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  • 1
    Online Resource
    Online Resource
    A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy
    The Endocrine Society ; 2021
    In:  Journal of the Endocrine Society Vol. 5, No. Supplement_1 ( 2021-05-03), p. A672-A673
    Details
    In: Journal of the Endocrine Society, The Endocrine Society, Vol. 5, No. Supplement_1 ( 2021-05-03), p. A672-A673
    Abstract: 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 from the first 3 months of treatment to identify potential indicators of suboptimal adherence and predict adherence over the following 9 months using a machine learning approach. Methods: We assessed adherence to r-hGH treatment in children with growth disorders in their first 12 months of treatment using a connected autoinjector and e-device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect). We selected children who started the use of the device before 18 years of age and who transmitted their injection data for at least 12 months. Adherence (mg injected/mg prescribed) between 4-12 months (outcome) was categorized as optimal (≥85%) versus suboptimal ( & lt;85%). In addition to adherence over the first 3 months, comfort settings (needle speed, injection depth, injection speed, injection time), number of transmissions, number of dose changes, age at start and sex were used as potential indicators of suboptimal adherence. Several machine learning models were optimized on a class-balanced training dataset using a 5-fold cross-validation scheme. On the best performing model, machine learning interpretation techniques and chi-squared statistical tests were applied to extract the statistically significant indicators of suboptimal and optimal adherence. Results: Anonymized data were available for 10,943 children. The optimal prediction performances were achieved with the random forest algorithm. The mean adherence and the adherence standard deviation over the first 3 months were the two most important features for predicting adherence in the following 9 months. Not using the system’s features (e.g. not transmitting data often and not changing some of the comfort settings, such as the needle speed setting), as well as starting treatment at an older age were significantly associated with an increased risk of suboptimal adherence (p & lt;0.001). When tested on first-time seen data following the same class distribution as the original data, the model achieved a sensitivity of 80% and a specificity of 81%. Conclusions: We developed a model predicting whether a child’s adherence in the following 9 months will be below or above the optimal threshold (85%) based on early data from the first 3 months of treatment and we identified the indicators of suboptimal adherence. These results can be used to identify children needing additional medical or other support to reach optimal adherence and therefore optimal clinical outcomes.
    Type of Medium: Online Resource
    ISSN: 2472-1972
    URL: Article
    DOI: 10.1210/jendso/bvab048.1371
    Language: English
    Publisher: The Endocrine Society
    Publication Date: 2021
    detail.hit.zdb_id: 2881023-5
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  • 2
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    Online Resource
    Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis
    Frontiers Media SA ; 2022
    In:  Frontiers in Endocrinology Vol. 13 ( 2022-6-30)
    Details
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 13 ( 2022-6-30)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1664-2392
    URL: Article
    DOI: 10.3389/fendo.2022.882192
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2592084-4
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  • 3
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    Online Resource
    The Impact of COVID-19 Regulations on Adherence to Recombinant Human Growth Hormone Therapy: Evidence from Real-World Data
    MDPI AG ; 2023
    In:  Endocrines Vol. 4, No. 1 ( 2023-03-09), p. 194-204
    Details
    In: Endocrines, MDPI AG, Vol. 4, No. 1 ( 2023-03-09), p. 194-204
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2673-396X
    URL: Article
    DOI: 10.3390/endocrines4010017
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 3019981-5
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  • 4
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    Online Resource
    Adherence to Growth Hormone Treatment Using a Connected Device in Latin America: Real-World Exploratory Descriptive Analysis Study
    JMIR Publications Inc. ; 2022
    In:  JMIR mHealth and uHealth Vol. 10, No. 1 ( 2022-1-20), p. e32626-
    Details
    In: JMIR mHealth and uHealth, JMIR Publications Inc., Vol. 10, No. 1 ( 2022-1-20), p. e32626-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2291-5222
    URL: Article
    DOI: 10.2196/32626
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2022
    detail.hit.zdb_id: 2719220-9
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  • 5
    Online Resource
    Online Resource
    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
    Springer Science and Business Media LLC ; 2022
    In:  BMC Medical Informatics and Decision Making Vol. 22, No. 1 ( 2022-12)
    Details
    In: BMC Medical Informatics and Decision Making, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-12)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1472-6947
    URL: Article
    DOI: 10.1186/s12911-022-01918-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2046490-3
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  • 6
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    Online Resource
    Persistence of Use in Children Receiving Growth Hormone Therapy
    The Endocrine Society ; 2021
    In:  Journal of the Endocrine Society Vol. 5, No. Supplement_1 ( 2021-05-03), p. A681-A682
    Details
    In: Journal of the Endocrine Society, The Endocrine Society, Vol. 5, No. Supplement_1 ( 2021-05-03), p. A681-A682
    Abstract: 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 disorders using the easypod™ connected autoinjector e-device, which automatically records adherence data and can transmit them via an online portal (easypod™ connect). We investigated persistence of use, defined indicators of a long persistence of use, and developed a model to predict and identify children at risk of discontinuing treatment in the following 6 months. Data and Methods: Anonymized data from children transmitting over 10 injections between January 2007 and April 2020 were analyzed. A child was considered to discontinue the use if they had no injection in the last 6 months (before April 2020) or had an injection pause of at least 6 consecutive months. Persistence was estimated by Kaplan-Meier analyses and Weibull accelerated failure time modeling. To predict the individual risk of discontinuing the use in the following 6 months, individual survival probabilities curves were estimated for each patient still using the system, and the survival probabilities were then recalculated such that they were conditional on the fact that a child had already used the device for a certain time. The Harrell’s c-index was used to assess the algorithm performance. Results: Data were available for 17,651 children of whom 11,056 discontinued the use and 6,595 were still persistent in April 2020. Median persistence of use for all patients using the device was 2.1 years. There was a highly significant difference in median persistence of use between the regions: 1.0, 1.5 and 2.8 years in the available countries in the Asia-Pacific, America and Europe regions, respectively. Other indicators that had a significant positive impact on persistence of use were: at least one dose change a year, having auxological measurements recorded in the system (and if ‘Yes’, height standard deviation & lt; -2 at start of use had an additional effect), starting treatment at an early age, adherence ≥85%, customized injection speed setting and being male. For the individual prediction, random survival forests showed the best performance (Harrell’s c-index=0.72). Conclusions: Data from the connected autoinjector e-device showed that the persistence of use was approximately 2 years in children with growth disorders. We were able to define 8 indicators that had a positive impact on persistence of use of which several indicators were related to patient management. Our prediction model can be used to identify children needing support to reach longer persistence of use and subsequently optimal clinical outcomes.
    Type of Medium: Online Resource
    ISSN: 2472-1972
    URL: Article
    DOI: 10.1210/jendso/bvab048.1389
    Language: English
    Publisher: The Endocrine Society
    Publication Date: 2021
    detail.hit.zdb_id: 2881023-5
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  • 7
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    Online Resource
    Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method
    Frontiers Media SA ; 2022
    In:  Frontiers in Endocrinology Vol. 13 ( 2022-10-5)
    Details
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 13 ( 2022-10-5)
    Abstract: 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, & lt;-1] and age [3, & lt;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 ( & lt;11 vs ≥11 years) had a small impact on the error SD, with patients born SGA and patients aged & lt;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.
    Type of Medium: Online Resource
    ISSN: 1664-2392
    URL: Article
    DOI: 10.3389/fendo.2022.999077
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2592084-4
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