Evaluating the Usefulness and Ease of Use of a Next-Generation-Connected Drug Delivery Device for Growth Hormone Therapy: Qualitative Study of Health Care Professionals' Perceptions.

BACKGROUND: Digital solutions targeting children's health have become an increasingly important element in the provision of integrated health care. For the treatment of growth hormone deficiency (GHD), a unique connected device is available to facilitate the delivery of recombinant human growth hormone (r-hGH) by automating the daily injection process and collecting injection data such that accurate adherence information is available to health care professionals (HCPs), caregivers, and patients. The adoption of such digital solutions requires a good understanding of the perspectives of HCPs as key stakeholders because they leverage data collection and prescribe these solutions to their patients. OBJECTIVE: This study aimed to evaluate the third generation of the easypod device (EP3) for the delivery of r-hGH treatment from the HCP perspective, with a focus on perceived usefulness and ease of use. METHODS: A qualitative study was conducted, based on a participatory workshop conducted in Zaragoza, Spain, with 10 HCPs experienced in the management of pediatric GHD from 7 reference hospitals in Spain. Several activities were designed to promote discussion among participants about predefined topics based on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology to provide their perceptions about the new device. RESULTS: Participants reported 2 key advantages of EP3 over previous easypod generations: the touch screen interface and the real-time data transmission functionality. All participants (10/10, 100%) agreed that the new device should be part of a digital health ecosystem that provides complementary functionalities including data analysis. CONCLUSIONS: This study explored the perceived value of the EP3 autoinjector device for the treatment of GHD by HCPs. HCPs rated the new capabilities of the device as having substantial improvements and concluded that it was highly recommendable for clinical practice. EP3 will enhance decision-making and allow for more personalized care of patients receiving r-hGH.

Participatory Study to Explore Healthcare Professionals' Perceptions of a Connected Digital Solution for Adherence Monitoring of Recombinant Human Growth Hormone Treatment: Study Protocol and First Findings.

Adherence to recombinant human growth hormone (r-hGH; somatropin, [Saizen®], Merck Healthcare KGaA, Darmstadt, Germany) treatment is fundamental to achieve positive growth outcomes in children with growth disorders and to improve quality of life and cardiometabolic risk in adult patients affected by GH deficiency. Pen injector devices are commonly used to deliver r-hGH but, to the authors' knowledge, none is currently digitally connected. Since digital health solutions are rapidly becoming valuable tools to support patients to adhere to treatment, the combination of a pen injector connected to a digital ecosystem to monitor treatment adherence is an important advance. Here, we present the methodology and first results of a participatory workshop that assessed clinicians' perceptions on such a digital solution - the aluetta™ smartdot™ (Merck Healthcare KGaA, Darmstadt, Germany) - combining the aluetta™ pen injector and a connected device, components of a comprehensive digital health ecosystem to support pediatric patients receiving r-hGH treatment. The aim being to highlight the importance of collecting clinically meaningful and accurate real-world adherence data to support data-driven healthcare.

Accelerating digital health literacy for the treatment of growth disorders: The impact of a massive open online course.

