Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control

Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions

BACKGROUND

Automated insulin delivery (AID) systems, permit improved treatment of type 1 diabetes (T1D). Unfortunately, malfunctioning in the insulin pump or in the infusion set can prevent insulin from being administered, reducing the AID efficacy and posing the patient at risk. Different data-driven methods available in the literature can be used to deal with the problem of automatically detecting complete insulin suspension in real-time. This article investigates both supervised and unsupervised strategies and proposes a fair comparison under either population or personalized settings.

METHODS

Several algorithms are compared using data generated through the UVA/Padova T1D simulator, a computer simulator widely used to test control strategies in silico and accepted by the Food and Drugs Administration (FDA) as a substitute to animal pre-clinical trials. Two synthetic data sets, each consisting of 100 virtual subjects monitored for 1 month, were generated. Occasional faults of the insulin pump are simulated as complete occlusions by suspending the therapy administration. Personalized algorithms are investigated with unsupervised approaches only, since personalized labels are hardly available.

RESULTS

In the population scenario, the supervised approach outperforms the unsupervised strategy. In particular, logistic regression and random forest achieves a recall of 72% and 82%, with 0.12 and 0.21 false positives (FP) per day, respectively. In the personalized setting scenario, the unsupervised algorithms are tailored on each patient and outperform the population ones, in particular isolation forest achieves a recall 80% and 0.06 FPs per day.

CONCLUSIONS

This article suggests that unsupervised personalized approach, by addressing the large variability in glucose response among individuals with T1D, is superior to other one-fits-all approaches in detecting insulin suspensions caused by malfunctioning. Population methodologies can be effectively used while waiting to collect sufficient patient data, when the system is installed on a new patient.

Closed-loop control of continuous piperacillin delivery: An in silico study

Background and objective: Sub-therapeutic dosing of piperacillin-tazobactam in critically-ill patients is associated with poor clinical outcomes and may promote the emergence of drug-resistant infections. In this paper, an in silico investigation of whether closed-loop control can improve pharmacokinetic-pharmacodynamic (PK-PD) target attainment is described.

Method: An in silico platform was developed using PK data from 20 critically-ill patients receiving piperacillin-tazobactam where serum and tissue interstitial fluid (ISF) PK were defined. Intra-day variability on renal clearance, ISF sensor error, and infusion constraints were taken into account. Proportional-integral-derivative (PID) control was selected for drug delivery modulation. Dose adjustment was made based on ISF sensor data with a 30-min sampling period, targeting a serum piperacillin concentration between 32 and 64 mg/L. A single tuning parameter set was employed across the virtual population. The PID controller was compared to standard therapy, including bolus and continuous infusion of piperacillin-tazobactam.

Results: Despite significant inter-subject and simulated intra-day PK variability and sensor error, PID demonstrated a significant improvement in target attainment compared to traditional bolus and continuous infusion approaches.

Conclusion: A PID controller driven by ISF drug concentration measurements has the potential to precisely deliver piperacillin-tazobactam in critically-ill patients undergoing treatment for sepsis.

Subcutaneous insulin administration by deep reinforcement learning for blood glucose level control of type-2 diabetic patients

BACKGROUND

Type-2 diabetes mellitus is characterized by insulin resistance and impaired insulin secretion in the human body. Many endeavors have been made in terms of controlling and reducing blood glucose via the medium of automated controlling tools to increase precision and efficiency and reduce human error. Recently, reinforcement learning algorithms are proved to be powerful in the field of intelligent control, which was the motivation for the current study.

METHODS

For the first time, a reinforcement algorithm called normalized advantage function (NAF) algorithm has been applied as a model-free reinforcement learning method to regulate the blood glucose level of type-2 diabetic patients through subcutaneous injection. The algorithm has been designed and developed in a model-free approach to avoid additional inaccuracies and parameter uncertainty introduced by the mathematical models of the glucoregulatory system. Insulin doses constitute the control action that is designed to be stated directly in clinical language with the unit IU. In this regard, a new environment state is considered in addition to the glucose level to take into account the delayed effect of insulin elimination under the skin. Finally, a simple but practical reward function is developed to be used with the NAF algorithm to correct the glucose level and maintain it in the desired range.

