A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Builes Montano, C. E.. Universidad de Antioquia; ColombiaFil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; ColombiaFil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Garcia Tirado, J.. University of Virginia; Estados UnidosFil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Artificial Pancreas: In Silico Study Shows No Need of Meal Announcement and Improved Time in Range of Glucose with Intraperitoneal vs. Subcutaneous Insulin Delivery

Contemporary Artificial Pancreas (AP) consists of a subcutaneous (SC) glucose sensor, a SC insulin pump and a control algorithm. Even the most advanced systems are far from optimal, in particular due to the non-physiologic nature of SC route. While SC insulin delivery is convenient and minimally invasive, it introduces delays to insulin action that make tight control difficult, particularly during meals. In addition frequent patient interventions are needed, e.g., at mealtime. The intraperitoneal (IP) insulin delivery could address this major challenge since it exhibits a faster pharmacokinetics/pharmacodynamics, hence making easier to quickly respond to glycemic disturbances. A 1-day hospital closed-loop study has shown significant improvements of IP glucose control vs SC AP, and that meal announcement is not necessary. However, the IP AP has not been tested in more realistic everyday life conditions. In this work we have performed an in silico study of 14 days of an IP AP by using the UVA/Padova simulator which includes intra- and inter-day variability of insulin sensitivity and several real life scenarios. We show superiority of IP AP vs SC AP in terms of quality of glucose control (time in range 87% IP vs 80% SC) without the need of a meal announcement

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 intrapatient variations related to daily insulin sensitivity (SI) 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 controloriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.Fil: Moscoso Vásquez, Hilda Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; ArgentinaFil: Colmegna, Patricio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Virginia; Estados Unidos. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Rosales, Nicolás. 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. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; Argentin

Analysis of particles size distribution on the agglomeration and shrinkage of alumina-zirconia compacts

The combination of Alumina and Zirconia has emerged as a promising ceramic structure for advance machine tool application. However, the particles of Alumina and Zirconia tend to agglomerate during mixture which affected shrinkage and dimension accuracy of the end product. This study focused on the analysis of the particle size of Alumina-Zirconia compacts and their relationship with the shrinkage and agglomerates. The particles size of single Alumina, Zirconia and ball-milled Alumina-Zirconia with 90-10 wt% ratio were examined by mastersizer. These powders then were compacted and sintered at 1400°C to examine their shrinkage. The results show that Alumina possesses larger particles size of 109.65 μm, which is 10 folds larger than Zirconia at 6.10 μm. When blended by ball mill, the Alumina�Zirconia particles were changed into 9.77 μm, showing that the ball mill to refine powder particles while reducing the risk of agglomeration. After sintering, the Alumina-Zirconia compacts were shrunk to maximum 9.56% when 75-25 wt% of Alumina-Zirconia. The combination of porosity, agglomerate and infiltration of zirconia between alumina grains were responsible for the shrinkage of Alumina-Zirconia compacts

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability

[EN] Background and Objective: Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. Methods: In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. Results: Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; percentage time in target [70, 180] mg/dl, 82.0 ± 7.0 vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0 vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4 vs. 140.5 ± 13.0 mg/dl; percentage time in target, 65.9 ± 12.9 vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1 vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.This project has been funded by the Welcome Trust.Herrero, P.; Bondía Company, J.; Adewuji, O.; Pesl, P.; El-Sharkawy, M.; Reddy, M.; Toumazou, C.... (2017). Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability. Computer Methods and Programs in Biomedicine. 146:125-131. https://doi.org/10.1016/j.cmpb.2017.05.010S12513114

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes

[EN] Objective: To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). Research design and methods: A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. Results: Ten participants (aged 40.8 +/- 7.0 years; HbA1c of 7.3 +/- 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates. Conclusions: The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.This work has been partially funded by the Spanish Government under grants DPI2016-78831-C2-1-R, DPI2016-78831-C2-2-R, and FPU0244 2015; by the EU through FEDER funds; by the National Counsel of Technological and Scientific Development, CNPq-Brazil 207688/2014-1; and by the Government of Catalonia under 2017SGR1551. C.V. is the recipient of a grant from the Hospital Clinic i Universitari of Barcelona ("Premi Fi de Residencia 2018-2019").Viñals, C.; Beneyto, A.; Martín-Sanjosé, J.; Furió-Novejarque, C.; Hirata-Bertachi, A.; Bondía Company, J.; Vehí, J.... (2021). Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism. 106(1):55-63. https://doi.org/10.1210/clinem/dgaa5625563106

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

[EN] Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are Mets (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only Mets is also viable for a more immediate implementation of this correction into market devices.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) (Grant Number DPI2016-78831-C2-1-R), the European Union (FEDER funds), and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politecnica de Valencia (Grant Number PAID-06-18). We would like to acknowledge the work performed by Josep Vehí, Lyvia Biagi, Ignacio Conget and Carmen Quirós. We thank all the participants and clinical staff who participated in clinical acquisition and pre-formatting of data for study, without whom none of this work would have been possible.Laguna Sanz, AJ.; Diez, J.; Giménez, M.; Bondía Company, J. (2019). Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration. Sensors. 19(17):1-14. https://doi.org/10.3390/s19173757114191