Background: Growth hormone deficiency (GHD) is a rare disorder characterized by inadequate secretion of growth hormone (GH) from the anterior pituitary gland. One of the challenges in optimizing GH therapy is improving adherence. Using digital interventions may overcome barriers to optimum treatment delivery. Massive open online courses (MOOCs), first introduced in 2008, are courses made available over the internet without charge to a large number of people. Here, we describe a MOOC aiming to improve digital health literacy among healthcare professionals managing patients with GHD. Based on pre- and post-course assessments, we evaluate the improvement in participants' knowledge upon completion of the MOOC. Methods: The MOOC entitled 'Telemedicine: Tools to Support Growth Disorders in a Post-COVID Era' was launched in 2021. It was designed to cover 4 weeks of online learning with an expected commitment of 2 h per week, and with two courses running per year. Learners' knowledge was assessed using pre- and post-course surveys via the FutureLearn platform. Results: Out of 219 learners enrolled in the MOOC, 31 completed both the pre- and post-course assessments. Of the evaluated learners, 74% showed improved scores in the post-course assessment, resulting in a mean score increase of 21.3%. No learner achieved 100% in the pre-course assessment, compared with 12 learners (40%) who achieved 100% in the post-course assessment. The highest score increase comparing the pre- and the post-course assessments was 40%, observed in 16% of learners. There was a statistically significant improvement in post-course assessment scores from 58.1 ± 18.9% to 72.6 ± 22.4% reflecting an improvement of 14.5% (p < 0.0005) compared to the pre-course assessment. Conclusion: This "first-of-its-kind" MOOC can improve digital health literacy in the management of growth disorders. This is a crucial step toward improving the digital capability and confidence of healthcare providers and users, and to prepare them for the technological innovations in the field of growth disorders and growth hormone therapy, with the aim of improving patient care and experience. MOOCs provide an innovative, scalable and ubiquitous solution to train large numbers of healthcare professionals in limited resource settings.

Individuals with numerical and structural variations of sex chromosomes: interdisciplinary management with focus on fertility potential.

Diagnosis and management of individuals who have differences of sex development (DSD) due to numerical or structural variations of sex chromosomes (NSVSC) remains challenging. Girls who have Turner syndrome (45X) may present with varying phenotypic features, from classical/severe to minor, and some remain undiagnosed. Boys and girls who have 45,X/46,XY chromosomal mosaicism may have Turner syndrome-like features and short stature; therefore, unexplained short stature during childhood requires karyotype analysis in both sexes, particularly if characteristic features or atypical genitalia are present. Many individuals with Klinefelter syndrome (47XXY) remain undiagnosed or are only diagnosed as adults due to fertility problems. Newborn screening by heel prick tests could potentially identify sex chromosome variations but would have ethical and financial implications, and in-depth cost-benefit analyses are needed before nationwide screening can be introduced. Most individuals who have NSVSC have lifelong co-morbidities and healthcare should be holistic, personalized and centralized, with a focus on information, psychosocial support and shared decision-making. Fertility potential should be assessed individually and discussed at an appropriate age. Oocyte or ovarian tissue cryopreservation is possible in some women who have Turner syndrome and live births have been reported following assisted reproductive technology (ART). Testicular sperm cell extraction (TESE) is possible in some men who have 45,X/46,XY mosaicism, but there is no established protocol and no reported fathering of children. Some men with Klinefelter syndrome can now father a child following TESE and ART, with multiple reports of healthy live births. Children who have NSVSC, their parents and DSD team members need to address possibilities and ethical questions relating to potential fertility preservation, with guidelines and international studies still needed.

Evaluating the TUITEK® patient support program in supporting caregivers of children diagnosed with growth hormone deficiency in Argentina.

Introduction: The appropriate use of recombinant human growth hormone (r-hGH) treatment provides an opportunity to improve growth outcomes among pediatric patients with growth hormone deficiency (GHD). However, a major challenge in clinical practice is to adequately recognize and address factors that negatively affect treatment adherence. TUITEK® patient support program (PSP) was designed to help caregivers of children diagnosed with GHD to personalize the care pathway, improve adherence, and achieve better outcomes. Effectiveness of TUITEK® PSP has been demonstrated previously in a smaller sample (n = 31) in Taiwanese population. Here, we present the results from Argentina. Methods: TUITEK® PSP was piloted among 76 caregivers of children with GHD administering r-hGH using easypod™ (Merck KGaA, Darmstadt, Germany) auto-injector device in Argentina. Based on TUITEK® personalization questionnaire, caregivers were assigned to high- and low-risk groups across four categories that may influence adherence, including disease and treatment coherence (DTC), self-administration (SA), treatment-related anxiety (TRA), and emotional burden (EB). The caregivers who were included in atleast one high-risk group had the provision of telephone calls with a nurse practitioner every 2 weeks for 3 months. The Wilcoxon signed-rank test was employed to assess changes in questionnaire-based scoring patterns between baseline and follow-up evaluations. Results: Statistically significant changes (p < 0.05) in questionnaire scores between baseline and follow-up evaluations were observed across the four categories. The mean/median DTC (n = 11) and SA (n = 23) scores changed from 2.45/3 and 2.17/2, respectively, to 4/4, with all the caregivers moving to low-risk group following program completion (100%) for both categories. The mean/median TRA score (n = 40) changed from 3.58/3 to 2.5/2 and 67.5% of patients (27/40) moved to low-risk group. The mean/median EB score (n = 32) changed from 3.69/3 to 3.13/3 however, none of the caregivers moved to low-risk group (0%). Conclusion: TUITEK® PSP is a simple, practical, and time-efficient interventional tool that can be used to address key adherence-related issues among caregivers of children with GHD and provide personalized adherence support. Our findings demonstrate that TUITEK® PSP has the potential to improve treatment adherence and self-management, thereby improving growth outcomes in Argentina.