RESULTS

The simulation environment was set up to imitate the basal-bolus process accurately. Results for 30 days of simulation of the designed controller on three different average virtual patients verify the feasibility and effectiveness of the method and reveal our proposed controller's learning ability. Moreover, as the insulin elimination dynamic was taken into account, a more complete and more realistic model than the previously studied models has emerged.

CONCLUSION

NAF has proved a promising control approach, able to successfully regulate and significantly reduce the fluctuation of the blood glucose without meal announcements, compared to standard optimized open-loop basal-bolus therapies. The method and its results, which are directly in the clinical language, are applicable in real-time clinical situations.

Diabetes Technology Meeting 2021

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation

Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry is manual which adds cognitive burden to the persons living with T1D. In this study, we proposed a control system based on Proximal Policy Optimisation (PPO) that controls both basal and bolus insulin infusion and only requires meal announcement, thus eliminating the need for carbohydrate estimation. We evaluated the system on a challenging meal scenario, using an open-source simulator based on the UVA/Padova 2008 model and achieved a mean Time in Range value of 65% for the adult subject cohort, while maintaining a moderate hypoglycemic and hyperglycemic risk profile. The approach shows promise and welcomes further research towards the translation to a real-life artificial pancreas. Clinical relevance- This was an in-silico analysis towards the development of an autonomous artificial pancreas system for glucose control. The proposed system show promise in eliminating the need for estimating the carbohydrate content in meals.

A Dual-Hormone Multicontroller for Artificial Pancreas Systems

Artificial pancreas (AP) algorithms can be divided into single-hormone (SH) and dual-hormone (DH). SH algorithms regulate glycemia using insulin as their control input. On the other hand, DH algorithms also use glucagon to counteract insulin. While SH-AP systems are already commercially available, DH-AP systems are still in an earlier research phase. DH-AP systems have been questioned since the added complexity of glucagon infusion does not always guarantee hypoglycemia prevention and might significantly raise insulin delivery. In this work, a DH multicontroller is proposed based on a SH linear quadratic gaussian (LQG) algorithm with an additional LQG controller to deliver glucagon. This strategy has a switched structure that allows activating one of the following three controllers when necessary: a conservative insulin LQG controller to modulate basal delivery ( K₁), an aggressive insulin LQG controller to counteract meals ( K₂), or a glucagon LQG controller to avoid imminent hypoglycemia ( K₃). Here, an in silico study of the benefits of incorporating controller K₃ is carried out. Intra-patient variability and mixed meals are considered. Results indicate that the proposed switched, dual-hormone strategy yields to a reduction in hypoglycemia without increasing hyperglycemia, with no significant rise in insulin delivery.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms

BACKGROUND

User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available.

METHODS

The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator.

RESULTS

Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%).

CONCLUSIONS

Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient's response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.

The Closed-Loop System Improved the Control of a Pregnant Patient with Type 1 Diabetes Mellitus

Objective

Closed-loop insulin systems represent a technological advance in diabetes management but have rarely been studied in pregnancy. We report a case of a patient with type 1 diabetes mellitus who was previously a user of the Paradigm VEO pump and then migrated to Medtronic 670G. Research Design and Methods. We reviewed the case of a G1P0 patient with type 1 diabetes, treated with the Medtronic 670G system during pregnancy; a comparison with current literature was done.

Results

The patient achieved improved glycemic control as measured by time spent in different ranges as follows: <70 mg/dL, 8–4% and 70–180 mg/dL, 83–94%. Secondary outcomes included reduction of stress, anxiety, fear of hypoglycemia, and the psychological burden related to the disease. There were no obstetric or neonatal complications.

Conclusion

The Medtronic 670G closed-loop system was used safely in a pregnant woman; nevertheless, further research is needed to validate this system in this patient population.

Glucagon as a Therapeutic Approach to Severe Hypoglycemia: After 100 Years, Is It Still the Antidote of Insulin?

