Use of an Automated Bolus Calculator in MDI-Treated Type 1 Diabetes

Schmidt, Signe; Meldgaard, Merete; Serifovski, Nermin; Storm, Camilla; Christensen, Tomas Møller; Gade-Rasmussen, Birthe; Nørgaard, Kirsten

doi:10.2337/dc11-2044

Cited by 107 publications

(178 citation statements)

References 20 publications

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“…43 In case of hyperglycemia or predicted hyperglycemia, the basal-bolus can suggest the patient to administer a correction bolus. The size of the correction bolus can be determined in the same way as the prandial bolus by solving the optimization problem (6).…”

Section: Discussionmentioning

confidence: 99%

“…7 They decreased the risk of error in the determination of the insulin dosage, and are usually preferred by patients with T1D compared to manual bolus calculation. 6,[8][9][10] In the current bolus calculators, the bolus size is proportional to the CHO in the meals, and possibly corrected depending on the current glucose level and the estimated insulin on board. Typical bolus calculators are in the form is the estimated insulin on board.…”

Section: Special Sectionmentioning

confidence: 99%

“…A study showed for example that a large majority of T1D patients underestimate their meal size to avoid insulin overdoses. 5 Bolus calculators have recently been developed for multiple daily injections (MDI) patients 6 and has been successfully been implemented in most insulin pumps since 2002. 7 They decreased the risk of error in the determination of the insulin dosage, and are usually preferred by patients with T1D compared to manual bolus calculation.…”

Section: Special Sectionmentioning

confidence: 99%

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An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Boiroux¹,

Aradóttir²,

Nørgaard

et al. 2016

J Diabetes Sci Technol

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Background: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulinglucose dynamics pose a challenge to the accuracy of such calculators. Method:We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. Results:We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target (P < .01) compared to the conventional bolus calculator. Conclusion:An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

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Section: Discussionmentioning

confidence: 99%

Section: Special Sectionmentioning

confidence: 99%

Section: Special Sectionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Boiroux¹,

Aradóttir²,

Nørgaard

et al. 2016

J Diabetes Sci Technol

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“…22 A randomized controlled trial (RCT) of this technology showed no increase in hypoglycemia frequency despite HbA1c improvement of 0.7-0.8% in participants provided with a BG meter equipped with bolus calculation and simultaneous education on carbohydrate counting. 23 An observational study of the automated bolus calculator showed that fear of hypoglycemia was reduced along with an increased confidence in bolus calculation although actual hypoglycemia rate reduction was not available. 24 A systematic review of this technology shows a trend toward fewer hypoglycemic episodes per week, although failing to achieve statistical significance, and improved postprandial BG values.…”

Section: Better Insulin Deliverymentioning

confidence: 99%

Technology to Reduce Hypoglycemia

Yeoh

Choudhary

2015

J Diabetes Sci Technol

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Hypoglycemia remains a major limiting factor toward the intensification of treatment for diabetes mellitus 1 and is a major cost to patients and to health care systems. 2 Hypoglycemia is often divided into mild or nonsevere hypoglycemia episodes (NSHE), where the person is able to selftreat, and severe hypoglycemia (SH), where external assistance is required. It is difficult to set a biochemical threshold for hypoglycemia, as the glucose threshold at which symptoms of hypoglycemia can be detected vary between individuals and even from day to day within the same person.3 The ADA workgroup on hypoglycemia offers some consensus guidelines, defining an alert value of ≤70 mg/dl, measured through self-monitored blood glucose (SMBG) or subcutaneous continuous glucose monitoring (CGM), as indicative of the potential for developing hypoglycemia. 4 All those treated with insulin secretagogues (sulphonylureas, glinides) and insulin therapy are at risk of hypoglycemia, and this risk is increased in the presence of endogenous insulin deficiency, longer duration of diabetes and longer duration of insulin therapy.5 Epidemiological data suggest that people with type 1 diabetes (T1D) with optimal glycemic control experience up to 2 mild episodes/week, with 20-30% experiencing episodes severe enough to require external help in any given year.6 A small proportion of these people, usually with impaired awareness of hypoglycemia, contribute to more than half of the incidence of SH, having recurrent disabling and sometimes life threatening events. 7CGM studies designed to study normative data suggest that while hypoglycemia is common in all insulin-treated people with diabetes, compared to subjects with type 2 diabetes (T2D), T1D subjects had twice as many hypoglycemic episodes per day, with more hours per day in the hypoglycemic range.8 However, although the incidence per patient of NSHE and SH is lower in T2D, due to the greater prevalence of T2D, the majority of episodes of SH occur in people with T2D. 9 In the US, rates of admissions for hypoglycemia have increased in recent years to 105 admissions per 100 000 person-years. 10 Antecedent hypoglycemia has been shown to blunt symptom and counter-regulatory hormonal responses to subsequent hypoglycemia.11 Over time, recurrent hypoglycemia eventually leads to a reduction in the ability to detect hypoglycemia and mount a hormonal response to it, known as impaired awareness of hypoglycemia (IAH) which can increase the risk of SH 6-fold. 12 In the T1D Exchange clinic registry, ≥ 1 SH events occurred in ~6% of over 9000 participants within 12 months, and was not associated with baseline glycated hemoglobin (HbA1c) levels. 13 Undoubtedly, SH has substantial clinical consequences on morbidity (both physical and psychological) and mortality. The more subtle effects of SH and hypoglycemia unawareness are often AbstractHypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article re...

