Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems

BACKGROUND

Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system.

METHOD

The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range.

RESULTS

Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state.

CONCLUSIONS

The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.

A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas

BACKGROUND AND OBJECTIVES

Complete identification of the glucose dynamics for a patient generally requires prior clinical procedures and several measurements for the patient. However, these steps may not be always feasible. To address this limitation, we propose a practical approach integrating learning-based model predictive control (MPC), adaptive basal and bolus injections, and suspension with minimal requirements of prior knowledge of the patient.

METHODS

The glucose dynamic system matrices were periodically updated using only input values, without any pretrained models. The optimal insulin dose was calculated based on a learning-based MPC algorithm. Meal detection and estimation modules were also introduced. The basal and bolus insulin injections were fine-tuned using the performance of glucose control from the previous day. To validate the proposed method, evaluations with 20 virtual patients from a type 1 diabetes metabolic simulator were employed.

RESULTS

Time-in-range (TIR) and time-below-range (TBR) were 90.8% (84.1% - 95.6%) and 0.3% (0% - 0.8%), as represented by the median, first (Q1), and third quartiles (Q3), respectively, when meal intakes were fully announced. When one out of three meal intake announcements was missing, TIR and TBR were 85.2% (75.0% - 88.9%) and 0.9% (0.4% - 1.1%), respectively.

CONCLUSIONS

The proposed approach obviates the need for prior tests from patients and shows effective regulation of blood glucose levels. From the perspective of practical implementation in clinical environments, to deal with minimal prior information of the patient, our study demonstrates how essential clinical knowledge and learning-based modules can be integrated into a control framework for an artificial pancreas.

Optimized type-2 fuzzy controller based on IoMT for stabilizing the glucose level in type-1 diabetic patients

Due to advancements in existing Internet of Medical Things (IoMT) systems and devices, the blood glucose level (BGL) for type-1 diabetic patients (T1DPs) is effectively and continually monitored and controlled by Artificial Pancreas. Because the regulation of BGL is a very complex process, many efforts have been conducted to design a powerful and effective controller for the exogenous insulin infusion system. The main objective of this study is to propose an optimized interval type-2 fuzzy (IT2F) based controller of artificial pancreas for regulation BGL of T1DP based on IoMT. The proposed controller should avoid the risk of hyperglycemia and hypoglycemia situations that T1DP faces during the infusion of exogenous insulin. The main contribution of this work is using meta-heuristic method called grey wolf optimizer (GWO) to tune the footprint of uncertainty for IT2F's membership functions to inject the proper dose of insulin under different conditions. The nonlinear extended Bergman minimal model (EBMM) with uncertainty is used to represent the blood glucose regulation and represent the dynamics of meal disturbance in T1DP. The effectiveness and the performance of the proposed controller are investigated using MATLAB/Simulink platform. Simulation results show that the proposed controller can avoid both severe hypoglycemia and hyperglycemia for nominal parameters of the model, in addition to model under the presence of both parametric uncertainty and uncertain meal disturbance.

Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control

We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.

Clinically-Similar Clusters of Daily CGM Profiles: Tracking the Progression of Glycemic Control Over Time

BACKGROUND

The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous.

METHODS

CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs.

RESULTS

The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time.

CONCLUSION

Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.

The Use of Extreme Value Statistics to Characterize Blood Glucose Curves and Patient Level Risk Assessment of Patients With Type I Diabetes

OBJECTIVE

Characterizing blood glucose curves and providing precise patient level risk assessment of hyperglycemia using extreme value statistics and comparing these assessments with traditional indicators of glycemic variability which are not designed to specifically capture the risk of hyperglycemia.

RESEARCH DESIGN AND METHODS

One year return level (blood glucose level exceeded exactly once every year on average) and probability of exceeding and expected time spent above certain thresholds (600 and 400 mg/dL) per year were calculated. As a comparison, traditional metrics for glycemic variability were determined too. The effect of body mass index on extremes was also investigated using non-stationary models. Metrics were calculated on a dataset containing 170.8 patient-years of measurements of 226 patients.

RESULTS

Nine high-risk patients were identified with the novel metrics: their estimated time spent above 600 mg/dL per year were above 2 hours. These patients were at moderate risk according to the traditional metrics. Higher body mass index was associated with more extreme glucose levels.

CONCLUSIONS

Through these estimates it is possible to assess each patient's individual clinical risk of hyperglycemia even beyond the observed blood glucose levels and detection limits. Additionally, it allows the assessment of the impact of clinical characteristics and treatments on blood glucose control in a novel, mathematically well-founded and potentially clinically more useful way than the already existing indicators.

Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes

This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements.

AGATA: A Toolbox for Automated Glucose Data Analysis

BACKGROUND

Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.

METHODS

Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis.

RESULTS

Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.

CONCLUSION

Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.

Practical constraint definition in safety schemes for artificial pancreas systems

Introduction:

Artificial pancreas systems usually define an insulin-on-board constraint ([Formula: see text]) for safety schemes to limit the insulin infusion and avoid hypoglycemia during the closed-loop performance. Several methods have been proposed with impractical considerations requiring information from the prandial events or complex procedures for ambulatory use.

