Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study

Thabit, Hood; Lubina-Solomon, Alexandra; Stadler, Marietta; Leelarathna, Lalantha; Walkinshaw, Emma; Pernet, A; Allen, Janet M.; Iqbal, Ahmed; Choudhary, Pratik; Kumareswaran, Kavita; Nodale, Marianna; Nisbet, Chloe; Wilinska, Malgorzata E.; Barnard, Katharine; Dunger, David B.; Heller, Simon; Amiel, Stephanie A.; Evans, Mark L.; Hovorka, Roman

doi:10.1016/s2213-8587(14)70114-7

Cited by 145 publications

(110 citation statements)

References 31 publications

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“…[12][13][14] This new study confirms the previous findings and adds two unique features: (1) a portable platform using a consumer electronics device (smartphone) to run the CLC system and all CGM, pump, and remote communications and (2) a control algorithm specifically designed to ''slide'' the patients' glucose levels to a target of 120 mg/dL at wakeup, thereby resetting their metabolic state overnight to near-normoglycemic morning BG levels, on consecutive nights. As a result, this study demonstrated not only significant improvement in overnight glucose control on CLC, but also significant improvement of patients' control on the next day.…”

Section: Discussionmentioning

confidence: 99%

“…9 The transition of CLC to a wearable outpatient system began in 2012 with the introduction of the Diabetes Assistant (DiAs)-the first wearable CLC system using a smartphone as a computational platform for its control algorithm. 10,11 Other recent trials confirmed the effectiveness of overnight CLC at diabetes camps for children 12 and at patients' homes, 13,14 placing laptop computers equipped with control algorithms at their patients' bedside.…”

mentioning

confidence: 91%

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Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

BrownSue

KovatchevBoris²,

BretonMarc³

et al. 2015

Diabetes Technology & Therapeutics

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Background: Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to ''reset'' the patient overnight to nearnormoglycemia each morning, for several consecutive nights. Subjects and Methods: Ten insulin pump users with type 1 diabetes (mean age, 46.4 -8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h. Results: Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P < 0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P < 0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P < 0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P < 0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P = 0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r = 0.52, P < 0.01. Conclusions: Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were ''reset'' to near-normoglycemia each morning.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 91%

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

BrownSue

KovatchevBoris²,

BretonMarc³

et al. 2015

Diabetes Technology & Therapeutics

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“…An iPhone 4S was used in [28] to run a control algorithm for bi-hormonal therapy that was evaluated in an outpatient study of preadolescent children with T1DM. A hand-held computer [29] was used to run the control algorithm in [30,31]. A free-living conditions clinical trial was performed using a smartphone that implemented the controller [32].…”

Section: Hardware Configurations Of the Ap Systemmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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“…Numerous studies have emerged testing AP control algorithms initially under clinical research settings, 56 subsequently to controlled diabetes camp use 55,57 and now being brought to home use. 58,59 These studies have shown exciting and promising data on improved glucose variability, increasing time in target BG range, and without an increase in hypoglycemia frequency or SH rates. The low rates of sensor glucose < 70 mg/dl in most of these studies, some of which have been tested for up to 4 weeks at home, has been very impressive.…”

Section: Automated 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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Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study

Cited by 145 publications

References 31 publications

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Technology to Reduce Hypoglycemia

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