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“…48 Forty-three people with diabetes using MDI therapy were taught carbohydrate counting first and were then randomized into either using the Accu-Chek Aviva Expert bolus advisor or trained to mentally calculate insulin doses. Subjects had CGM testing at baseline and at the end of the study.…”

Section: Finer Detailsmentioning

confidence: 99%

6^thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, April 25–27, 2013, Riga, Latvia

Alzaid

Schlaeger²,

Hinzmann³

2013

Diabetes Technology & Therapeutics

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International experts in the fields of diabetes, diabetes technology, endocrinology, and pediatrics gathered for the 6 th Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and beyond. The aim of this meeting was to continue setting up a global network of experts in this field and provide an international platform for exchange of ideas to improve life for people with diabetes. The 2013 meeting comprised a comprehensive scientific program, parallel interactive workshops, and two keynote lectures. All these discussions were intended to help identify gaps and areas where further scientific work and clinical studies are warranted. Opening Lecture: News from the World of DiabetesSatish Garg, University of Colorado Denver, Aurora, Colorado Drugs can't do it alone, we need technology too S atish Garg, in his opening lecture, gave insights into the status of diabetes care in general, described the direction of the ongoing diabetes research, and unveiled exciting technological advances coming our way in the near future. He argued that diabetes technology has a major contribution to make on the quality of life and the day-to-day interaction between patient and disease. He put forward a poignant question, saying that we are almost spoiled for choice by the number of antidiabetes agents currently available to treat diabetes, but has that changed patient outcome?Garg advocated that drugs cannot do it alone and that there is a clear gap in the management of diabetes that may be filled in by diabetes-related technologies. Diabetes: uncontrollable costGarg presented recently published data that show the cost of diabetes alone amounts to a staggering $245 billion per year in the United States.1 This figure is expected to rise in the future. Almost one-third of people with diabetes belong to the elderly age group (above 65 years of age), meaning that most individuals have been systematically excluded from research trials and clinical studies. Garg made a timely and passionate plea that elderly people with diabetes must not be left out of the diabetes research agenda.A closer examination of the cost of diabetes expenditure reveals that 80% of the money is used to pay for services and late complications. Eight percent of the total expenditure is used for diabetes technology. Garg suggested that people with diabetes should be cared for from the outset to reduce the expense incurred due to late complications. Glycated hemoglobin and hypoglycemia as measures of healthcare outcomeRecent clinical data of 25,000 people with type 1 diabetes attending 67 leading diabetes centers across the United States were presented by Garg. The T1D Exchange Study 2 is an observational study that showed several interesting findings. First, despite all existing efforts, the pursuit of the target glycated hemoglobin (HbA1c) level remains elusive for many people with diabetes across the United States. Second, the incidence of hypoglycemia remains a major concern for many people with diabetes, especially the elderly. Another and...

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Use of an Automated Bolus Calculator in MDI-Treated Type 1 Diabetes

Cited by 107 publications

References 20 publications

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Technology to Reduce Hypoglycemia

6^thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, April 25–27, 2013, Riga, Latvia

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Use of an Automated Bolus Calculator in MDI-Treated Type 1 Diabetes

Cited by 107 publications

References 20 publications

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Technology to Reduce Hypoglycemia

6thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, April 25–27, 2013, Riga, Latvia

Contact Info

Product

Resources

About

6^thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, April 25–27, 2013, Riga, Latvia