Title
A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems
Author
Araujo, M.
van Dommelen, P.
Srivastava, J.
Koledova, E.
Publication year
2021
Abstract
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,
Subject
Adherence
Cluster modeling
Growth hormone deficiency
Pediatrics
Recombinant human growth hormone
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http://resolver.tudelft.nl/uuid:c2d0531f-2f22-41f4-bdc3-50f2210f9eb0
DOI
https://doi.org/10.3233/shti210803
TNO identifier
961085
Source
Studies in Health Technology and Informatics, 2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021, 22 November 2021 through 24 November 2021, 287 (287), 23-27
Document type
article