Title
A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy
Author
Spataru, A.
van Dommelen, P.
Arnaud, L.
Le Masne, Q.
Quarteroni, S.
Koledova, E.B.
Publication year
2021
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 (
Subject
Growth
Hormone
Therapy
Children
Human growth hormone
Models
Machine learning
To reference this document use:
http://resolver.tudelft.nl/uuid:7b025094-5d1b-45bb-97a4-e5d0d79968a2
DOI
https://doi.org/10.1210/jendso/bvab048.1371
TNO identifier
956064
Source
Journal of the Endocrine Society, 5 (5), A672-A673
Document type
article