Hypoglycemia represents a dark and tormented side of diabetes mellitus therapy. Patients treated with insulin or drug inducing hypoglycemia, consider hypoglycemia as a harmful element, which leads to their resistance and lack of acceptance of the pathology and relative therapies. Severe hypoglycemia, in itself, is a risk for patients and relatives. The possibility to have novel strategies and scientific knowledge concerning hypoglycemia could represent an enormous benefit. Novel available glucagon formulations, even now, allow clinicians to deal with hypoglycemia differently with respect to past years. Novel scientific evidence leads to advances concerning physiopathological mechanisms that regulated glycemic homeostasis. In this review, we will try to show some of the important aspects of this field.

Performance Analysis of Different Embedded Systems and Open-Source Optimization Packages Towards an Impulsive MPC Artificial Pancreas

Current technological advances have brought closer to reality the project of a safe, portable, and efficient artificial pancreas for people with type 1 diabetes (T1D). Among the developed control strategies for T1D, model predictive control (MPC) has been emphasized in literature as a promising control for glucose regulation. However, these control strategies are commonly designed in a computer environment, regardless of the limitations of a portable device. In this paper, the performances of six embedded platforms and three open-source optimization solver algorithms are assessed for T1D treatment. Their advantages and limitations are clarified using four MPC formulations of increasing complexity and a hardware-in-the-loop methodology to evaluate glucose control in virtual adult subjects. The performance comparison includes the execution time, the difference concerning the evolution obtained in MATLAB, the processor temperature, energy consumption, time percentage in normoglycemia, and the number of hypo- and hyperglycemic events. Results show that Quadprog is the package that faithfully follows the results obtained with control strategies designed and tuned on a computer with the MATLAB software. In addition, the Raspberry Pi 3 and the Tinker Board S embedded systems present the appropriate characteristics to be implemented as portable devices in the artificial pancreas application according to the criteria set out in this work.

Continuous glucose monitoring for the prevention of morbidity and mortality in preterm infants

BACKGROUND

Preterm infants are susceptible to hyperglycemia and hypoglycemia, conditions which may lead to adverse neurodevelopment. The use of continuous glucose monitoring devices (CGM) might help keeping glucose levels in the normal range, and reduce the need for blood sampling. However, the use of CGM might be associated with harms in the preterm infant.

OBJECTIVES

Objective one: to assess the benefits and harms of CGM alone versus standard method of glycemic measure in preterm infants. Objective two: to assess the benefits and harms of CGM with automated algorithm versus standard method of glycemic measure in preterm infants. Objective three: to assess the benefits and harms of CGM with automated algorithm versus CGM without automated algorithm in preterm infants.

SEARCH METHODS

We adopted the standard search strategy of Cochrane Neonatal to search the Cochrane Central Register of Controlled Trials (CENTRAL; 2020, Issue 9), in the Cochrane Library; MEDLINE via PubMed (1966 to 25 September 2020); Embase (1980 to 25 September 2020); and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (1982 to 25 September 2020). We also searched clinical trials databases, conference proceedings, and reference lists of retrieved articles for randomized controlled trials and quasi-randomized trials.

SELECTION CRITERIA

Randomized controlled trials (RCTs) and quasi-RCTs in preterm infants comparing: 1) the use of CGM versus intermittent modalities to measure glycemia (comparison 1); or CGM associated with prespecified interventions to correct hypoglycemia or hyperglycemia versus CGM without such prespecified interventions (comparison 2).

DATA COLLECTION AND ANALYSIS

We assessed the methodological quality of included trials using Cochrane Effective Practice and Organisation of Care Group (EPOC) criteria (assessing randomization, blinding, loss to follow-up, and handling of outcome data). We evaluated treatment effects using a fixed-effect model with risk ratio (RR) for categorical data and mean, standard deviation (SD), and mean difference (MD) for continuous data. We used the GRADE approach to assess the certainty of the evidence.

