Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method

Curve matching may be used to predict growth outcomes using data of patients whose growth curves resemble those of a new patient with growth hormone deficiency (GHD) and those born small for gestational age (SGA). We aimed to investigate the validity of curve matching to predict growth in patients with GHD and those born SGA receiving...

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...

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...

Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy

The problem of consistent therapy adherence is a current challenge for health informatics, and its solution can increase the success rate of treatments. Here we show a methodology to predict, at individual-level, future therapy adherence for patients receiving daily injections of growth hormone (GH) therapy for GH deficiency. Our proposed model...