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.
|Original language||English (US)|
|Title of host publication||Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era - Proceedings of the 2021 EFMI Special Topic Conference|
|Editors||Jaime Delgado, Arriel Benis, Paula de Toledo, Parisis Gallos, Mauro Giacomini, Alicia Martinez-Garcia, Dario Salvi|
|Publisher||IOS Press BV|
|Number of pages||5|
|State||Published - Nov 18 2021|
|Event||2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021 - Virtual, Online, Spain|
Duration: Nov 22 2021 → Nov 24 2021
|Name||Studies in Health Technology and Informatics|
|Conference||2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021|
|Period||11/22/21 → 11/24/21|
Bibliographical noteFunding Information:
This research was made possible thanks to a collaboration between The Netherlands Organization for Applied Scientific Research TNO, Leiden, The Netherlands & Merck Healthcare KGaA, Darmstadt, Germany. MA, JS, and PvD have consultancy agreements with Merck. EK is an employee of Merck Healthcare KGaA, Darmstadt, Germany and holds shares in the company. Editorial assistance was provided by Sinéad Mutton, inScience Communications, Springer Healthcare Ltd, UK, funded by Merck Healthcare KGaA, Darmstadt, Germany.
© 2021 The European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.
- cluster modeling
- growth hormone deficiency
- recombinant human growth hormone
PubMed: MeSH publication types
- Journal Article