Fully Closed-Loop Insulin Delivery in Patients Undergoing Pancreatic Surgery.

The central role of pancreas in glucose regulation imposes high demands on perioperative glucose management in patients undergoing pancreatic surgery. In a post hoc subgroup analysis of a randomized controlled trial, we evaluated the perioperative use of subcutaneous (SC) fully closed-loop (FCL; CamAPS HX) versus usual care (UC) insulin therapy in patients undergoing partial or total pancreatic resection. Glucose control was compared using continuous glucose monitoring (CGM) metrics (% time with CGM values between 5.6 and 10.0 mmol/L and more). Over the time of hospitalization, FCL resulted in better glucose control than UC with more time spent in the target range 5.6-10.0 mmol/L (mean [standard deviation] % time in target 77.7% ± 4.6% and 41.1% ± 19.5% in FCL vs. UC subjects, respectively; mean difference 36.6% [95% confidence interval 18.5-54.8]), without increasing the risk of hypoglycemia. Findings suggest that an adaptive SC FCL approach effectively accommodated the highly variable insulin needs in patients undergoing pancreatic surgery. Clinical trials registration: ClinicalTrials.gov, NCT04361799.

Performance of a closed-loop glucose control system, comprising a continuous glucose monitoring system and an AI-based controller in swine during severe hypo- and hyperglycemic provocations.

Intensive care unit (ICU) patients develop stress induced insulin resistance causing hyperglycemia, large glucose variability and hypoglycemia. These glucose metrics have all been associated with increased rates of morbidity and mortality. The only way to achieve safe glucose control at a lower glucose range (e.g., 4.4-6.6 mmol/L) will be through use of an autonomous closed loop glucose control system (artificial pancreas). Our goal with the present study was to assess the safety and performance of an artificial pancreas system, composed of the EIRUS (Maquet Critical Care AB) continuous glucose monitor (CGM) and novel artificial intelligence-based glucose control software, in a swine model using unannounced hypo- and hyperglycemia challenges. Fourteen piglets (6 control, 8 treated) underwent sequential unannounced hypoglycemic and hyperglycemic challenges with 3 IU of NovoRapid and a glucose infusion at 17 mg/kg/min over the course of 5 h. In the Control animals an experienced ICU physician used every 30-min blood glucose values to maintain control to a range of 4.4-9 mmol/L. In the Treated group the artificial pancreas system attempted to maintain blood glucose control to a range of 4.4-6.6 mmol/L. Five of six Control animals and none of eight Treated animals experienced severe hypoglycemia (< 2.22 mmol/L). The area under the curve 3.5 mmol/L was 28.9 (21.1-54.2) for Control and 4.8 (3.1-5.2) for the Treated animals. The total percent time within tight glucose control range, 4.4-6.6 mmol/L, was 32.8% (32.4-47.1) for Controls and 55.4% (52.9-59.4) for Treated (p < 0.034). Data are median and quartiles. The artificial pancreas system abolished severe hypoglycemia and outperformed the experienced ICU physician in avoiding clinically significant hypoglycemic excursions.

Managing hyperglycemia during the COVID-19 pandemic: Improving outcomes using new technologies in intensive care.

Hyperglycemia is a significant risk for mortality in COVID-19 infections and is most dramatically noted in critically ill patients. Hyperglycemia and/or diabetes are noted in approximately 30%-40% of patients admitted with COVID-19 infections. Previous studies have shown a marked increase in mortality related to increased glucose concentrations and reduction with improved glucose control. In vivo and in vitro studies reveal the mechanisms by which hyperglycemia increases virulence and how glucose control and insulin reduce it. Optimal glucose control in intensive care is limited by manual sampling of glucose and intravenous insulin adjustment, as well as increased nursing workload and the need of protective equipment. Tools for safe and effective automation of glucose control in intensive care are discussed. A suitable closed loop device could save the lives of thousands of hospitalized hyperglycemic individuals infected with COVID-19 while protecting medical professionals from infection risk.

