New closed-loop insulin systems.
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ABSTRACT:
SUBMITTER: Boughton CK
PROVIDER: S-EPMC8012332 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We investigated whether continuous glucose monitoring (CGM) levels can accurately assess glycemic control while directing closed-loop insulin delivery.Data were analyzed retrospectively from 33 subjects with type 1 diabetes who underwent closed-loop and conventional pump therapy on two separate nights. Glycemic control was evaluated by reference plasma glucose and contrasted against three methods based on Navigator (Abbott Diabetes Care, Alameda, CA) CGM levels.Glucose mean and variability were estimated by unmodified CGM levels with acceptable clinical accuracy. Time when glucose was in target range was overestimated by CGM during closed-loop nights (CGM vs. plasma glucose median [interquartile range], 86% [65-97%] vs. 75% [59-91%]; P=0.04) but not during conventional pump therapy (57% [32-72%] vs. 51% [29-68%]; P=0.82) providing comparable treatment effect (mean [SD], 28% [29%] vs. 23% [21%]; P=0.11). Using the CGM measurement error of 15% derived from plasma glucose-CGM pairs (n=4,254), stochastic interpretation of CGM gave unbiased estimate of time in target during both closed-loop (79% [62-86%] vs. 75% [59-91%]; P=0.24) and conventional pump therapy (54% [33-66%] vs. 51% [29-68%]; P=0.44). Treatment effect (23% [24%] vs. 23% [21%]; P=0.96) and time below target were accurately estimated by stochastic CGM. Recalibrating CGM using reference plasma glucose values taken at the start and end of overnight closed-loop was not superior to stochastic CGM.CGM is acceptable to estimate glucose mean and variability, but without adjustment it may overestimate benefit of closed-loop. Stochastic CGM provided unbiased estimate of time when glucose is in target and below target and may be acceptable for assessment of closed-loop in the outpatient setting.
| S-EPMC3540898 | biostudies-other
Project description:A differential equation model is formulated that describes the dynamics of glucose concentration in blood circulation. The model accounts for the intake of food, expenditure of calories and the control of glucose levels by insulin and glucagon. These and other hormones affect the blood glucose level in various ways. In this study only main effects are taken into consideration. Moreover, by making a quasi-steady state approximation the model is reduced to a single nonlinear differential equation of which parameters are fit to data from healthy subjects. Feedback provided by insulin plays a key role in the control of the blood glucose level. Reduced β-cell function and insulin resistance may hamper this process. With the present model it is shown how by closed-loop control these defects, in an organic way, can be compensated with continuous infusion of exogenous insulin.
| S-EPMC5217952 | biostudies-literature
Project description:Physiological Closed-Loop Controlled systems continue to take a growing part in clinical practice, offering possibilities of providing more accurate, goal-directed care while reducing clinicians' cognitive and task load. These systems also provide a standardized approach for the clinical management of the patient, leading to a reduction in care variability across multiple dimensions. For fluid management and administration, the advantages of closed-loop technology are clear, especially in conditions that require precise care to improve outcomes, such as peri-operative care, trauma, and acute burn care. Controller design varies from simplistic to complex designs, based on detailed physiological models and adaptive properties that account for inter-patient and intra-patient variability; their maturity level ranges from theoretical models tested in silico to commercially available, FDA-approved products. This comprehensive scoping review was conducted in order to assess the current technological landscape of this field, describe the systems currently available or under development, and suggest further advancements that may unfold in the coming years. Ten distinct systems were identified and discussed.
| S-EPMC9315597 | biostudies-literature
Project description:Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.
| S-EPMC6548466 | biostudies-literature
Project description:Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.
| S-EPMC7336885 | biostudies-literature
Project description: Not available
| S-EPMC9255645 | biostudies-literature
Project description:ObjectiveTo quantify age-related variability of insulin needs during day and night closed-loop insulin delivery.Research design and methodsWe retrospectively analyzed data from hybrid closed-loop studies involving young children (1-6 years old, n = 20), children (7-12 years, n = 21), adolescents (13-17 years, n = 15), and adults (>18 years, n = 58) with type 1 diabetes. The coefficient of variation quantified variability of insulin needs during 3 weeks of unrestricted-living hybrid closed-loop use.ResultsData from 2,365 nights and 2,367 days in 114 participants were analyzed. The coefficient of variation of insulin delivery was higher in young children compared with adults (mean difference at nighttime 10.7 percentage points [95% CI 2.9-18.4], P = 0.003; daytime 6.4 percentage points [95% CI 2.0-10.9], P = 0.002) and compared with adolescents (mean difference at nighttime 10.2 percentage points [95% CI 0.0-20.4], P = 0.049; daytime 7.0 percentage points [95% CI 1.1-12.8], P = 0.014).ConclusionsDiabetes management in young children is complicated by higher variability in insulin requirements, supporting fast-track clinical practice adoption of closed-loop in this vulnerable population.
