iGRO™ is Pfizer’s first multi-lingual software medical device. It is a web-based medical device that complies with all relevant European and local data protection regulations. It makes it easy to apply these algorithms in clinical practice — enabling physicians to predict how much a child may grow in the first and subsequent years of GH therapy.
It is designed to support you in calculating growth predictions for a range of pre-pubertal and pubertal patients, and provides evidence-based guidance and justification for GH treatment decisions9.
iGRO™ can be used to calculate growth predictions for a range of patients.
● Predictions for the first to 8th pre-pubertal year of treatment
● Subsequent pre-pubertal years
● Total pubertal growth.
For girls with Turner syndrome (TS)5-8:
● First to 7th pre-pubertal year of growth hormone treatment
● Total pubertal growth.
iGRO™ requires only standard data that is routinely collected during clinic visits:
● Birth date
● Primary diagnosis
● Birth weight
● Parents’ heights
● Treatment start date
● GH dose
iGRO™ prediction algorithms can explain up to 70% of variability in growth responses1,2,4-6:
● 30–70% for children with IGHD
● 30–68% for girls with Turner syndrome
● 30–52% for short children born SGA
iGRO™ gives you an assessment of the patient’s innate capacity to grow in response to GH treatment by calculating the index of responsiveness (IoR) at the end of the first year of treatment9.
At the start of treatment, iGRO aids assessment of a child’s potential to respond to GH, based on his or her baseline characteristics.
Response can be monitored and optimised during the first year of treatment, using the iGRO™ growth chart where yearly growth predictions are displayed clearly alongside the child’s growth curve before and during treatment. This enables physicians to monitor the effect of GH treatment by comparing a child’s predicted and actual growth responses to GH each year.
After the first year of treatment, iGRO™ indicates the patient’s capacity to respond by calculating the index of responsiveness (IoR): a valuable parameter that determines long-term outcomes.
1 - 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.
2 - Ranke MB., et al. Accurate long-term prediction of height during the first four years of growth hormone treatment in prepubertal children with growth hormone deficiency or Turner Syndrome. Hormone research in paediatrics (2012):8–17.
3 - Ranke MB., et al. Increased response but lower responsiveness to growth hormone (GH) in very young children (aged 0-3 years) with idiopathic GH deficiency: analysis of data from KIGS. Journal of Clinical Endocrinology and Metabolism (2005):1966–71.
4 - 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.
5 - 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.
6 - 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.
7 - Ranke MB., et al. Observed and predicted total pubertal growth during treatment with growth hormone in adolescents with idiopathic growth hormone deficiency, Turner Syndrome, short stature, born small for gestational age and idiopathic short stature: KIGS analysis and review. Hormone research in paediatrics (2011):423–32.
8 - Ranke MB., et al. Prediction of long-term response to recombinant human growth hormone in Turner Syndrome: development and validation of mathematical models. Journal of Clinical Endocrinology and Metabolism (2000):4212–18.
9 - Loftus J, Lindberg A, Aydin F, et al. Journal of Pediatric Endocrinology and Metabolism (2017):30:1019–1026.