MAIN RESULTS

Four trials enrolling 138 infants met our inclusion criteria. Investigators in three trials (118 infants) compared the use of CGM to intermittent modalities (comparison one); however one of these trials was analyzed separately because CGM was used as a standalone device, without being coupled to a control algorithm like in the other trials. A fourth trial (20 infants) assessed CGM with an automated algorithm versus CGM with a manual algorithm. None of the four included trials reported the neurodevelopmental outcome, i.e. the primary outcome of this review. Within comparison one, the certainty of the evidence on the use of CGM on mortality during hospitalization is very uncertain (typical RR 3.00, 95% CI 0.13 to 70.30; typical RD 0.04, 95% CI -0.06 to 0.14; 50 participants; 1  study; very low certainty). The number of hypoglycemic episodes was reported in two studies with conflicting data. The number of hyperglycemic episodes was reported in one study (typical MD -1.40, 95% CI -2.84 to 0.04; 50 participants; 1 study). The certainty of the evidence was very low for all outcomes because of limitations in study design, and imprecision of estimates.  Three studies are ongoing.

AUTHORS' CONCLUSIONS

There is insufficient evidence to determine if CGM improves preterm infant mortality or morbidities. Long-term outcomes were not reported. Clinical trials are required to determine the most effective CGM and glycemic management regimens in preterm infants before larger studies can be performed to assess the efficacy of CGM  for reducing mortality, morbidity and long-term neurodevelopmental impairments. The absence of CGM labelled for neonatal use is still a major limit in its use as well as the absence of dedicated neonatal devices.

Embedded Model Predictive Control for a Wearable Artificial Pancreas

While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.

Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study

Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals. Graphical Abstract Block diagram and illustrative example of insulin and glucose evolution over time for the proposed algorithm (ARGAE).

An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments

One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy design and testing. In this respect, we here introduce the Control Theory for Therapy Design (CT4TD) framework, which employs optimal control theory on patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, to deliver optimized therapeutic strategies. The definition of personalized PK/PD models allows to explicitly consider the physiological heterogeneity of individuals and to adapt the therapy accordingly, as opposed to standard clinical practices. CT4TD can be used in two distinct scenarios. At the time of the diagnosis, CT4TD allows to set optimized personalized administration strategies, aimed at reaching selected target drug concentrations, while minimizing the costs in terms of toxicity and adverse effects. Moreover, if longitudinal data on patients under treatment are available, our approach allows to adjust the ongoing therapy, by relying on simplified models of cancer population dynamics, with the goal of minimizing or controlling the tumor burden. CT4TD is highly scalable, as it employs the efficient dCRAB/RedCRAB optimization algorithm, and the results are robust, as proven by extensive tests on synthetic data. Furthermore, the theoretical framework is general, and it might be applied to any therapy for which a PK/PD model can be estimated, and for any kind of administration and cost. As a proof of principle, we present the application of CT4TD to Imatinib administration in Chronic Myeloid leukemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimized therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime.

Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes?

Severe hypoglycemia is defined as a condition with serious cognitive dysfunction, such as a convulsion and coma, requiring external help from other persons. This condition is still lethal and is reported to be the cause of death in 4-10% in children and adolescents with type 1 diabetes. The incidence of severe hypoglycemia in the pediatric population was previously reported as high as more than 50-100 patient-years; however, there was a decline in the frequency of severe hypoglycemia during the past decades, and relationship with glycemic control became weaker than previously reported. A lot of studies have shown the neurological sequelae with severe hypoglycemia as cognitive dysfunction and abnormalities in brain structure. This serious condition also provides negative psychosocial outcomes and undesirable compensatory behaviors. Various possible factors, such as younger age, recurrent hypoglycemia, nocturnal hypoglycemia, and impaired awareness of hypoglycemia, are possible risk factors for developing severe hypoglycemia. A low HbA_1c level is not a predictable value for severe hypoglycemia. Prevention of severe hypoglycemia remains one of the most critical issues in the management of pediatric patients with type 1 diabetes. Advanced technologies, such as continuous glucose monitoring (CGM), intermittently scanned CGM, and sensor-augmented pump therapy with low-glucose suspend system, potentially minimize the occurrence of severe hypoglycemia without worsening overall glycemic control. Hybrid closed-loop system must be the most promising tool for achieving optimal glycemic control with preventing the occurrence of severe hypoglycemia in pediatric patients with type 1 diabetes.

