A constrained sub‐optimal controller for glucose regulation in type 1 diabetes mellitus

Ghosh, Subhojit; Maka, Srinivasu

doi:10.1002/oca.2063

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Diabetes, as a chronic endocrine disease, has a high incidence in the middle-aged and elderly population, seriously affecting the physical health and quality of life 1,2. In recent years, with the improvement of living standards and medical conditions, the number of aging population has increased, and the incidence of diabetes has also increased.…”

Section: Introductionmentioning

confidence: 99%

Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes

Liu

et al. 2019

Pak J Med Sci

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Objective: To investigate the clinical efficacy of insulin glargine combined with acarbose in the treatment of elderly patients with diabetes. Methods: One hundred and forty-four elderly patients with diabetes who received treatment between December 2016 and December 2017 in Binzhou People’s Hospital, China, were selected and divided into a control group and an observation group, 72 each, using random number table. The control group was treated with insulin glargine, while the observation group was treated with insulin glargine combined with acarbose. The therapeutic effect, improvement of quality of life and adverse reactions were compared between the two groups. Results: After treatment, fasting blood glucose (FBG), 2h postprandial blood glucose (PBG) and glycosylated hemoglobin (Hb Alc) of the two groups were lower than those before treatment, and the decrease degree of the observation group was significantly larger than that of the control group (P<0.05). The time needed for blood glucose reaching the standard level and daily insulin dosage of the observation group were significantly lower than that of the control group, and the differences were statistically significant (P<0.05). SF-36 scale score of the observation group was significantly better than the control group, and the difference was statistically significant (P<0.05). There was no significant difference in the incidence of adverse reactions between the two groups (P>0.05). Conclusion: The combination of insulin Glargine and Acarbose can significantly control the blood glucose level of elderly patients with diabetes, improve the biochemical indicators, and enhance the quality of life. It is worth promotion in clinical practice. doi: https://doi.org/10.12669/pjms.35.3.86 How to cite this:Li J, Ji J, Liu F, Wang L. Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes. Pak J Med Sci. 2019;35(3):---------. doi: https://doi.org/10.12669/pjms.35.3.86 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes

Liu

et al. 2019

Pak J Med Sci

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“…The ultimate goal of an AP is finding the optimal insulin rates that can effectively maintain normal BG levels, and infusing it in such way that glycemic profiles can mimic the body's natural regulatory mechanism (Ali and Padhi, 2011). To achieve this, most closed-loop control strategies compute the optimal insulin bolus to be administrated on the basis of plasma glucose and plasma insulin concentration (De Paula et al, 2015;Ghosh and Maka, 2014;Hughes et al, 2011). This can partly be achieved by the availability of continuous glucose monitors that measure glucose levels in the interstitial fluid and considering the dynamics between this evaluation and plasmatic concentrations (Sparacino et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques

Ávila¹,

Paula²,

Reinoso³

2018

Biosystems

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The ultimate goal of an artificial pancreas is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most closed-loop control strategies need to compute the optimal insulin action on the basis of precedent glucose and insulin levels. Unlike glucose levels which can be measured in real-time, unavailability of insulin sensors makes it essential the use of mathematical models to estimate plasma insulin concentrations. Between others, filtering techniques based on a generalization of the Kalman filter (KF) have been the most widely applied in the estimation of hidden states in nonlinear dynamic systems. Nevertheless, poor predictability of BG levels is a key issue since the glucose-insulin dynamics presents great inter- and intra-patient variability. Here, the question arises as to whether glycemic variability is not properly taken into account in models formulations and whether or it would compromise proper estimation of plasma insulin concentration. In order to tackle this point, a deterministic model describing glucose-insulin interaction plus a stochastic process to account for BG fluctuations were incorporated into the extended (EKF), cubature (CKF) and unscented (UKF) configurations of the Kalman filter to provide an estimate of the plasma insulin concentration. We found that for low glycemic variability, insulin state estimation can be attained with acceptable accuracy; however, as glycemic variability rises, Kalman filters rapidly degrade their performance as a consequence of large nonlinearities.

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“…Continuous glucose monitoring will certainly improve daily diabetes treatment, but the realization of this promise awaits a complementary shift in the way that glycemic data are processed . Similarly to other regulatory loops in physiological systems, accessibility to some key variables requires a highly invasive mechanism, which is not always feasible . Moreover, periodical measuring of plasma glucose to check if the AP is performing correctly results impracticable for ambulatory patients, particularly at night time .…”

Section: Introductionmentioning

confidence: 99%

A grid-based tool for optimal performance monitoring of a glycemic regulator

Ávila

Martínez

2015

Optim. Control Appl. Meth.

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Summary Recent technology breakthroughs towards a fully automated artificial pancreas give rise to the need of new monitoring tools aiming at increasing both reliability and performance of a closed‐loop glycemic regulator. Based on error grid analysis, an insightful monitoring tool is proposed to assess if a given closed‐loop implementation respects its specification of an optimally performing glycemic regulator under uncertainty. The optimal behavior specification is obtained using linearly solvable Markov decision processes, whereby the Bellman optimality equation is made linear through an exponential transformation that allows obtaining the optimal control policy in an explicit form. The specification for the desired glucose dynamics is learned using Gaussian processes for state transitions in an optimally performing artificial pancreas. By means of the proposed grid, the specification is vis‐à‐vis compared with glucose sensor readings so that any significant deviation from the expected closed‐loop performance under abnormal or faulty scenarios can be detected. Copyright © 2015 John Wiley & Sons, Ltd.

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A constrained sub‐optimal controller for glucose regulation in type 1 diabetes mellitus

Cited by 4 publications

References 27 publications

Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes

Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes

Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques

A grid-based tool for optimal performance monitoring of a glycemic regulator

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