An Augmented Reality Mobile App (Easypod AR) as a Complementary Tool in the Nurse-Led Integrated Support of Patients Receiving Recombinant Human Growth Hormone: Usability and Validation Study.

BACKGROUND: Children with growth hormone deficiency face the prospect of long-term recombinant human growth hormone (r-hGH) treatment requiring daily injections. Adherence to treatment is important, especially at treatment initiation, to achieve positive health outcomes. Historically, telenursing services embedded in patient support programs (PSPs) have been a valid approach to support r-hGH treatment initiation and patient education and facilitate adherence by identifying and optimizing appropriate injection techniques. The development of mobile phones with augmented reality (AR) capabilities offers nurses new tools to support patient education. OBJECTIVE: To investigate experiences among nurses of a new mobile phone app developed to support patient training with a phone-based PSP for r-hGH treatment. METHODS: In 2020, the Easypod AR mobile app was launched to support nurse-driven telehealth education for patients initiating r-hGH therapy with the Easypod electromechanical auto-injector device. Nurses who were part of PSPs in countries where the Easypod AR app had been launched or where training was provided as part of an anticipated future launch of the app were invited to participate in an online survey based on the Mobile App Rating Scale to capture their feedback after using the app. RESULTS: In total, 23 nurses completed the online questionnaire. They positively rated the quality of the app across multiple dimensions. The highest mean scores were 4.0 for engagement (ie, adaptation to the target group; SD 0.74), 4.1 (SD 0.79) for functionality (navigation) and 4.1 (SD 0.67) for aesthetics (graphics). Responses indicated the potential positive impact of such a tool on enhancing patient education, patient support, and communication between patients and PSP nurses. Some participants also suggested enhancements to the app, including gamification techniques that they felt have the potential to support the formation of positive treatment behaviors and habits. CONCLUSIONS: This study highlights the potential for new digital health solutions to reinforce PSP nurse services, including patient education. Future studies could explore possible correlations between any behavioral and clinical benefits that patients may derive from the use of such apps and how they may contribute to support improved patient experiences and treatment outcomes.

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 recombinant human growth hormone (r-hGH). Height data collected between 0-48 months of treatment were extracted from the easypod™ connect ecosystem and the easypod™ connect observational study. Selected patients with height standard deviation scores (HSDS) [-4, <-1] and age [3, <16y] at start were included. The 'Matching Database' consisted of patients' monthly HSDS obtained by the broken stick method and imputation. Standard deviation (SD) was obtained from the observed minus the predicted HSDS (error) based on matched patients within the 'Matching Database'. Data were available for 3,213 patients in the 'Matching Database', and 2,472 patients with 16,624 HSDS measurements in the observed database. When ≥2 HSDS measurements were available, the error SD for a one-year prediction was approximately 0.2, which corresponds to 1.1 cm, 1.3 cm, and 1.5 cm at 7, 11, and 15 years of age, respectively. Indication and age at treatment start (<11 vs ≥11 years) had a small impact on the error SD, with patients born SGA and patients aged <11 years at treatment start generally having slightly lower values. We conclude that curve matching is a simple and valid technique for predicting growth in patients with GHD and those born SGA.