Methods:

This paper presents a simple method that consists of two novel rules that allow finding an [Formula: see text] based only on common clinical parameters that do not require patient intervention. The method robustness was evaluated using a control system coupled to a safety layer under demanding scenarios implemented on the FDA-approved simulator for preclinical studies.

Results:

The method maintains a safe performance, even in the face of interpatient variability, hybrid and fully automatic implementations of an artificial pancreas system, and uncertain settings. Both proposed rules work as effectively or even better and without the patient intervention than other methods that have already been clinically validated.

Conclusion:

This method can be used to define a constant [Formula: see text] that ensures performance and safety of the control system, even under scenarios with incorrect clinical data. Unlike other methods, this method only requires reliable information that is easily obtained from the patient, such as their total daily dose of insulin or body mass.

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (-4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to [Formula: see text] with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.

Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes

Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes

OBJECTIVE

In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements.

METHODS

Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements.

RESULTS

Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean μ = [50, 75, 75] grams and standard deviation σ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) % to 93.6(6.7) %, p = 0.02.

CONCLUSIONS

Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets.

SIGNIFICANCE

Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70-180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.

In Silico Examination of Initiation of Long-Acting Insulin Analogs Toujeo Compared to Lantus Under 3 Dosing Titration Rules in Virtual Type 2 Diabetes Subjects

BACKGROUND

Despite the benefits and clinical necessity of insulin treatment in type 2 diabetes (T2D), healthcare providers are reluctant to initiate insulin, and patients are reluctant to start it for several reasons, one of these being the complexity of insulin treatment. Patients and their healthcare providers can benefit from titration algorithms (TAs) or rules that assist with the initiation and titration of insulin, performing the calculations that are needed to safely initiate and conservatively adjust.

METHODS

The primary objective for this in silico study was to examine the effectiveness of 3 dose TAs (1-3) for optimization of basal insulin glargine (Gla-100 and Gla-300). In the simulations, 100 virtual subjects with T2D were included (50% men, age 62 ± 3 years, HbA1c 8.1% ± 2.9%, body weight 94 ± 16 kg). Subjects were studied under each TA (TA1 and TA2 fasting blood glucose [FBG] targets 90-130 mg/dL, TA3 FBG target 110-150 mg/dL). Initial dose of both insulins was based on 0.2 U/kg body weight. During 3 months, subjects reported their FBG to the LTHome web-based dose guidance system with a rules engine to safely guide long-acting insulin titration and maintenance. Subjects followed dose recommendations to reach designated FBG target ranges.

RESULTS

All subjects reached stable doses under all TAs with both Gla-100 and Gla-300 insulin, and 93 or more of the 100 subjects, depending on the assigned TA, achieved the target FBG range within the 3-month simulation for all TAs. Mean FBG was lowered (Gla-100: 155 ± 40 to 118 ± 11 mg/dL with TA1 and TA2 and 132 ± 12 mg/dL for TA3; Gla-300: 125 ± 14 with TA1 and TA2 and 134 ± 15 mg/dL with TA3). Calculated HbA1c improved from 8.1% ± 2.9% to 7.1% ± 2.5% for TA1 and TA2 and 7.5% ± 2.5% for TA3, a reduction of 0.9% and 0.6% over 3 months for both insulins. Three subjects on Gla-100 and one subject on Gla-300 experienced mild hypoglycemia.

CONCLUSION

All TAs delivered safe dose recommendations with minimal hypoglycemia, leading to a stable glucose control in the majority of subjects.

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ( S_I) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients' safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients' safety, which reveals the feasibility of the approach to be included in different control algorithms.

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method

This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra‐patient parametric variation and non‐availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed‐loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post‐prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed‐loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post‐prandial hyperglycemia are significantly reduced in the presence of bounded (±30% ) parametric variability and uncertain exogenous meal disturbance.

Design of an online-tuned model based compound controller for a fully automated artificial pancreas

This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.

Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps

BACKGROUND AND OBJECTIVE

Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing.

METHODS

The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided.

RESULTS

The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75g meal: 81.9 ± 15.59 vs. 89.51 ± 11.95, ρ ≃ 0; 100g meal: 75.12 ± 18.23 vs. 85.46 ± 14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ± 14.48 vs. 0.97 ± 4.15, ρ=0.008; 100g meal: 9.5 ± 17.02 vs. 1.85 ± 7.05, ρ=0.014). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ± 14.48 vs. 4.21 ± 6.18, ρ=0.028) and improved the time in euglycemia (79.46 ± 17.46 vs. 86.29 ± 11.73, ρ=0.007).

CONCLUSIONS

The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.

Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors' previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.

Assessment of Mitigation Methods to Reduce the Risk of Hypoglycemia for Announced Exercise in a Uni-hormonal Artificial Pancreas

BACKGROUND

Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting.

