Predicting Treatment Outcome of Patients with Type 2 Diabetes on Once-Daily Basal Insulin Injections using Machine...

Basal insulin has been and remains a common and cost-effective intensification step from insufficient oral antidiabetic drug (OAD) treatment for people with type 2 diabetes (T2D), but individualized intensification alternatives are rapidly increasing. Recently, decision support tools to assist healthcare professionals based on machine learning (ML) algorithms are becoming more popular. By means of ML, the aim of this pilot study is to explore to what extent patient characteristics and continuous glucose monitoring (CGM) data enhance the ability to predict a successful basal insulin treatment outcome beyond what can be predicted based on hemoglobin A1C (HbA1c) alone at treatment initiation. Clinical data were acquired from four different trials with a total of 222 poorly regulated (HbA1c ≥ 7% ) patients with T2D on OAD initiating basal insulin treatment. HbA1c, patient characteristics, and consensus CGM metrics (based on three days) were available and systematically added as input to three classification models, respectively, based on logistic regression and Gaussian process (GP) classification with linear and both linear and nonlinear kernels. Classification models predicted a binarized HbA1c value after six months as either acceptable (HbA1c < 7%) or suboptimal (HbA1c≥7%) using a repeated stratified cross-validation setup. The consensus metrics based on only three days of CGM show a trend towards slightly improved performance when added on top of HbA1c. However, it appears difficult to accurately predict a binarized HbA1c outcome based on the considered patient information to a satisfactory level for clinical use. Future research should consider the outlined limitations associated with this study and suggested considerations for improvement. However, this pilot study can be considered an initial attempt towards leveraging the potential of ML and CGM data for personalised and cost-effective treatment decision-support for basal insulin initiation.