Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Koledova, Ekaterina; Tornincasa, Vincenzo; Dommelen, Paula van

doi:10.1186/s12911-020-01183-1

Cited by 26 publications

(38 citation statements)

References 37 publications

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“…The data captured in the easypod™ ecosystem database currently include longitudinal records for 13,553 children transmitting data on > 10 injections from January 2007 to February 2019 [ 29 ]. The 10 injection cut-off point was imposed to exclude test or training injections from when therapy is initiated.…”

Section: Resultsmentioning

confidence: 99%

“…Analysis of results from the overall database [ 24 ] and separate analyses of data from ECOS [ 23 ] suggest that pubertal children, especially boys, are at greater risk for suboptimal adherence and may benefit from closer engagement, particularly around the age when they start to take responsibility for their injections [ 24 , 28 , 29 ]. Specifically, analyses of data from 9,314 records from the overall database showed that, at each time point studied, a significantly greater proportion of female patients had high adherence (defined as ≥ 85% [medium: > 56% to < 85%; low: ≤ 56%]) than male patients (2849 of 3867 [74%] vs 3879 of 5447 [71%]; P < 0.001) [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

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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

Fernández-Luque²,

Koledova

et al. 2021

BMC Med Inform Decis Mak

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Background A range of factors can reduce the effectiveness of treatment prescribed for the long-term management of chronic health conditions, such as growth disorders. In particular, prescription medications may not achieve the positive outcomes expected because approximately half of patients adhere poorly to the prescribed treatment regimen. Methods Adherence to treatment has previously been assessed using relatively unreliable subjective methods, such as patient self-reporting during clinical follow-up, or counting prescriptions filled or vials returned by patients. Here, we report on a new approach, the use of electronically recorded objective evidence of date, time, and dose taken which was obtained through a comprehensive eHealth ecosystem, based around the easypod™ electromechanical auto-injection device and web-based connect software. The benefits of this eHealth approach are also illustrated here by two case studies, selected from the Finnish cohort of the easypod™ Connect Observational Study (ECOS), a 5-year, open-label, observational study that enrolled children from 24 countries who were being treated with growth hormone (GH) via the auto-injection device. Results Analyses of data from 9314 records from the easypod™ connect database showed that, at each time point studied, a significantly greater proportion of female patients had high adherence (≥ 85%) than male patients (2849/3867 [74%] vs 3879/5447 [71%]; P < 0.001). Furthermore, more of the younger patients (< 10 years for girls, < 12 years for boys) were in the high adherence range (P < 0.001). However, recursive partitioning of data from ECOS identified subgroups with lower adherence to GH treatment ‒ children who performed the majority of injections themselves at an early age (~ 8 years) and teenagers starting treatment aged ≥ 14 years. Conclusions The data and case studies presented herein illustrate the importance of adherence to GH therapy and how good growth outcomes can be achieved by following treatment as described. They also show how the device, software, and database ecosystem can complement normal clinical follow-up by providing HCPs with reliable information about patient adherence between visits and also providing researchers with real-world evidence of adherence and growth outcomes across a large population of patients with growth disorders treated with GH via the easypod™ device.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Fernández-Luque²,

Koledova

et al. 2021

BMC Med Inform Decis Mak

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“…1. Mean dose adherence over the duration of use (injected vs prescribed in %) categorized into optimal (≥85%) or suboptimal (<85%) [7] (this is a reference threshold for adherence and corresponds to approximately one missed injection/week [8]); 2. Persistence of use, defined as the time in years between the first injections and the last recorded injection in the system; patients were considered to have stopped the use if they had no injection recorded for 6 consecutive months; and 3.…”

Section: Data and Feature Engineeringmentioning

confidence: 99%

“…This enables the collection of real-world data on patients and their injection behavior and, hence, the accurate estimation of key treatment-related parameters. While several studies have shown the impact of clinical features on key r-hGH treatment success metrics [6,7], to our knowledge, none has investigated whether engagement with the treatment per se has a positive impact.…”

Section: Introductionmentioning

confidence: 99%

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

Spataru

Quarteroni

Arnaud

et al. 2021

Studies in Health Technology and Informatics

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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.

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“…Physicians’ knowledge may be limited, which can often lead to delayed referral and diagnosis, and the small number of patients creates difficulties in collection of data to generate large and meaningful data sets unless performed on an international scale. The collection of adherence data is of crucial importance [ 21 ] to optimizing adherence and improving adult height [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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

Dimitri¹,

Fernández-Luque²,

Banerjee³

et al. 2021

J Med Internet Res

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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.

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Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Cited by 26 publications

References 37 publications

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

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

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

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

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