The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results

BACKGROUND

Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment.

METHODS

The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented.

RESULTS

Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL).

CONCLUSION

The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment.

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement

BACKGROUND

Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size.

METHOD

An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only.

RESULTS

The ARG algorithm with unannounced meals (ARG_um) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARG_am). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARG_am: 78.1 [68.6-80.2]% (median [IQR]) and ARG_um: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARG_am: 87.4 [86.0-88.9]% and ARG_um: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARG_am: 81.4 [75.4-83.5]% and ARG_um: 80.9 [77.0-85.1]%).

CONCLUSION

In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms

BACKGROUND

Recent development of automated closed-loop (CL) insulin delivery systems, the so-called artificial pancreas (AP), improved the quality of type 1 diabetes (T1D) therapy. As new technologies emerge, patients put increasing trust in their therapeutic devices; therefore, it becomes increasingly important to detect malfunctioning affecting such devices. In this work, we explore a new paradigm to detect insulin pump faults (IPFs) that use unsupervised anomaly detection.

METHODS

We generated CL data corrupted with IPFs using the latest version of the T1D Padova/UVA simulator. From the data, we extracted several features capable to describe the patient dynamics and making more apparent suspicious data portions. Then, a feature selection is performed to determine the optimal feature set. Finally, the performance of several popular unsupervised anomaly detection algorithms is analyzed and compared on the identified optimal feature set.

RESULTS

Using the identified optimal configuration, the best performance is obtained by the Histogram-Based Outlier Score (HBOS) algorithm, which detected 87% of the IPF with only 0.08 false positives per day on average. Isolation forest is the best algorithm that offers more conservative performances, detection of 85% of the faults but only 0.06 false positives per day on average.

CONCLUSION

Unsupervised anomaly detection algorithms can be used effectively to detect IPFs and improve the safety of the AP. Future studies will be dedicated to test the presented method inside dedicated clinical trials.

Automated Insulin Delivery Algorithms

IN BRIEF Automated insulin delivery (AID; also known as artificial pancreas) has improved the regulation of blood glucose concentrations, reduced the frequency of hyperglycemic and hypoglycemic episodes, and improved the quality of life of people with diabetes and their families. Three different types of algorithms-proportional-integral-derivative control, model predictive control, and fuzzy-logic knowledge-based systems-have been used in AID control systems. This article will highlight the foundations of these algorithms and discuss their strengths and limitations. Multivariable artificial pancreas and dual-hormone (insulin and glucagon) systems will be introduced.

Therapeutic medications against diabetes: What we have and what we expect

Diabetes has become one of the largest global health and economic burdens, with its increased prevalence and high complication ratio. Stable and satisfactory blood glucose control are vital to reduce diabetes-related complications. Therefore, continuous attempts have been made in antidiabetic drugs, treatment routes, and traditional Chinese medicine to achieve better disease control. New antidiabetic drugs and appropriate combinations of these drugs have increased diabetes control significantly. Besides, novel treatment routes including oral antidiabetic peptide delivery, nanocarrier delivery system, implantable drug delivery system are also pivotal for diabetes control, with its greater efficiency, increased bioavailability, decreased toxicity and reduced dosing frequency. Among these new routes, nanotechnology, artificial pancreas and islet cell implantation have shown great potential in diabetes therapy. Traditional Chinese medicine also offer new options for diabetes treatment. Our paper aim to overview these therapeutic methods for diabetes therapy. Proper combinations of these existing anti-diabetic medications and searching for novel routes are both necessary for better diabetes control.

Biopsychosocial Factors Associated With Satisfaction and Sustained Use of Artificial Pancreas Technology and Its Components: a Call to the Technology Field

PURPOSE OF REVIEW

Summarize biopsychosocial factors associated with using continuous glucose monitors (CGMs), insulin pumps, and artificial pancreas (AP) systems and provide a "call to the field" about their importance to technology uptake and maintained use.

