6778 Visual Presentations for Adherence Risk Management In Patients Receiving Recombinant Human Growth Hormone Therapy
Authors: van Dommelen P , Michelis F , Arnaud L , Loche S , Koledova E 
Published: October 2024

Disclosure: P. van Dommelen: Consulting Fee; Self; PvD has a consultancy agreement with Merck KGaA, Darmstadt, Germany. F. Michelis: Employee; Self; FL 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. 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. E. Koledova: Employee; Self; EK is an employee of Merck KGaA,Darmstadt, Germany. Stock Owner; Self; EK holds shares in Merck KGaA, Darmstadt, Germany.

Background: A data-driven clinical decision support system based on visual presentations for adherence risk management can support patients receiving recombinant human growth hormone (r-hGH) treatment. Our focus is on examining adherence patterns during the first 6 weeks of treatment, with a particular emphasis on the days of the week, the variability in injection timings and the summer holiday. Our aim is to assess whether these early patterns can serve as indicators for predicting adherence in the subsequent 7-12 weeks of treatment. 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. Data about sex, age at start treatment, as well as adherence, classified as high (≥95%) versus low/medium &lt;95%), standard deviation (SD) of adherence (%), log (to adjust for skewness) of the interquartile range (IQR) of the timing of injection throughout the day, median timing of the injection after midnight, and ≥50% versus &lt;50% of the injections fall in the summer period (July-August) aggregated by day over the first 6 weeks of treatment were included in a generalized linear mixed model with adherence between 7-12 weeks as outcome. Results: In total, 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 model showed no difference in effect of adherence rates during Monday-Thursday. We, therefore, aggregated the data by weekday (Monday-Thursday) versus weekend day (Friday-Sunday, Friday was included due to the common practice of administering injections during the evening). Adherence, SD of adherence, IQR of timing of injections, and injections falling in summer were significant (p&lt;0.05) predictors of adherence during a weekday and weekend between 7-12 weeks. Areas under the curves were both 0.96 during the weekday and weekend. Conclusions: Our research shows that adherence and timing of injections, as well as taking into account the summer holidays, can predict future adherence accurately. Visual presentations showing aggregated adherence rates and variation of timing of injections by days of the week, as well as highlighting the summer periods may improve adherence risk management and timely support.

Presentation: 6/3/2024