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.

Adherence to r-hGH Therapy in Pediatric Growth Hormone Deficiency: Current Perspectives on How Patient-Generated Data Will Transform r-hGH Treatment Towards Integrated Care.

Pediatric growth hormone (GH) deficiency is a licensed indication for replacement therapy with recombinant human growth hormone (r-hGH). Treatment, consisting of daily subcutaneous injections, extends from the time of diagnosis until cessation of linear growth at completion of puberty. Suboptimal adherence to r-hGH therapy is common and has been well documented to substantially impair the growth response and achievement of the optimal goal which is attainment of adult height within the genetic target range. The causes of poor adherence are complex and include disease-, patient-, doctor-, and treatment-related factors. Interventions for suboptimal adherence are important for a long-term successful outcome and can include both face-to-face and digital strategies. Face-to-face interventions include behavioral change approaches such as motivational interviewing and non-judgmental assessment. Medical and nursing staff require training in these techniques. Digital solutions are rapidly advancing as evidenced by the electronic digital auto-injector device, easypod® (Merck Healthcare KGaA, Darmstadt, Germany), which uses the web-based easypod® connect platform allowing adherence data to be transmitted electronically to healthcare professionals (HCPs), who can then access GH treatment history, enhancing clinical decisions. Over the past 10 years, the multi-national Easypod® Connect Observational Study has reported high levels of adherence (>85%) from up to 40 countries. The easypod® connect system can be supported by a smartphone app, growlink™, which facilitates the interactions between the patients, their care team, and patient support services. HCPs are empowered by new digital techniques, however, the human-digital partnership remains essential for optimal growth management. The pediatric patient on r-hGH therapy will benefit from these innovations to enhance adherence and optimize long-term response.

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.

Investigating the Impact of the TUITEK® Patient Support Programme, Designed to Support Caregivers of Children Prescribed Recombinant Human Growth Hormone Treatment in Taiwan.

Purpose: Poor adherence to recombinant human growth hormone (r-hGH) treatment presents a significant barrier to achieving optimal growth outcomes. It is important to identify and address the treatment adherence-related needs of children prescribed r-hGH treatment, and develop new approaches to improve adherence. We aimed to measure the impact of the TUITEK® patient support programme, a multi-component personalized service intervention, on caregivers' knowledge, beliefs, and perceptions of short stature and adherence to its treatment. Patients and Methods: The evaluation of the TUITEK® patient support programme was conducted among 31 caregivers of children with short stature and receiving r-hGH treatment via the easypod™ auto-injector device in Taiwan. Caregivers within the 'high risk' category for knowledge, beliefs and perception factors influencing adherence to r-hGH treatment (disease and treatment coherence, emotional burden, self-administration, and treatment-related anxiety) were identified via the TUITEK® personalization questionnaire and followed up with bi-weekly telephone calls by a nurse practitioner over a 3-month period. A Wilcoxon signed-rank test was used to compare changes in questionnaire-based scoring patterns between baseline and follow-up. Results: Between baseline and follow-up, the percentage of caregivers scoring as 'high risk' for emotional burden reduced by 37%; there was an improvement in confidence of self-administration by 57% and the percentage of caregivers scoring as 'high risk' for treatment-related anxiety reduced by 52%. At follow-up, all caregivers classified as 'high risk' within the disease and treatment coherence item at baseline moved into the 'low risk' category. Statistically significant changes in questionnaire scores between baseline and follow-up for disease and treatment understanding, emotional burden, self-administration, and treatment-related anxiety were also observed. Conclusion: These findings indicate that the TUITEK® patient support programme can positively address disease and treatment-related barriers amongst caregivers regarding optimal adherence of their children to r-hGH treatment, which has the potential to positively impact on adherence levels and patient clinical health outcomes.

Observed High Adherence to Recombinant Human Growth Hormone Treatment Using a Multi-Component Approach to Improve Adherence in Individuals with Growth Disorders.