RECENT FINDINGS

Insulin pumps and CGMs are becoming standard of care for individuals with type 1 diabetes (T1D). AP systems combining a CGM, insulin pump, and automated dosing algorithm are available for commercial use. Despite improved glycemic control with AP system use, numerous barriers exist which may limit their benefit. Studies on components of AP systems (pumps, CGMs) are limited and demonstrate mixed results of their impact on fear of hypoglycemia, adherence, quality of life, depression and anxiety, and diabetes distress. Studies examining biopsychological factors associated specifically with sustained use of AP systems are also sparse. Biological, psychological and social impacts of AP systems have been understudied and the information they provide has not been capitalized upon.

Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system

Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7 mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS. Graphical abstract ᅟ.

Optimizing Hybrid Closed-Loop Therapy in Adolescents and Emerging Adults Using the MiniMed 670G System

OBJECTIVE

The MiniMed 670G System is the first commercial hybrid closed-loop (HCL) system for management of type 1 diabetes. Using data from adolescent and young adult participants, we compared insulin delivery patterns and time-in-range metrics in HCL (Auto Mode) and open loop (OL). System alerts, usage profiles, and operational parameters were examined to provide suggestions for optimal clinical use of the system.

RESEARCH DESIGN AND METHODS

Data from 31 adolescent and young adult participants (14-26 years old) at three clinical sites in the 670G pivotal trial were analyzed. Participants had a 2-week run-in period in OL, followed by a 3-month in-home study phase with HCL functionality enabled. Data were compared between baseline OL and HCL use after 1 week, 1 month, 2 months, and 3 months.

RESULTS

Carbohydrate-to-insulin (C-to-I) ratios were more aggressive for all meals with HCL compared with baseline OL. Total daily insulin dose and basal-to-bolus ratio did not change during the trial. Time in range increased 14% with use of Auto Mode after 3 months (P < 0.001), and HbA_1c decreased 0.75%. Auto Mode exits were primarily due to sensor/insulin delivery alerts and hyperglycemia. The percentage of time in Auto Mode gradually declined from 87%, with a final use rate of 72% (-15%).

CONCLUSIONS

In transitioning young patients to the 670G system, providers should anticipate immediate C-to-I ratio adjustments while also assessing active insulin time. Users should anticipate occasional Auto Mode exits, which can be reduced by following system instructions and reliably bolusing for meals. Unique 670G system functionality requires ongoing clinical guidance and education from providers.

Closed-Loop Control Without Meal Announcement in Type 1 Diabetes

OBJECTIVE

A fully closed-loop insulin-only system was developed to provide glucose control in patients with type 1 diabetes without requiring announcement of meals or activity. Our goal was to assess initial safety and efficacy of this system.

RESEARCH DESIGN AND METHODS

The multiple model probabilistic controller (MMPPC) anticipates meals when the patient is awake. The controller used the subject's basal rates and total daily insulin dose for initialization. The system was tested at two sites on 10 patients in a 30-h inpatient study, followed by 15 subjects at three sites in a 54-h supervised hotel study, where the controller was challenged by exercise and unannounced meals. The system was implemented on the UVA DiAs system using a Roche Spirit Combo Insulin Pump and a Dexcom G4 Continuous Glucose Monitor.

RESULTS

The mean overall (24-h basis) and nighttime (11 PM-7 AM) continuous glucose monitoring (CGM) values were 142 and 125 mg/dL during the inpatient study. The hotel study used a different daytime tuning and manual announcement, instead of automatic detection, of sleep and wake periods. This resulted in mean overall (24-h basis) and nighttime CGM values of 152 and 139 mg/dL for the hotel study and there was also a reduction in hypoglycemia events from 1.6 to 0.91 events/patient/day.

CONCLUSIONS

The MMPPC system achieved a mean glucose that would be particularly helpful for people with an elevated A1c as a result of frequent missed meal boluses. Current full closed loop has a higher risk for hypoglycemia when compared with algorithms using meal announcement.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial

OBJECTIVE

As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively.

RESEARCH DESIGN AND METHODS

A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA_1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms.

RESULTS

AP improved percent time 70-140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70-180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes.

CONCLUSIONS

Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation

Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error.

