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 appropriate. The challenge for endocrinologists is how to optimise GH treatment with respect to growth, safety and cost for each child.
iGRO™ is a web-based medical device that supports personalised growth evaluations to help you deliver personalised treatment that optimises response to growth hormone (GH) treatment. This allows you to evaluate a child’s potential to respond to GH treatment before it is initiated. This includes expected height velocities (mean and range) from the start of treatment based on a child’s unique combination of baseline characteristics and the given GH dose.
Supported by iGRO™, you can give patients and their families realistic expectations of short and long-term growth outcomes. And, monitor the effect of GH treatment by comparing a child’s predicted and actual growth responses to GH each year.
To do this, iGRO™ requires standard data that is routinely collected during clinic visits.
iGRO™ calculates growth predictions using data typically collected during routine clinical practice
Using iGRO™ you can evaluate
● Index of responsiveness (IoR) after the first 12 months of GH treatment
● Distance to target height vs. age
● GH dose relative to age
● Maximum GH peak or weight standard deviation score (SDS) relative to age
HV: height velocity, MPH: mid parental-height, SDS: standard deviation score
Evaluations of growth response can be used to guide treatment decisions.
iGRO™ provides the information you need to identify discrepancies between predictions and actual response. You can then investigate causes such as non-adherence, where additional support can be offered, and the presence of additional diseases, which may have therapeutic consequences.
1 - Kaspers S et al Implications of a data-driven approach to treatment with growth hormone in children with growth hormone deficiency and Turner syndrome. Appl Health Econ Health Policy 2013;11:237–49.