We explored whether a multi-component approach - using a digital health device, the easypod™ auto-injector, the 'MySupport' patient support programme (PSP) and a Patient Activation Measure® (PAM®) - could improve adherence in patients receiving recombinant human growth hormone (r-hGH). A 13-item PAM was used to assess caregiver self-reported knowledge, resulting in two PAM scores for 88 patients at four UK hospitals after an average of 5.6 months. Most patients improved their PAM score by ≥1 level (43%) or maintained it (>-1 and <1; 21%). In parallel, 74% of patients maintained (-5 to +5%) or improved (≥5%) their adherence. Further studies are required to evaluate a multi-component approach to adherence in a larger population and for a longer duration.

Adherence to Growth Hormone Treatment Using a Connected Device in Latin America: Real-World Exploratory Descriptive Analysis Study.

BACKGROUND: Recombinant human growth hormone (rhGH) therapy is an effective treatment for children with growth disorders. However, poor outcomes are often associated with suboptimal adherence to treatment. OBJECTIVE: The easypod connected injection device records and transmits injection settings and dose data from patients receiving rhGH. In this study, we evaluated adherence to rhGH treatment, and associated growth outcomes, in Latin American patients. METHODS: Adherence and growth data from patients aged 2-18 years from 12 Latin American countries were analyzed. Adherence data were available for 6207 patients with 2,449,879 injections, and growth data were available for 497 patients with 2232 measurements. Adherence was categorized, based on milligrams of rhGH injected versus milligrams of rhGH prescribed, as high (≥85%), intermediate (>56%-<85%), or low (≤56%). Transmission frequency was categorized as high (≥1 per 3 months) or low (<1 per 3 months). Chi-square tests were applied to study the effect of pubertal status at treatment start and sex on high adherence, and to test differences in frequency transmission between the three adherence levels. Multilevel linear regression techniques were applied to study the effect of adherence on observed change in height standard deviation score (∆HSDS). RESULTS: Overall, 68% (4213/6207), 25% (n=1574), and 7% (n=420) of patients had high, intermediate, and low adherence, respectively. Pubertal status at treatment start and sex did not have a significant effect on high adherence. Significant differences were found in the proportion of patients with high transmission frequency between high (2018/3404, 59%), intermediate (608/1331, 46%), and low (123/351, 35%) adherence groups (P<.001). Adherence level had a significant effect on ∆HSDS (P=.006). Mean catch-up growth between 0-24 months was +0.65 SD overall (+0.52 SD in patients with low/intermediate monthly adherence and +0.69 SD in patients with high monthly adherence). This difference translated into 1.1 cm greater catch-up growth with high adherence. CONCLUSIONS: The data extracted from the easypod Connect ecosystem showed high adherence to rhGH treatment in Latin American patients, with positive growth outcomes, indicating the importance of connected device solutions for rhGH treatment in patients with growth disorders.

A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems.

Recombinant human growth hormone (r-hGH) is an established therapy for growth hormone deficiency (GHD); yet, some patients fail to achieve their full height potential, with poor adherence and persistence with the prescribed regimen often a contributing factor. A data-driven clinical decision support system based on "traffic light" visualizations for adherence risk management of patients receiving r-hGH treatment was developed. This research was feasible thanks to data-sharing agreements that allowed the creation of these models using real-world data of r-hGH adherence from easypod™ connect; data was retrieved for 11,015 children receiving r-hGH therapy for ≥180 days. Patients' adherence to therapy was represented using four values (mean and standard deviation [SD] of daily adherence and hours to next injection). Cluster analysis was used to categorize adherence patterns using a Gaussian mixture model. Following a traffic lights-inspired visualization approach, the algorithm was set to generate three clusters: green, yellow, or red status, corresponding to high, medium, and low adherence, respectively. The area under the receiver operating characteristic curve (AUC-ROC) was used to find optimum thresholds for independent traffic lights according to each metric. The most appropriate traffic light used the SD of the hours to the next injection, with an AUC-ROC value of 0.85 when compared to the complex clustering algorithm. For the daily adherence-based traffic lights, optimum thresholds were >0.82 (SD, <0.37), 0.53-0.82 (SD, 0.37-0.61), and <0.53 (SD, >0.61) for high, medium, and low adherence, respectively. For hours to next injection, the corresponding optimum thresholds were <27.18 (SD, <10.06), 27.18-34.01 (SD, 10.06-29.63), and >34.01 (SD, >29.63). Our research indicates that implementation of a practical data-driven alert system based on recognised traffic-light coding would enable healthcare practitioners to monitor sub-optimally-adherent patients to r-hGH treatment for early intervention to improve treatment outcomes.

Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care.

Digitalization of healthcare delivery is rapidly fostering development of precision medicine. Multiple digital technologies, known as telehealth or eHealth tools, are guiding individualized diagnosis and treatment for patients, and can contribute significantly to the objectives of precision medicine. From a basis of "one-size-fits-all" healthcare, precision medicine provides a paradigm shift to deliver a more nuanced and personalized approach. Genomic medicine utilizing new technologies can provide precision analysis of causative mutations, with personalized understanding of mechanisms and effective therapy. Education is fundamental to the telehealth process, with artificial intelligence (AI) enhancing learning for healthcare professionals and empowering patients to contribute to their care. The Gulf Cooperation Council (GCC) region is rapidly implementing telehealth strategies at all levels and a workshop was convened to discuss aspirations of precision medicine in the context of pediatric endocrinology, including diabetes and growth disorders, with this paper based on those discussions. GCC regional investment in AI, bioinformatics and genomic medicine, is rapidly providing healthcare benefits. However, embracing precision medicine is presenting some major new design, installation and skills challenges. Genomic medicine is enabling precision and personalization of diagnosis and therapy of endocrine conditions. Digital education and communication tools in the field of endocrinology include chatbots, interactive robots and augmented reality. Obesity and diabetes are a major challenge in the GCC region and eHealth tools are increasingly being used for management of care. With regard to growth failure, digital technologies for growth hormone (GH) administration are being shown to enhance adherence and response outcomes. While technical innovations become more affordable with increasing adoption, we should be aware of sustainability, design and implementation costs, training of HCPs and prediction of overall healthcare benefits, which are essential for precision medicine to develop and for its objectives to be achieved.

New Horizons in Short Children Born Small for Gestational Age.

Children born small for gestational age (SGA) comprise a heterogeneous group due to the varied nature of the cause. Approximately 85-90% have catch-up growth within the first 4 postnatal years, while the remainder remain short. In later life, children born SGA have an increased risk to develop metabolic abnormalities, including visceral adiposity, insulin resistance, and cardiovascular problems, and may have impaired pubertal onset and growth. The third "360° European Meeting on Growth and Endocrine Disorders" in Rome, Italy, in February 2018, funded by Merck KGaA, Germany, included a session that examined aspects of short children born SGA, with three presentations followed by a discussion period, on which this report is based. Children born SGA who remain short are eligible for GH treatment, which is an approved indication. GH treatment increases linear growth and can also improve some metabolic abnormalities. After stopping GH at near-adult height, metabolic parameters normalize, but pharmacological effects on lean body mass and fat mass are lost; continued monitoring of body composition and metabolic changes may be necessary. Guidelines have been published on diagnosis and management of children with Silver-Russell syndrome, who comprise a specific group of those born SGA; these children rarely have catch-up growth and GH treatment initiation as early as possible is recommended. Early and moderate pubertal growth spurt can occur in children born SGA, including those with Silver-Russell syndrome, and reduce adult height. Treatments that delay puberty, specifically metformin and gonadotropin releasing hormone analogs in combination with GH, have been proposed, but are used off-label, currently lack replication of data, and require further studies of efficacy and safety.

An eHealth Framework for Managing Pediatric Growth Disorders and Growth Hormone Therapy.