Model Free iPID Control for Glycemia Regulation of Type-1 Diabetes

OBJECTIVE

The objective is to design a fully automated glycemia controller of Type-1 Diabetes (T1D) in both fasting and postprandial phases on a large number of virtual patients.

METHODS

A model-free intelligent proportional-integral-derivative (iPID) is used to infuse insulin. The feasibility of iPID is tested in silico on two simulators with and without measurement noise. The first simulator is derived from a long-term linear time-invariant model. The controller is also validated on the UVa/Padova metabolic simulator on 10 adults under 25 runs/subject for noise robustness test.

RESULTS

It was shown that without measurement noise, iPID mimicked the normal pancreatic secretion with a relatively fast reaction to meals as compared to a standard PID. With the UVa/Padova simulator, the robustness against CGM noise was tested. A higher percentage of time in target was obtained with iPID as compared to standard PID with reduced time spent in hyperglycemia.

CONCLUSION

Two different T1D simulators tests showed that iPID detects meals and reacts faster to meal perturbations as compared to a classic PID. The intelligent part turns the controller to be more aggressive immediately after meals without neglecting safety. Further research is suggested to improve the computation of the intelligent part of iPID for such systems under actuator constraints. Any improvement can impact the overall performance of the model-free controller.

SIGNIFICANCE

The simple structure iPID is a step for PID-like controllers since it combines the classic PID nice properties with new adaptive features.

Algorithms for a Single Hormone Closed-Loop Artificial Pancreas: Challenges Pertinent to Chemical Process Operations and Control

The development of a closed-loop artificial pancreas to regulate the blood glucose concentration of individuals with type 1 diabetes has been a focused area of research for over 50 years, with rapid progress during the past decade. The daily control challenges faced by someone with type 1 diabetes include asymmetric objectives and risks, and one-sided manipulated input action with frequent relatively fast disturbances. The major automation steps toward a closed-loop artificial pancreas include (i) monitoring and overnight alarms for hypoglycemia (low blood glucose); (ii) overnight low glucose suspend (LGS) systems to prevent hypoglycemia; and (iii) fully closed-loop systems that adjust insulin (and perhaps glucagon) to maintain desired blood glucose levels day and night. We focus on the steps that we used to develop and test a probabilistic, risk-based, model predictive control strategy for a fully closed-loop artificial pancreas. We complete the paper by discussing ramifications of lessons learned for chemical process systems applications.

A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy

Current closed-loop insulin delivery methods stem from sophisticated models of the glucose-insulin (G/I) system, mostly based on complex studies employing glucose tracer technology. We tested the performance of a new minimal model (GLUKINSLOOP 2.0) of the G/I system to characterize the glucose and insulin dynamics during multiple mixed meal tests (MMT) of different sizes in patients with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion, CSII). The GLUKINSLOOP 2.0 identified the G/I system, provided a close fit of the G/I time-courses and showed acceptable reproducibility of the G/I system parameters in repeated studies of identical and double-sized MMTs. This model can provide a fairly good and reproducible description of the G/I system in T1D patients on CSII, and it may be applied to create a bank of “virtual” patients. Our results might be relevant at improving the architecture of upcoming closed-loop CSII systems.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

OBJECTIVE

To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.

RESEARCH DESIGN AND METHODS

After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL.

RESULTS

Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.

CONCLUSIONS

This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

The artificial pancreas: evaluating risk of hypoglycaemia following errors that can be expected with prolonged at-home use

AIMS

Artificial pancreas systems show benefit in closely monitored at-home studies, but may not have sufficient power to assess safety during infrequent, but expected, system or user errors. The aim of this study was to assess the safety of an artificial pancreas system emulating the β-cell when the glucose value used for control is improperly calibrated and participants forget to administer pre-meal insulin boluses.

METHODS

Artificial pancreas control was performed in a clinic research centre on three separate occasions each lasting from 10 p.m. to 2 p.m. Sensor glucose values normally used for artificial pancreas control were replaced with scaled blood glucose values calculated to be 20% lower than, equal to or 33% higher than the true blood glucose. Safe control was defined as blood glucose between 3.9 and 8.3 mmol/l.