METHODS

A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period.

RESULTS

The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P < 0.01) and from 41.3% to 0.0% (P < 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced.

CONCLUSIONS

The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter

BACKGROUND

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden.

METHODS

The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory.

RESULTS

The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs.

CONCLUSIONS

The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Metrics for glycaemic control - from HbA1c to continuous glucose monitoring

As intensive treatment to lower levels of HbA_1c characteristically results in an increased risk of hypoglycaemia, patients with diabetes mellitus face a life-long optimization problem to reduce average levels of glycaemia and postprandial hyperglycaemia while simultaneously avoiding hypoglycaemia. This optimization can only be achieved in the context of lowering glucose variability. In this Review, I discuss topics that are related to the assessment, quantification and optimal control of glucose fluctuations in diabetes mellitus. I focus on markers of average glycaemia and the utility and/or shortcomings of HbA_1c as a 'gold-standard' metric of glycaemic control; the notion that glucose variability is characterized by two principal dimensions, amplitude and time; measures of glucose variability that are based on either self-monitoring of blood glucose data or continuous glucose monitoring (CGM); and the control of average glycaemia and glucose variability through the use of pharmacological agents or closed-loop control systems commonly referred to as the 'artificial pancreas'. I conclude that HbA_1c and the various available metrics of glucose variability reflect the management of diabetes mellitus on different timescales, ranging from months (for HbA_1c) to minutes (for CGM). Comprehensive assessment of the dynamics of glycaemic fluctuations is therefore crucial for providing accurate and complete information to the patient, physician, automated decision-support or artificial-pancreas system.

Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. AUTOMATICA : THE JOURNAL OF IFAC, THE INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL 2016;71:237-246. [PMID: 27695131 PMCID: PMC5040369 DOI: 10.1016/j.automatica.2016.04.015]

A novel Model Predictive Control (MPC) law for an Artificial Pancreas (AP) to automatically deliver insulin to people with type 1 diabetes is proposed. The MPC law is an enhancement of the authors' zone-MPC approach that has successfully been trialled in-clinic, and targets the safe outpatient deployment of an AP. The MPC law controls blood-glucose levels to a diurnally time-dependent zone, and enforces diurnal, hard input constraints. The main algorithmic novelty is the use of asymmetric input costs in the MPC problem's objective function. This improves safety by facilitating the independent design of the controller's responses to hyperglycemia and hypoglycemia. The proposed controller performs predictive pump-suspension in the face of impending hypoglycemia, and subsequent predictive pump-resumption, based only on clinical needs and feedback. The proposed MPC strategy's benefits are demonstrated by in-silico studies as well as highlights from a US Food and Drug Administration approved clinical trial in which 32 subjects each completed two 25 hour closed-loop sessions employing the proposed MPC law.

Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies

BACKGROUND

Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type 1 diabetes mellitus (T1DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals.

METHODS

A switched LPV controller that switches between 3 LPV controllers, each with a different level of aggressiveness, is designed to further cope with both unannounced meals and postprandial PA. Thus, the proposed control strategy has a "standard" mode, an "aggressive" mode, and a "conservative" mode. The "standard" mode is designed to be applied most of the time, while the "aggressive" mode is designed to deal only with hyperglycemia situations. On the other hand, the "conservative" mode is focused on postprandial PA control.

RESULTS

An ad hoc simulator has been developed to test the proposed controller. This simulator is based on the distribution version of the UVA/Padova model and includes the effect of PA based on Schiavon.(1) The test results obtained when using this simulator indicate that the proposed control law substantially reduces the risk of hypoglycemia with the conservative strategy, while the risk of hyperglycemia is scarcely affected.

CONCLUSIONS

It is demonstrated that the announcement, or anticipation, of exercise is indispensable for letting a mono-hormonal artificial pancreas deal with the consequences of postprandial PA. In view of this the proposed controller allows switching into a conservative mode when notified of PA by the user.

Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement-we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers-frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the "artificial pancreas." Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components-amplitude and timing.

Tackling problem nonlinearities & delays via asymmetric, state-dependent objective costs in MPC of an artificial pancreas

The design of a Model Predictive Control (MPC) law for an Artificial Pancreas (AP) that automatically delivers insulin to people with type 1 diabetes mellitus is considered. An MPC law was recently proposed that exploits the simplicity of linear dynamical models, but is in two ways a 'nonlinear' departure of standard linear MPC, while circumnavigating the complexity of cumbersome, fully nonlinear MPC approaches. The first of two issues focused on is the nonlinearity of the control problem, and it is demonstrated how this can be tackled via asymmetric objective functions. The second issue is controller induced hypoglycemia resulting from the large delay in actuation and sensing. The proposed MPC strategy employs an asymmetric, state-dependent objective function that leads to a nonlinear optimization problem. The result is an AP controller with significantly elevated safety and comparable control performance. The contribution of this paper is a detailed in-silico analysis of the proposed control law, and a clinical demonstration of the benefits of asymmetric objective functions.

Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.