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 (ARGum) 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 (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fastabsorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 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.Fil: Fushimi, Emilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Colmegna, Patricio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Virginia; Estados Unidos. Universidad Nacional de Quilmes; ArgentinaFil: de Battista, Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Sanchez Peña, Ricardo Salvador. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; Argentin

Automatic Glucose Control during Meals and Exercise in Type 1 Diabetes: Proof-of-Concept in Silico Tests Using a Switched LPV Approach

Keeping the blood glucose levels within the safe range during meals and exercise still represents a major hurdle not only for patients with type 1 diabetes (T1D), but also for Artificial Pancreas (AP) systems. One of the reasons a fully (autonomous) closed-loop solution has not been released onto the market yet is the slow action of current insulin analogs. To partially overcome this limitation, the authors have previously designed a switched control strategy equipped with an insulin-on-board (IOB) safety loop that mitigates meal-related glucose excursions without carbohydrate counting. In this letter, a similar strategy based on a Linear Parameter-Varying (LPV) control law has been adapted to safely handle also exercise challenges with minimum user intervention. In silico results using the UVA/Padova simulator evidence that the proposed closed-loop scheme is feasible under moderate-intense exercise bouts by effectively and safely reducing the risk of hypoglycemia.Fil: Colmegna, Patricio Hernán. University of Virginia; Estados Unidos. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Cronobiología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bianchi, Fernando Daniel. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sanchez Peña, Ricardo Salvador. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Sliding-mode disturbance observers for an artificial pancreas without meal announcement

[EN] Carbohydrate counting is not only a burden for patients with type 1 diabetes, but estimation errors in meal announcement could also degrade the outcomes of the current hybrid closed-loop systems. Therefore, removing meal announcement is desirable. A novel control system is addressed here to face postprandial control without meal announcement. The proposed system grounds on two applications of the sliding mode observers in dealing with disturbances: first, the equivalent output technique is used to reconstruct the meal rate of glucose appearance via a first order sliding mode observer; second, a super-twisting -based residual generator is used to detect the meals. Subsequently, a bolusing algorithm uses the information of the two observers to trigger a series of boluses based on a proportional-derivative-like strategy. An in silico validation with 30 patients in a 30-day scenario reveals that the meal detector algorithm achieves a low rate of false positives per day (0.1 (0.1), mean (SD)) and a detection time of 28.5(6.2) min. Additionally, the bolusing algorithm fulfills a non-statistically different mean glucose than the hybrid counterpart with bolus misestimation (146.69 (12.20) mg/dLvs. 144.28 (11.01) mg/dL,p>0.05), without increasing hypoglycemia (0.029 (0.077) vs. 0.004 (0.014)%, p > 0,05), although at the expense of a slightly higher time in hyperglycemia (22.51(8.72) % vs. 18.65 (7.89)%, p <0.05) due to the conservative tuning of the bolusing algorithm for the sake of safety.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) [grant number DPI2016-78831-C2-1-12]; the European Union [FEDER funds]: and Generalitat Valenciana [grant number ACIF/2017/021]Sala-Mira, I.; Diez, J.; Ricarte Benedito, B.; Bondía Company, J. (2019). Sliding-mode disturbance observers for an artificial pancreas without meal announcement. Journal of Process Control. 78:68-77. https://doi.org/10.1016/j.jprocont.2019.03.00868777

Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring

[EN] The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the samemeal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 +/- 2%, 93 +/- 5%, and 47 +/- 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes.This work was funded by the Spanish Government through grants DPI2016-78831-C2-1-R and DPI2016-78831-C2-2-R, the University of Girona through grant BR2014/51, and the European Union through Fondo Europeo de Desarrollo Regional (FEDER) Funds.Ramkissoon, C.; Herrero, P.; Bondía Company, J.; Vehí, J. (2018). Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring. Sensors. 18(3):1-18. https://doi.org/10.3390/s18030884S11818

Deep learning methods for improving diabetes management tools

Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.Comment: 11 pages, 5 figures, 2 table

