The Impact of COVID-19 Regulations on Adherence to Recombinant Human Growth Hormone Therapy: Evidence from Real-World Data

Worldwide regulations during COVID-19 positively and negatively impacted self-management in paediatric patients with chronic medical conditions. We investigated the impact of regulations on adherence to recombinant human growth hormone (r-hGH) therapy in paediatric patients with growth disorders, using real-world adherence data extracted March...

Use of machine learning to identify patients at risk of sub‑optimal adherence: study based on real‑world data from 10,929 children using a connected auto‑injector device

Background. Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders. Methods. Adherence to r-hGH treatment was...

Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis

Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents,...

Persistence of Use in Children Receiving Growth Hormone Therapy

Background: Long-term persistence of use and starting at the earliest possible age are associated with attainment of near-normal adult or final height in children who are treated with growth hormone therapy. However, there are many factors associated with a lack of persistence. Our aim was to study persistence of use in children with growth...

High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use,...

A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy

Background: Suboptimal adherence to recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes. Being able to identify children who are at risk of suboptimal adherence in the near future, and take adequate measures to support adherence, may maximize clinical outcomes. Our aim was to develop a model based on data...