BACKGROUND: The use of technology to support health and health care has grown rapidly in the last decade across all ages and medical specialties. Newly developed eHealth tools are being implemented in long-term management of growth failure in children, a low prevalence pediatric endocrine disorder. OBJECTIVE: Our objective was to create a framework that can guide future implementation and research on the use of eHealth tools to support patients with growth disorders who require growth hormone therapy. METHODS: A total of 12 pediatric endocrinologists with experience in eHealth, from a wide geographical distribution, participated in a series of online discussions. We summarized the discussions of 3 workshops, conducted during 2020, on the use of eHealth in the management of growth disorders, which were structured to provide insights on existing challenges, opportunities, and solutions for the implementation of eHealth tools across the patient journey, from referral to the end of pediatric therapy. RESULTS: A total of 815 responses were collected from 2 questionnaire-based activities covering referral and diagnosis of growth disorders, and subsequent growth hormone therapy stages of the patient pathway, relating to physicians, nurses, and patients, parents, or caregivers. We mapped the feedback from those discussions into a framework that we developed as a guide to integration of eHealth tools across the patient journey. Responses focused on improved clinical management, such as growth monitoring and automation of referral for early detection of growth disorders, which could trigger rapid evaluation and diagnosis. Patient support included the use of eHealth for enhanced patient and caregiver communication, better access to educational opportunities, and enhanced medical and psychological support during growth hormone therapy management. Given the potential availability of patient data from connected devices, artificial intelligence can be used to predict adherence and personalize patient support. Providing evidence to demonstrate the value and utility of eHealth tools will ensure that these tools are widely accepted, trusted, and used in clinical practice, but implementation issues (eg, adaptation to specific clinical settings) must be addressed. CONCLUSIONS: The use of eHealth in growth hormone therapy has major potential to improve the management of growth disorders along the patient journey. Combining objective clinical information and patient adherence data is vital in supporting decision-making and the development of new eHealth tools. Involvement of clinicians and patients in the process of integrating such technologies into clinical practice is essential for implementation and developing evidence that eHealth tools can provide value across the patient pathway.

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 is able to generate predictions of future adherence using a recurrent neural network with adherence data recorded by easypodTM, a connected autoinjection device. The model was trained with a multi-year long dataset with 2500 patients, from January 2007 to June 2019. When testing, the model reached an average sensitivity of 0.70 and a specificity of 0.88 per patient when predicting non-adherence (<85%) periods. When evaluated with thousands of therapy segments extracted from a test set, our model reached an AUC-PR score of 0.79 and AUC-ROC of 0.90; both metrics were consistently better than traditional approaches, such as simple average model. Using this model, we can perform precise early identification of patients who are likely to become non-adherent patients. This opens a path for healthcare practitioners to personalize GH therapy at any stage of the patients' journey and improve shared decision making with patients and caregivers to achieve optimal outcomes.

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, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.

Digital Health in the Management of Pediatric Growth Hormone Therapy - 10 Years of Developments.

Growth hormone administration is approved for use in a number of growth failure conditions in children. Optimal growth outcome requires continuous daily injections over many years, leading to problems of persistence and adherence with therapy. The easypod™ connect ecosystem enables electronic monitoring of injection and dose history, transmitted to a secure cloud database via the internet or cellular networks. Thus, healthcare providers can easily monitor adherence with therapy and be alerted to problems. The growlink™ patient app has been added to the ecosystem to provide solutions that can engage and educate patients and their families/caregivers. growlink™ also allows patients to self-report height and weight, enabling healthcare providers to track growth progression. The patient support program, TuiTek and the easypod™ Augmented Reality (AR) app are being developed within the ecosystem to support telehealth services, increase disease awareness and reduce therapy-related anxiety. easypod™ connect provides objective assessments of adherence, shown to be maintained at a high level over several years, and analyses showed that increased adherence was significantly associated with a better growth outcome. Studies have identified factors that influence persistence and adherence with GH therapy via the easypod™ connect ecosystem. These novel technologies are generating solutions that enable data-driven personalized care for children with growth disorders and optimize long-term clinical outcomes.