A modular safety system for an insulin dose recommender: A feasibility study

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are: predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycaemia; and a personalised safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycaemic control and safety system functioning were compared between the two-weeks run-in period and the end-point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. Results: Overall, glycaemic control improved over the evaluated period. In particular, percentage time in the hypoglycaemia range (<3.0mmol/l) significantly decreased from 0.82 (0.05-4.79) % at run-in to 0.33 (0.00-0.93) % at endpoint (p=0.02). This was associated with a significant increase in percentage time in target range (3.9-10.0mmol/l) from 52.8 (38.3-61.5) % to 61.3 (47.5-71.7) % (p=0.03). There was also a reduction in number of carbohydrate recommendations. Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes

Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems

The development of a fully autonomous artificial pancreas system (APS) to independently regulate the glucose levels of a patient with Type 1 diabetes has been a long-standing goal of diabetes research. A significant barrier to progress is the difficulty of testing new control algorithms and safety features, since clinical trials are time- and resource-intensive. To facilitate ease of validation, we propose an open-source APS testbed by integrating APS controllers with two state-of-the-art glucose simulators and a novel fault injection engine. The testbed is able to reproduce the blood glucose trajectories of real patients from a clinical trial conducted over six months. We evaluate the performance of two closed-loop control algorithms (OpenAPS and Basal Bolus) using the testbed and find that more advanced control algorithms are able to keep blood glucose in a safe region 93.49% and 79.46% of the time on average, compared with 66.18% of the time for the clinical trial. The fault injection engine simulates the real recalls and adverse events reported to the U.S. Food and Drug Administration (FDA) and demonstrates the resilience of the controller in hazardous conditions. We used the testbed to generate 2.5 years of synthetic data representing 20 different patient profiles with realistic adverse event scenarios, which would have been expensive and risky to collect in a clinical trial. The proposed testbed is a valid tool that can be used by the research community to demonstrate the effectiveness of different control algorithms and safety features for APS.Comment: 12 pages, 12 figures, to appear in the IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 202

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses

Background: Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. Methods: A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. Results: For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, &lt;70 mg/dl: 5.8%, and &lt;50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, &lt;70 mg/dl: 4.1%, and &lt;50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, &lt;70 mg/dl: 5.0%, and &lt;50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. Conclusions: The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.Facultad de Ingenierí

Model-Based Closed-Loop Glucose Control in Critical Illness

Stress hyperglycemia is a common complication in critically ill patients and is associated with increased mortality and morbidity. Tight glucose control (TGC) has shown promise in reducing mean glucose levels in critically ill patients and may mitigate the harmful repercussions of stress hyperglycemia. Despite the promise of TGC, care must be taken to avoid hypoglycemia, which has been implicated in the failure of some previous clinical attempts at TGC using intensive insulin therapies. In fact, a single hypoglycemic event has been shown to result in worsened patient outcomes. The nature of tight glucose regulation lends itself to automatic monitoring and control, thereby reducing the burden on clinical staff. A blood glucose target range of 110-130 mg/dL has been identified in the High-Density Intensive Care (HIDENIC) database at the University of Pittsburgh Medical Center (UPMC). A control framework comprised of a zone model predictive controller (zMPC) with moving horizon estimation (MHE) is proposed to maintain euglycemia in critically ill patients. Using continuous glucose monitoring (CGM) the proposed control scheme calculates optimized insulin and glucose infusion to maintain blood glucose concentrations within the target zone. Results from an observational study employing continuous glucose monitors at UPMC are used to reconstruct blood glucose from noisy CGM data, identify a model of CGM error in critically ill patients, and develop an in silico virtual patient cohort. The virtual patient cohort recapitulates expected physiologic trends with respect to insulin sensitivity and glycemic variability. Furthermore, a mechanism is introduced utilizing proportional-integral-derivative (PID) to modulate basal pancreatic insulin secretion rates in virtual patients. The result is virtual patients who behave realistically in simulated oral glucose tolerance tests and insulin tolerance tests and match clinically observed responses. Finally, in silico trials are used to simulate clinical conditions and test the developed control system under realistic conditions. Under normal conditions the control system is able to tightly control glucose concentrations within the target zone while avoiding hypoglycemia. To safely counteract the effect of faulty CGMs a system to detect sensor error and request CGM recalibration is introduced. Simulated in silico tests of this system results in accurate detection of excessive error leading to higher quality control and hypoglycemia reduction