The Concept of Growth Prediction

Responses to growth hormone (GH) treatment vary widely. There is a broad range of sensitivity — and responsiveness — to GH, so a ‘one size fits all’ approach to GH treatment is no longer appropriate1-2.
The challenge facing endocrinologists is to deliver a personal approach that improves growth outcomes, safety and cost of treatment for each child.

iGRO™ is a web-based medical device that helps to deliver individualised GH treatment as soon as treatment starts: applying validated growth prediction algorithms developed using the wealth of real-world clinical data in KIGS (the Pfizer International Growth Database)3.

This enables you to evaluate a child’s potential to respond to GH treatment before it is initiated and the intrinsic potential of an individual to grow in response to GH, independent of GH dose4.

growth prediction


iGRO™ also helps to monitor the effect of GH treatment by comparing a child’s predicted and actual growth responses to GH each year: supporting evidence-based individualisation of GH therapy and helping patients and their families understand their growth potential.

Having a realistic expectation of the short and long-term effects of GH therapy is one of the most important factors for ensuring adherence to treatment.

iGRO™ prediction algorithms can explain up to 70% of variability in growth responses:5-8

  • 30–70% for children with IGHD
  • 30–68% for girls with Turner syndrome
  • 30–52% for short children born SGA



1 - Wit JM, Ranke MB, Albertsson-Wikland K et al. Personalized approach to growth hormone treatment: clinical use of growth prediction models. Hormone research in paediatrics (2013);79:257–70.

2 - Kaspers S, Ranke MB, Han D et al. Implications of a datadriven approach to treatment with growth hormone in children with growth hormone deficiency and Turner syndrome. Appl Health Econ Health Policy (2013);11:237–49.

3 - Loftus J, Lindberg A, Aydin F, et al. Journal of Pediatric Endocrinology and Metabolism (2017);30:1019–1026

5 - Ranke MB., et al. Derivation and validation of a mathematical model for predicting the response to exogenous recombinant human growth hormone (GH) in prepubertal children with idiopathic GH deficiency. The Journal of Clinical Endocrinology and Metabolism (1999):1174–83.

6 - Ranke MB., et al. The mathematical model for total pubertal growth in idiopathic growth hormone (GH) deficiency suggests a moderate role of GH dose. The Journal of Clinical Endocrinology and Metabolism (2003):38.

7 - Ranke MB and Lindberg A. Prediction models for short children born short for gestational age and idiopathic short stature: KIGS analysis and review. BMC medical informatics and decision making (2011):423–32.

8 - Ranke MB., et al. Prediction of response to growth hormone treatment in short children born small for gestational age: Analysis of data from KIGS. Journal of Clinical Endocrinology and Metabolism (2003):125–31