A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy

Abstract

\n Background: Suboptimal adherence to recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes. Being able to identify children who are at risk of suboptimal adherence in the near future, and take adequate measures to support adherence, may maximize clinical outcomes. Our aim was to develop a model based on data from the first 3 months of treatment to identify potential indicators of suboptimal adherence and predict adherence over the following 9 months using a machine learning approach.\n Methods: We assessed adherence to r-hGH treatment in children with growth disorders in their first 12 months of treatment using a connected autoinjector and e-device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect). We selected children who started the use of the device before 18 years of age and who transmitted their injection data for at least 12 months. Adherence (mg injected/mg prescribed) between 4-12 months (outcome) was categorized as optimal (≥85%) versus suboptimal (<85%). In addition to adherence over the first 3 months, comfort settings (needle speed, injection depth, injection speed, injection time), number of transmissions, number of dose changes, age at start and sex were used as potential indicators of suboptimal adherence. Several machine learning models were optimized on a class-balanced training dataset using a 5-fold cross-validation scheme. On the best performing model, machine learning interpretation techniques and chi-squared statistical tests were applied to extract the statistically significant indicators of suboptimal and optimal adherence. Results: Anonymized data were available for 10,943 children. The optimal prediction performances were achieved with the random forest algorithm. The mean adherence and the adherence standard deviation over the first 3 months were the two most important features for predicting adherence in the following 9 months. Not using the system’s features (e.g. not transmitting data often and not changing some of the comfort settings, such as the needle speed setting), as well as starting treatment at an older age were significantly associated with an increased risk of suboptimal adherence (p<0.001). When tested on first-time seen data following the same class distribution as the original data, the model achieved a sensitivity of 80% and a specificity of 81%.\n Conclusions: We developed a model predicting whether a child’s adherence in the following 9 months will be below or above the optimal threshold (85%) based on early data from the first 3 months of treatment and we identified the indicators of suboptimal adherence. These results can be used to identify children needing additional medical or other support to reach optimal adherence and therefore optimal clinical outcomes.