THU163 Traffic Light Alerting System For Adherence Risk Management To Improve Catch-Up Growth In Patients Receiving Recombinant Human Growth Hormone Therapy

Abstract

Abstract Disclosure: S. Loche: Advisory Board Member; Self; SL has received advisory board fees from the healthcare business of Merck KGaA, Darmstadt, Germany. Consulting Fee; Self; SL has received consultancy fees from the healthcare business of Merck KGaA, Darmstadt, Germany. Speaker; Self; SL has received lecture fees from the healthcare business of Merck KGaA, Darmstadt, Germany. P. van Dommelen: Consulting Fee; Self; PvD has a consultancy agreement with the healthcare business of Merck KGaA, Darmstadt, Germany. E. Koledova: Employee; Self; EK is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany. Stock Owner; Self; EK holds shares in the healthcare business of Merck KGaA, Darmstadt, Germany. Background: In our previous study, a data-driven clinical decision support system based on “traffic light” visualizations for adherence risk management in patients receiving recombinant human growth hormone (r-hGH) treatment was developed.1 Two “traffic lights” were most promising: mean adherence and standard deviation (SD) of hours to next injection (administering injections at around the same time each day). We aimed to study the effect of these two adherence-based traffic lights on catch-up growth in the first year of treatment in patients with growth hormone deficiency (GHD) and those born small for gestational age (SGA). Data and Methods: Height and adherence data were extracted from the easypod™ connect ecosystem and taken from the easypod™ connect observational study.2 Patients with: height standard deviation scores (HSDS) of ≤-2 at treatment start; age 2-15 years at treatment start; and ≥1 measurement and adherence data available in the first year of treatment, were selected. Mean adherence was classified as high (≥85%), intermediate (>56%-<85%), or low (≤56%); high (≥15.9 [P95]), intermediate (≥10.1 [P85]), or low (<10.1) were used for SD of hours to next injection. Linear regression analysis was performed with ΔHSDS between treatment start and first year of treatment as dependent variables and the adherence-based values as independent variables, all adjusted for age and HSDS at treatment start. Results: In total, data for 1,045 patients (776 and 269 with GHD and those born SGA, respectively) were available. Only some of the adherence-based categories had a significant and independent effect on ΔHSDS (P=0.004). Adjusted for age and HSDS at treatment start, patients with high/intermediate mean adherence had, on average, a +0.15 SD higher ΔHSDS (P=0.004), while this was +0.09 SD for patients with a low SD of hours to the next injection (P=0.003). Therefore, patients with both high/intermediate mean adherence and a low SD of hours to the next injection had, on average, a +0.24 SD higher ΔHSDS compared with patients with both low mean adherence and a high SD of hours to the next injection. Conclusions: Our research shows that good adherence and administering injections around the same time each day play an essential role in optimizing catch-up growth. Adherence-based traffic lights can alert clinicians to have discussions with patients/caregivers to mitigate the risk of sub-optimal adherence and, consequently, improve catch-up growth.