6818 Adherence Risk Management in Patients on Recombinant Human Growth Hormone Therapy, With Traffic Light Alerting Systems Tailored for Both Weekday and Weekend Adherence
Authors: Loche S , van Dommelen P , Tornincasa V , Arnaud L , Koledova E 
Published: October 2024

Abstract
Disclosure: S. Loche: Advisory Board Member; Self; SL has received advisory board fees from Merck KGaA, Darmstadt, Germany. Consulting Fee; Self; SL has received consultancy fees from Merck KGaA, Darmstadt, Germany. Speaker; Self; SL has received lecture fees from Merck KGaA, Darmstadt, Germany. P. van Dommelen: Consulting Fee; Self; PvD has a consultancy agreement with Merck KGaA, Darmstadt, Germany. V. Tornincasa: Employee; Self; VT is an employee of Ares Trading SA, an affiliate of Merck KGaA. L. Arnaud: Employee; Self; LA is an employee of Ares Trading SA, an affiliate of Merck KGaA. E. Koledova: Employee; Self; EK is an employee of Merck KGaA,Darmstadt, Germany. Stock Owner; Self; EK is an employee of Merck KGaA,Darmstadt, Germany.
Background: The integration of data-driven strategies, such as clustering analysis with an alerting system, empowers healthcare professionals with actionable insights. In this study, we focused on clustering adherence patterns during the weekdays and weekends in the first 12 weeks of treatment. Our aim is to develop a data-driven clinical decision support system based on "traffic light" visualizations addressing adherence challenges during both weekdays and weekends in patients receiving Recombinant Human Growth Hormone Therapy. Data and Methods: Adherence data between March-November 2023 were extracted from the newly launched third generation of easypod™ device (EP3) and GrowZen Connect Next ecosystem. EP3 is the only connected device that delivers r-hGH and monitors real-time adherence to therapy. EP3 was perceived by health care professionals as more intuitive, comfortable, user-friendly, simpler, and easier to use than previous EP2. Patients with age 2-18 years at treatment start, a 7-day regimen and complete adherence data available between 1-12 weeks of treatment were selected. Patients’ adherence to therapy was represented using mean and standard deviation (SD) of adherence (%) and the interquartile range (IQR) of the timing of injections throughout the day in minutes aggregated by weekday (Monday-Thursday) and weekend day (Friday-Sunday). 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 create three clusters: high (green), medium (yellow), and low adherence (red). The area under the receiver operating characteristic curve (AUC-ROC) was used to find optimum thresholds for independent traffic lights. Results: Data for 232 patients (124 boys and 108 girls, median (Q25-Q75) age at treatment start was 10.5 (7.6-12.3)) were available. The most appropriate traffic light used the SD of adherence during the weekend, with an AUC-ROC value of 0.87. For the weekday, adherence-based traffic lights using optimum thresholds were &gt;98% (SD&lt;10.2), 88-98% (SD:10.2-22.8) and &lt;88% (SD&gt;22.8) for high, medium, and low adherence, respectively. For IQR of the timing of injections throughout the weekday, optimum thresholds were &lt;38, 38-70 and &gt;70 (min). For the weekend, optimum thresholds were &gt;96% (SD&lt;7.7), 86-96% (SD:7.7-21.5) and &lt;86% (SD&gt;21.5). For IQR of the timing of injections throughout the weekend, optimum thresholds were &lt;56, 56-84 and &gt;84 (min). Conclusions: Our research indicates that implementing a practical data-driven alert system, utilizing our proposed traffic-light coding, would empower healthcare practitioners to monitor patients' adherence to growth hormone therapy comprehensively. By understanding patient behavior during the weekdays and weekend, healthcare practitioners can tailor interventions for improved treatment outcomes.
Presentation: 6/3/2024