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

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Boman , N , Fernandez-Luque , L , Koledova , E , Kause , M & Lapatto , R 2021 , ' 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 ' , BMC Medical Informatics and Decision Making , vol. 21 , no. 1 , 136 . https://doi.org/10.1186/s12911-021-01491-0

Title: 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
Author: Boman, N; Fernandez-Luque, L; Koledova, E; Kause, M; Lapatto, R
Contributor organization: CAMM - Research Program for Clinical and Molecular Metabolism
Children's Hospital
Clinicum
HUS Children and Adolescents
Date: 2021-04-26
Language: eng
Number of pages: 12
Belongs to series: BMC Medical Informatics and Decision Making
ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-021-01491-0
URI: http://hdl.handle.net/10138/336621
Abstract: 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 (TM) 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 (TM) 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 (TM) 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 (similar to 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 (TM) device.
Subject: EHealth
Adherence monitoring
Growth outcomes
Observational study
ADULT HEIGHT
TREATMENT ADHERENCE
AUTO-INJECTOR
LONG-TERM
CHILDREN
THERAPY
NONADHERENCE
ACCEPTANCE
DEFICIENCY
MANAGEMENT
3123 Gynaecology and paediatrics
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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