiency can be a challenge. The aim of the study is to compare two prediction models; the KIGS (Pfizer International Growth Study) prediction models which are more accessible and the Gothenburg model which has previously been clinically validated.
Design:
All prepubertal patients who commenced GH treatment at Queen Silvia Children's Hospital in Gothenburg during a 13-year-period were candidates for the study. Children were excluded if suspected syndrome, malignant disease, chronic disease, or poor adherence to treatment were found. The KIGS model and the Gothenburg model were used to make predictions. Data was obtained from medical charts for the period from birth to the end of the first year of treatment. The predicted height outcome was compared against observed.
Results:
The study included 123 prepubertal children (76 males). The average age at treatment start and standard deviation (SD) was 5.7 (1.8) years. Correlation analyses were performed between predicted growth by both the Gothenburg and KIGS models versus the first year observed growth response showing strong correlations of r = 0.990 and r = 0.991 respectively with studentized residuals of 0.10 (0.81) for the Gothenburg model and 0.03 (0.96) for the KIGS model.
Conclusion:
We found that both the Gothenburg model and the KIGS model are equivalent when applying to our clinical cohort. Both models are very precise, hence it is encouraged to use either based on accessibility for the clinic.
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