Desarrollo y Simulación de un Algoritmo de Control Automatizado para Insulinoterapia de Urgencias Hiperglucémicas en Diabetes / Development and Simulation of an Automated Control Algorithm for Insulin Therapy of Hyperglycemic Emergencies in Diabetes

Resumen El presente trabajo describe el desarrollo y simulación de un algoritmo para el control automático de la infusión de insulina en el manejo glucémico de pacientes con cetoacidosis diabética (CAD) y estado hiperosmolar hiperglucémico (EHH). Se programó un algoritmo que calcula la insulina necesaria para lograr un descenso glucémico de 50 mg/dL/h hasta llegar a glucemias de 250 mg/dL, para posteriormente mantenerlas en 220 mg/dL hasta la remisión de la patología. La simulación del software se realizó haciendo uso de registros glucémicos de 10 pacientes con CAD manejados en el Hospital Juárez de México. Los resultados de la simulación mostraron una incidencia 6 veces menor de hipoglucemias, así como un 33.7% menos de insulina necesaria dentro del tratamiento, sin diferencias entre los descensos medios de glucosa por hora de las mediciones reales y simuladas. Este software propone un uso innovador de los llamados páncreas artificiales al aplicarlos en urgencias hiperglucémicas, implementando además el uso de la sensibilidad a la insulina como variable para el funcionamiento de los mismos. Los resultados demuestran que el algoritmo podría ser capaz de lograr un manejo glucémico apegado a las guías de tratamiento, generando un menor gasto de insulina y evitando hipoglucemias durante la terapéutica, con una posible aplicación en dispositivos biomédicos autónomos.

Abstract This paper describes the development and simulation of an algorithm for the automatic control of insulin infusion, in the glycemic management of patients with diabetic ketoacidosis (CAD) and hyperglycemic hyperosmolar state (EHH). An algorithm was programmed to calculate the requirement insulin for a glycemic decrease of 50 mg/dL/h until reach 250 mg/dL in blood glucose levels, and thus maintaining it at 220 mg/dL until the pathology remission. The software simulation was performed using glycemic records of 10 patients with CAD managed in the Hospital Juárez de México. The results of the simulation showed a lower incidence of hypoglycemia, as well as a lower insulin requirement within the treatment, without differences in the average glucose decreases per hour between real and simulated measurements. This software proposes an innovative use of the artificial pancreas in hyperglycemic emergencies, and also implementing the use of insulin sensitivity as a variable for their function. The results show that the algorithm could be able to achieve glycemic management attached to the treatment guidelines, generating lower insulin expenditure and avoiding hypoglycemia during therapy, with a possible application in autonomous biomedical devices.

Insulin-Based Infusion System: Advancing the Development.

BACKGROUND: Deviations in glucose control in critical care have been shown to increase mortality and morbidity. However, optimal glucose control through present technologies has shown to be a challenge. The insulin balanced infusion system (IBIS) is a new and emerging technology. METHODS: The closed loop system was tested in a stress trial to evaluate glucose stability in response to various conditions in nonrandomized people with type 1 diabetes mellitus (n=12). The prototype used in this trial was based on intermittent capillary measurements. RESULTS: Induced stresses in the study using unpredicted stimuli of intravenous or oral glucose and intravenous insulin boluses, was contained with glucose remaining in target 43.8% of the time. Mean increase in glucose concentration after glucose load was 17.4 mg/dl; after insulin bolus, no hypoglycemia (blood glucose less than 70 mg/dl) occurred. CONCLUSION: The use of IBIS proved safe and feasible under a wide range of conditions. The sensing and stress response of the IBIS demonstrated noticeable features.

Insulin-Based Infusion System: Preliminary Study.

BACKGROUND: Optimal glucose control has been shown to be useful in critical care as well as in other settings. Glucose concentrations in patients admitted to critical care are characterized by marked variability and hypoglycemia due to inadequate sensing and treatment technologies. METHODS: The insulin balanced infusion system (IBIS) is a closed-loop system that uses a system controller, two syringe pumps, and capillary glucose sensor intravenously infusing regular insulin and/or dextrose. The IBIS performance was evaluated in terms of glucose stability in response to various conditions in subjects with type 1 and insulin requiring type 2 diabetes mellitus (n = 15) with frequent intermittent capillary measurements, entered into the system and an adaptive algorithm adjusting the treatment modalities without other nursing intervention. RESULTS: Target glucose concentrations (80-125 mg/dl) were achieved from hyperglycemic levels in 2.49 hours in the first study with mean and standard deviation of 105.2 mg/dl and 11.5 mg/dl, respectively. CONCLUSION: Preliminary studies using a prototype closed-loop glucose control system for critical care produced noticeable results. Improvements were initiated within the system and further studies performed.