| S-EPMC6609966 | biostudies-literature
Project description:BackgroundThis study evaluated meal bolus insulin delivery strategies and associated postprandial glucose control while using an artificial pancreas (AP) system.Subjects and methodsThis study was a multicenter trial in 53 patients, 12-65 years of age, with type 1 diabetes for at least 1 year and use of continuous subcutaneous insulin infusion for at least 6 months. Four different insulin bolus strategies were assessed: standard bolus delivered with meal (n=51), standard bolus delivered 15 min prior to meal (n=40), over-bolus of 30% delivered with meal (n=40), and bolus purposely omitted (n=46). Meal carbohydrate (CHO) intake was 1 g of CHO/kg of body weight up to a maximum of 100 g for the first three strategies or up to a maximum of 50 g for strategy 4.ResultsOnly three of 177 meals (two with over-bolus and one with standard bolus 15 min prior to meal) had postprandial blood glucose values of <60 mg/dL. Postprandial hyperglycemia (blood glucose level >180 mg/dL) was prolonged for all four bolus strategies but was shorter for the over-bolus (41% of the 4-h period) than the two standard bolus strategies (73% for each). Mean postprandial blood glucose level was 15.9 mg/dL higher for the standard bolus with meal compared with the prebolus (baseline-adjusted, P=0.07 for treatment effect over the 4-h period).ConclusionsThe AP handled the four bolus situations safely, but at the expense of having elevated postprandial glucose levels in most subjects. This was most likely secondary to suboptimal performance of the algorithm.
| S-EPMC4183919 | biostudies-literature
Project description:This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or rotary encoders, are difficult to adapt to robots that lack discrete mechanical joints. The rigid nature of these sensors runs contrary to the aspirational benefits of soft systems. As truly soft sensor solutions are still in their infancy, motion control of soft robots has so far relied on laboratory-based sensing systems such as motion capture, electromagnetic (EM) tracking, or Fiber Bragg Gratings. In this article, we used embedded flexible sensors known as Smart Braids to sense the contraction of McKibben muscles through changes in inductance. We evaluated closed-loop control on two systems: a revolute joint and a planar, one degree of freedom continuum manipulator. In the revolute joint, our proposed controller compensated for elasticity in the actuator connections. The Smart Braid feedback allowed motion control with a steady-state root-mean-square (RMS) error of [1.5]°. In the continuum manipulator, Smart Braid feedback enabled tracking of the desired tip angle with a steady-state RMS error of [1.25]°. This work demonstrates that Smart Braid sensors can provide accurate position feedback in closed-loop motion control suitable for field applications of soft robotic systems.
| S-EPMC5649447 | biostudies-literature
Project description:BackgroundIn outpatient studies of closed-loop insulin delivery systems, it is not typically practical to obtain blood glucose measurements for an outcome measure. Using a continuous glucose monitoring (CGM) device as both part of the intervention and as the outcome in a clinical trial can give a biased estimate of the treatment effect. A stochastic adjustment has been proposed to correct this problem.Materials and methodsWe performed Monte Carlo simulations to assess the performance of the stochastic adjustment in various scenarios where the CGM device was used passively and when it was used to inform insulin delivery. The resulting bias for using CGM to estimate the percentage of glucose values inside a target range was compared with and without the proposed stochastic adjustment.ResultsCGM bias for estimating the percentage of glucose values 70-180 mg/dL ranged from -6% to +4% in the various scenarios studied. In some circumstances, stochastic adjustment did indeed reduce this CGM bias. However, in other circumstances, stochastic adjustment made the bias worse. Stochastic adjustment tended to underestimate the true percentage of glucose values in range for most, but not all, scenarios considered in these simulations.ConclusionsStochastic adjustment is not a general solution to the problem of CGM bias. The proposed adjustment relies on an implicit assumption that usually does not hold. The appropriate level of adjustment depends on how efficacious the closed-loop system is, which is not typically known in practice.
| S-EPMC3934513 | biostudies-literature