Methodology of algorithm development

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.
iGRO™ applies evidence-based, validated and peer-reviewed growth prediction algorithms to provide realistic and personalised growth targets, to help improve growth outcomes and optimise treatment.

These algorithms have been developed using the wealth of real-world clinical data from separate cohorts of patients in KIGS (the Pfizer International Growth Database1.

This international database contains growth data collected over a 25-year period from over 83,000 children who have received growth hormone treatment from 52 countries. These algorithms enable you to make a more accurate prediction, based on the gender, age and condition of a child; and monitor their response to treatment. Using this information you can identify any issues, confirm diagnoses and guide further treatment — offering personalised growth hormone treatment that is based on your patient’s needs.

 

 

1 - Ranke MB et al. Towards optimal treatment with growth hormone in short children and adolescents: evidence and theses. Horm Res Paediatr 2013; 79:51–67