RESULTS

Artificial pancreas control resulted in fasting scaled blood glucose values not different from target (6.67 mmol/l) at any scaling factor. Meal control with scaled blood glucose 33% higher than blood glucose resulted in supplemental carbohydrate to prevent hypoglycaemia in four of six participants during breakfast, and one participant during the night. In all instances, scaled blood glucose reported blood glucose as safe.

CONCLUSIONS

Outpatient trials evaluating artificial pancreas performance based on sensor glucose may not detect hypoglycaemia when sensor glucose reads higher than blood glucose. Because these errors are expected to occur, in-hospital artificial pancreas studies using supplemental carbohydrate in anticipation of hypoglycaemia, which allow safety to be assessed in a controlled non-significant environment should be considered as an alternative. Inpatient studies provide a definitive alternative to model-based computer simulations and can be conducted in parallel with closely monitored outpatient artificial pancreas studies used to assess benefit.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

CONTEXT

Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort.

OBJECTIVE

The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC.

DESIGN

This study reports a multicenter, outpatient, randomized, crossover clinical trial.

PATIENTS

Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites.

INTERVENTIONS

Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session.

MAIN OUTCOMES AND MEASURES

Time in the glucose range was 80-140 mg/dL, compared between both arms.

RESULTS

Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL.

CONCLUSIONS

A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study

There is room for improvement in the algorithms used in closed-loop insulin therapy during the prandial period. This pilot study evaluated the efficacy and safety of the Diabeloop algorithm (model predictive control type) during the postprandial period. This 2-center clinical trial compared interstitial glucose levels over two 5-hour periods (with/without the algorithm) following a calibrated lunch. On the control day, the amount of insulin delivered by the pump was determined according to the patient's usual parameters. On the test day, 50% or 75% of the theoretical bolus required was delivered, while the algorithm, informed of carbohydrate intake, proposed changes to insulin delivery every 15 minutes using modeling to forecast glucose levels. The primary endpoint was percentage of time spent at near normoglycemia (70-180 mg/dl). Twelve patients with type 1 diabetes (9 men, age 35.6 ± 12.7 years, HbA1c 7.3 ± 0.8%) were included. The percentage of time spent in the target range was 84.5 ± 20.8 (test day) versus 69.2 ± 33.9% (control day, P = .11). The percentage of time spent in hypoglycemia < 70 mg/dl was 0.2 ± 0.8 (test) versus 4.4 ± 8.2% (control, P = .18). Interstitial glucose at the end of the test (5 hours) was 127.5 ± 40.1 (test) versus 146 ± 53.5 mg/dl (control, P = .25). The insulin doses did not differ, and no differences were observed between the 50% and 75% boluses. In a semi-closed-loop configuration with manual priming boluses (25% or 50% reduction), the Diabeloop v1 algorithm was as successful as the manual method in determining the prandial bolus, without any exposure to excessive hypoglycemic risk.

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals

BACKGROUND

Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement.

MATERIALS AND METHODS

A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern.

RESULTS

On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively.

CONCLUSIONS

The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.

The artificial pancreas: current status and future prospects in the management of diabetes

Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional-integral-derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.

Algorithms for a closed-loop artificial pancreas: the case for model predictive control

The relative merits of model predictive control (MPC) and proportional-integral-derivative (PID) control are discussed, with the end goal of a closed-loop artificial pancreas (AP). It is stressed that neither MPC nor PID are single algorithms, but rather are approaches or strategies that may be implemented very differently by different engineers. The primary advantages to MPC are that (i) constraints on the insulin delivery rate (and/or insulin on board) can be explicitly included in the control calculation; (ii) it is a general framework that makes it relatively easy to include the effect of meals, exercise, and other events that are a function of the time of day; and (iii) it is flexible enough to include many different objectives, from set-point tracking (target) to zone (control to range). In the end, however, it is recognized that the control algorithm, while important, represents only a portion of the effort required to develop a closed-loop AP. Thus, any number of algorithms/approaches can be successful--the engineers involved in the design must have experience with the particular technique, including the important experience of implementing the algorithm in human studies and not simply through simulation studies.