In silico evaluation of a control system and algorithm for automated insulin infusion in the ICU setting

<p>Abstract</p> <p>Background</p> <p>It is known that tight control of glucose in the Intensive Care Unit reduces morbidity and mortality not only in diabetic patients but also in those non-diabetics who become transiently hyperglycemic. Taking advantage of a recently marketed subcutaneous glucose sensor we designed an <it>Automatic Insulin Infusion System </it>(AIIS) for inpatient treatment, and tested its stability under simulated clinical conditions.</p> <p>Methods</p> <p>The system included: reference glucose, glucose sensor, insulin and glucose infusion controllers and emergency infusion logic. We carried out computer simulations using Matlab/Simulink^®, in both common and worst-case conditions.</p> <p>Results</p> <p>The system was capable of controlling glucose levels without entering in a phase of catastrophic instability, even under severe simulated challenges. Care was taken to include in all simulations the 5-10 minute delay of the subcutaneous glucose signal when compared to the real-time serum glucose signal, a well-known characteristic of all subcutaneous glucose sensors.</p> <p>Conclusions</p> <p>When tested <it>in-Silico</it>, a commercially available subcutaneous glucose sensor allowed the stable functioning of a proportional-derivative Automatic Insulin Infusion System, which was able to maintain glucose within acceptable limits when using a well-established glucose response model simulating a patient. Testing of the system <it>in vivo </it>using animal models is now warranted.</p

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient

Modeling and Prediction in Diabetes Physiology

Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction

Basal Insulin Adjustments and Continuous Glucose Monitoring During Exercise in Type 1 Diabetes

Research has demonstrated that the timing, type, duration and intensity of exercise can all impact blood glucose levels differently in patients with type 1 diabetes. Continuous steady state moderate intensity aerobic exercise tends to drop blood glucose levels, while vigorous-to-maximal intensity exercise tends to cause blood glucose levels to rise. The purpose of this dissertation was to test different basal insulin strategies to improve glycemia during various forms of exercise and in recovery in patients with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy. A secondary purpose was to examine the effectiveness of continuous glucose monitor (CGM) technology to track interstitial glucose levels during exercise and in recovery. Initially, we examined the effects of insulin pump suspension on glycemia during aerobic and circuit-based exercise. We found that pump suspension at exercise onset caused a greater drop in glycemia during aerobic vs. circuit-based exercise. Aerobic exercise also modestly increased the time spent in hypoglycemia 12 hours post-exercise. We then investigated the effects of a reduced insulin infusion rate (pump on) vs. pump suspension (pump off) during intermittent high intensity exercise and found neither an advantage nor disadvantage on blood glucose level with pump removal for exercise. Interestingly, pump on resulted in slightly higher time spent in hypoglycemia in the 12-hour period post-exercise vs. pump off. In a third study, we tested the strategy of lowering basal insulin delivery by 50% or 80% well in advance of exercise vs. pump suspension at exercise onset in an attempt to reduce hypoglycemia risk. Overall, we found that a 50-80% basal rate reduction set 90 mins pre-exercise attenuated the drop in blood glucose level better than pump suspension at exercise start. Finally, we assessed CGM accuracy during exercise and in the meal post-exercise and found that CGM underestimated the drop in glycemia during exercise and appeared to lag behind self-monitoring blood glucose values when glycemia was changing rapidly. This thesis dissertation highlights a number of novel strategies, including basal insulin rate reductions and altering the type of exercise for improved exercise management in people living with type 1 diabetes on CSII

Commuted PD controller for nonlinear systems: glucose–insulin regulatory case

As an option to deal with insulin-dependent disease, a recently commuted PD control strategy is designed and carefully analyzed for different clinic diabetic patients. This controller approach is mainly conceived to stabilize the glucose blood concentration in a diabetic patient around its basal value; hence, avoiding extreme situations such as hypoglycemia and hyperglycemia. This control strategy receives two inputs carefully tuned to actuate when the measured variable is out of a prescribed healthy zone. Therefore, one of these variables is invoked to decrease the glucose concentration to insulin injection, and the other is employed to increase the glucose absorption, both by using a proper PD controller. According to our numerical experiments, our controller approach performs well, even when there is an external disturbance in the controlled systemPeer ReviewedPostprint (published version

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

A new familial form of a late-onset, persistent hyperinsulinemic hypoglycemia of infancy caused by a novel mutation in KCNJ11.

The ATP-sensitive potassium channel (KATP) functions as a metabo-electric transducer in regulating insulin secretion from pancreatic β-cells. The pancreatic KATP channel is composed of a pore-forming inwardly-rectifying potassium channel, Kir6.2, and a regulatory subunit, sulphonylurea receptor 1 (SUR1). Loss-of-function mutations in either subunit often lead to the development of persistent hyperinsulinemic hypoglycemia of infancy (PHHI). PHHI is a rare genetic disease and most patients present with immediate onset within the first few days after birth. In this study, we report an unusual form of PHHI, in which the index patient developed hyperinsulinemic hypoglycemia after 1 year of age. The patient failed to respond to routine medication for PHHI and underwent a complete pancreatectomy. Genotyping of the index patient and his immediate family members showed that the patient and other family members with hypoglycemic episodes carried a heterozygous novel mutation in KCNJ11 (C83T), which encodes Kir6.2 (A28V). Electrophysiological and cell biological experiments revealed that A28V hKir6.2 is a dominant-negative, loss-of-function mutation and that KATP channels carrying this mutation failed to reach the cell surface. De novo protein structure prediction indicated that this A28V mutation reoriented the ER retention motif located at the C-terminal of the hKir6.2, and this result may explain the trafficking defect caused by this point mutation. Our study is the first report of a novel form of late-onset PHHI that is caused by a dominant mutation in KCNJ11 and exhibits a defect in proper surface expression of Kir6.2

CONTROL OF CONSTRAINED BIOSYSTEMS

Biological systems (biosystems), due to their complexity and multidisplinary character, are becoming one of the challenging research topics in the field of systems and control. In this work, several tools for dealing with control subject to constraints in the area of biosystems have been explored.Revert Tomás, A. (2011). CONTROL OF CONSTRAINED BIOSYSTEMS. http://hdl.handle.net/10251/12873Archivo delegad