Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes

In type 2 diabetes (T2D) treatment, finding a safe and effective basal insulin dose is a challenge. The dose-response is highly individual and to ensure safety, people with T2D titrate by slowly increasing the daily insulin dose to meet treatment targets. This titration can take months. To ease and accelerate the process, we use short-term artificial pancreas (AP) treatment tailored for initial titration and apply it as a diagnostic tool. Specifically, we present a method to automatically estimate a personalized daily dose of basal insulin from closed-loop data collected with an AP. Based on AP-data from a stochastic simulation model, we employ the continuous-discrete extended Kalman filter and a maximum likelihood approach to estimate parameters in a simple dose-response model for 100 virtual people. With the identified model, we compute a daily dose of basal insulin to meet treatment targets for each individual. We test the personalized dose and evaluate the treatment outcomes against clinical reference values. In the tested simulation setup, the proposed method is feasible. However, more extensive tests will reveal whether it can be deemed safe for clinical implementation.Comment: 6 pages, 4 figures, 1 table. Accepted for publication in Proceedings of the 2022 61st IEEE Conference on Decision and Control (CDC

Leveraging Artificial Pancreas Technology for Treatment Optimization in T2D

In type 2 diabetes (T2D), injections with long-acting insulin can become necessary to normalize blood glucose and avoid long-term complications. However, finding a safe and effective insulin dose, a process known as titration, is both challenging and time demanding. In this thesis, we propose a new titration method for swift and safe identification of a personalized insulin dose with long-acting insulin through short-term use of rapid-acting insulin in an artificial pancreas (AP).We augment a published T2D model to simulate an AP driving the blood glucose into the clinical target range followed by a switch to injections with long-acting insulin. In simulation, the new titration method can reduce the titration period to a single week, compared to five weeks on standard-of-care titration. To explore how to best switch between rapid- and long-acting insulin, we use clinical trial data to assess the correlation between the insulin response to rapid- and long-acting insulin injections in the same individual. In an in silico cohort of a hundred people with T2D, we investigate how differences in bioavailability may influence the conversion from rapid-acting insulin delivered in a pump to an equivalent injection dose of long-acting insulin. The cohort simulation reveals that many individuals need more than one week of AP treatment to reach the clinical target range.As an alternative to letting an AP drive the blood glucose into the target range, we explore how to predict a safe and effective long-acting insulin dose from 24 to 48 hours of AP data. With simulated AP data, we estimate parameters in dose-response models using maximum likelihood estimation (MLE). We apply the continuous-discrete extended Kalman filter (CDEKF) to approximate the likelihood function which is maximized in MLE. To improve the model-based dose predictions, we apply model-based design of experiment (MBDoE) and determine how to best run an AP system to collect data for parameter estimation. Finally, we obtain personalized dose-response models from the experimental data and evaluate their ability to predict a safe and effective insulin dose for each simulated individual.In simulation, the proposed method is feasible. However, the efficacy and safety of the dose estimates heavily depend on the level of system excitation. The results indicate that MBDoE holds a potential to improve the performance of model-based dose-guidance solutions. Still, without clinical data, it is not possible to conclude on the clinical feasibility of a translating between pump- and pen-based treatment in T2D. In the future, commercial AP systems may enable clinical evaluation of the new titration method.<br/

From Optimal Design of Experiment to Safe System Identification in Type 2 Diabetes

Model-based design of experiment (MBDoE) provides a framework to collect informative data for system identification. However, a parametric and structural mismatch between the design model and the underlying physical system can lead to hazardous experiments in safety critical systems. In this work, we present a method to safely improve system identification based on insights from a model-based optimal experimental design. From a visual inspection of a MBDoE, we select an approximated output curve fulfilling system constraints as a reference for the physical system. To avoid open-loop implementation of the MBDoE, we use our approximated reference together with a reference-tracking controller to collect experimental data in closed-loop. In this type 2 diabetes (T2D) case study, the proposed design method is safe and provides informative experimental data for system identification.</p