Connected health for growth hormone treatment research and clinical practice: learnings from different sources of real-world evidence (RWE)-large electronically collected datasets, surveillance studies and individual patients' cases

The adult growth hormone multicentric retrospective observational study: a 24-month Italian experience of adherence monitoring via Easypod™ of recombinant growth hormone treatment in adult GH deficiency

Introduction

Non-compliance to recombinant human growth hormone (rhGH) treatment is universally recognized as a key detrimental factor to achieve the expected clinical outcomes in adult GH deficiency (aGHD). The Easypod™ electronic device allows objective measurement of adherence. Adherence to treatment has been reported to be related with IGF-1 levels and consequently with clinical satisfactory results. The aim of this multicentric, observational, retrospective, 24- month study, is to objectively assess aGHD patients' compliance to rhGH, using the Easypod™ device. Additionally, the study aims to compare the biochemical responses of adherent vs non-adherent patients.

Methods

Forty-three patients (28 females and 15 males) affected by aGHD and equipped with Easypod™ from 3 Italian centers were included in the study. Adherence to treatment was defined as the proportion of injections correctly administered during the observational period, out of the expected total number of injections. All patients were evaluated for IGF-1, glucose, insulin, HOMA and QUICKI index, total/LDL/HDL cholesterol and triglycerides.

Results

Mean adherence rate was consistently under 85% across the 2-year observation period (73% at year 2). A trend toward significant difference in adherence was shown when comparing female and male patients (respectively 76% and 61%) after a 2-year period. Among the anamnestic features, the prescribed frequency of administration of rhGH and the number of administered therapies appeared to be the most relevant adherence-influencing factors. A strong direct correlation between IGF-1 z-score and adherence to rhGH therapy was detected in the whole population.

Discussion

Compliance to rhGH therapy is still a major issue in aGHD treatment. Adherence relates to therapy efficacy in aGHD. The use of Easypod™ could be beneficial for physicians to better manage aGHD patients and to achieve improved better biochemical and clinical responses.

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 2019–February 2020 (before COVID-19) and March 2020–February 2021 (during COVID-19) from the easypod™ connect ecosystem. Data from three measures of regulations were analysed: stringency index (SI), school closure and stay-at-home. The mean SI, and the proportion of days with required school closure or stay-at-home during COVID-19 were categorised as high versus medium/low based on the 75th percentile. Adherence was categorised as optimal (≥85%) versus suboptimal (<85%). Adherence data were available for 8915 patients before and 7606 patients during COVID-19. A high SI (mean ≥68) and a high proportion of required school closure (≥88%) resulted in an increase in the proportion of optimal adherence during COVID-19 versus pre-COVID-19 (p < 0.001). Stay-at-home requirements showed no statistically significant effect (p = 0.13). Stringent COVID-19 regulations resulted in improved adherence to r-hGH therapy in patients with growth disorders, supported by connected digital health technologies. Insights into patient behavior during this time are useful to understand potential influences and strategies to improve long-term adherence to r-hGH.

Precision diagnostics in children

Medical practice is transforming from a reactive to a pro-active and preventive discipline that is underpinned by precision medicine. The advances in technologies in such fields as genomics, proteomics, metabolomics, transcriptomics and artificial intelligence have resulted in a paradigm shift in our understanding of specific diseases in childhood, greatly enhanced by our ability to combine data from changes within cells to the impact of environmental and population changes. Diseases in children have been reclassified as we understand more about their genomic origin and their evolution. Genomic discoveries, additional 'omics' data and advances such as optical genome mapping have driven rapid improvements in the precision and speed of diagnoses of diseases in children and are now being incorporated into newborn screening, have improved targeted therapies in childhood and have supported the development of predictive biomarkers to assess therapeutic impact and determine prognosis in congenital and acquired diseases of childhood. New medical device technologies are facilitating data capture at a population level to support higher diagnostic accuracy and tailored therapies in children according to predicted population outcome, and digital ecosystems now tailor therapies and provide support for their specific needs. By capturing biological and environmental data as early as possible in childhood, we can understand factors that predict disease or maintain health and track changes across a more extensive longitudinal path. Data from multiple health and external sources over long-time periods starting from birth or even in the in utero environment will provide further clarity about how to sustain health and prevent or predict disease. In this respect, we will not only use data to diagnose disease, but precision diagnostics will aid the 'diagnosis of good health'. The principle of 'start early and change more' will thus underpin the value of applying a personalised medicine approach early in life.

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 assessed in children (aged < 18 years) who started using a connected auto-injector device (easypod™), and transmitted injection data for ≥ 12 months. Adherence in the following 3, 6, or 9 months after treatment start was categorized as optimal (≥ 85%) versus sub-optimal (< 85%). Logistic regression and tree-based models were applied.

RESULTS

Data from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort settings, and starting treatment at an older age was important in predicting the risk of sub-optimal adherence in the following 3, 6, or 9 months. Sensitivities ranged between 0.72 and 0.77, and specificities between 0.80 and 0.81.

CONCLUSIONS

To the authors' knowledge, this is the first attempt to integrate a machine learning model into a digital health ecosystem to help healthcare providers to identify patients at risk of sub-optimal adherence to r-hGH in the following 3, 6, or 9 months. This information, together with patient-specific indicators of sub-optimal adherence, can be used to provide support to at-risk patients and their caregivers to achieve optimal adherence and, subsequently, improve clinical outcomes.

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, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.