Automated blood glucose control in type 1 diabetes: A review of progress and challenges.

Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.

Dual-hormone treatment with insulin and glucagon in patients with type 1 diabetes.

Intensive insulin treatment in type 1 diabetes reduces the incidence and slows the progression of microvascular and macrovascular complications; however, it is associated with an increased risk of hypoglycaemia and weight gain. In this review, we propose dual-hormone treatment with insulin and glucagon as a method for achieving near normalization of blood glucose levels without increasing hypoglycaemia frequency and weight gain. We briefly summarize glucagon pathophysiology in type 1 diabetes as well as the current applications of glucagon for the treatment of hypoglycaemia. Until now, the use of glucagon has been limited by the need for reconstitution immediately before use, because of instability of the available compounds; however, stabile compounds are soon to be launched and will render long-term intensive dual-hormone treatment in type 1 diabetes possible.

Continuous glucose monitoring awaits its "killer app".

Continuous glucose monitoring (CGM) could drive a paradigm shift in diabetes care, but realization of this promise awaits a complementary shift in the way CGM data is used. The most exciting use for CGM is as the input for automated, closed-loop glucose control. Although first generation CGM devices leave much room for improvement, closed-loop control does not have to wait. Algorithms should target blood glucose levels above the normal range for safety in the setting of imperfect CGM measurements. If the mean glucose under closed-loop control is sufficiently close to the chosen target, hemoglobin A1c goals could be met while minimizing risk of hypoglycemia. CGM may also improve the care of intensive care unit patients treated with intensive insulin therapy and the large numbers of diabetic patients in general hospital wards.

Further development of artificial pancreas: blocked by patents?

Patent activity in the field of medical device technology and especially in the area of artificial pancreas development has surged in recent years. According to the search presented in this article, the number of granted U.S. patents in the area of closed-loop glucose control (CLGC) increased from 24 filed in 1991 to 247 filed in 2001. A company active in the area of diabetes technology development will likely need to understand a patent landscape consisting of hundreds of patents. Currently, both in the United States and in Europe, patentability requirements seem to be raised in order to ensure patent quality. However, the current patent landscape reflects the work of the patent offices in the past, as already granted patents are not affected by changes made to the patent grant procedure today.Regarding the increasing amount of patents and considering the complexity of CLGC systems, the attempt to develop a CLGC system will become more and more venturesome regarding the risk of infringement of already existing patents. The consequence of this situation can be that less innovation takes place.This article highlights some important general aspects of the patent system, briefly characterizes the current CLGC patent landscape, and illustrates by means of two exemplary patents what one angle of said patent landscape looks like. It is our opinion that, in order to support the rapid development of an artificial pancreas for patients with diabetes, adequate action to lower this hurdle should be undertaken by a consortium of all parties involved (industries, patient organizations, health-care professionals, and institutional payers).

Analysis of algorithms for intensive care unit blood glucose control.

Intensive care unit (ICU) blood glucose control algorithms were reviewed and analyzed in the context of linear systems theory and classical feedback control algorithms. Closed-loop performance was illustrated by applying the algorithms in simulation studies using an in silico model of an ICU patient. Steady-state and dynamic input-output analysis was used to provide insight about controller design and potential closed-loop performance. The proportional-integral-derivative, columnar insulin dosing (CID, Glucommander-like), and glucose regulation for intensive care patients (GRIP) algorithms were shown to have similar features and performance. The CID strategy is a time-varying proportional-only controller (no integral action), whereas the GRIP algorithm is a nonlinear controller with integral action. A minor modification to the GRIP algorithm was suggested to improve the closed-loop performance. Recommendations were made to guide control theorists on important ICU control topics worthy of further study.