Intermittent Control for Safe Long-Acting Insulin Intensification for Type 2 Diabetes: In-Silico Experiment
Anas El Fathi, Mohammadreza Ganji, Dimitri Boiroux, Henrik Bengtsson,, Marc D. Breton

TL;DR
This study introduces an adaptive control strategy for insulin titration in type 2 diabetes, demonstrating in-silico that it achieves faster and safer glycemic control compared to standard methods, with robustness to measurement variability.
Contribution
The paper presents a novel receding horizon control algorithm for insulin titration, improving speed and safety of glycemic management in T2D through in-silico validation.
Findings
Achieves target glucose levels faster (by week 8) than standard care.
Maintains safety with low hypoglycemia risk over 52 weeks.
Robust to missing fasting blood glucose measurements.
Abstract
Around a third of type 2 diabetes patients (T2D) are escalated to basal insulin injections. Basal insulin dose is titrated to achieve a tight glycemic target without undue hypoglycemic risk. In the standard of care (SoC), titration is based on intermittent fasting blood glucose (FBG) measurements. Lack of adherence and the day-to-day variabilities in FBG measurements are limiting factors to the existing insulin titration procedure. We propose an adaptive receding horizon control strategy where a glucose-insulin fasting model is identified and used to predict the optimal basal insulin dose. This algorithm is evaluated in \textit{in-silico} experiments using the new UVA virtual lab (UVlab) and a set of T2D avatars matched to clinical data (NCT01336023). Compared to SoC, we show that this control strategy can achieve the same glucose targets faster (as soon as week 8) and safer (increased…
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Taxonomy
TopicsDiabetes Management and Research · Diabetes Treatment and Management · Pancreatic function and diabetes
