Fully Integrated Artificial Pancreas in Type 1 Diabetes

Modular Closed-Loop Glucose Control Maintains Near Normoglycemia

Marc Breton; Anne Farret; Daniela Bruttomesso; Stacey Anderson; Lalo Magni; Stephen Patek; Chiara Dalla Man; Jerome Place; Susan Demartini; Simone Del Favero; Chiara Toffanin; Colleen Hughes-Karvetski; Eyal Dassau; Howard Zisser; Francis J. Doyle III; Giuseppe De Nicolao; Angelo Avogaro; Claudio Cobelli; Eric Renard; Boris Kovatchev

Disclosures

Diabetes. 2012;61(9):2230-2237. 

In This Article

Abstract and Introduction

Abstract

Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9–10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.

Introduction

The maintenance of close-to-normal blood glucose (BG) levels slows the onset and progression of long-term microvascular complications in patients with type 1 diabetes,[1] therefore, the ultimate therapeutic goal of type 1 diabetes is to restore near normoglycemia.[2] In the past decade, the advent of both continuous glucose monitoring (CGM)[3–5] and automated CGM-assisted insulin delivery, known as artificial pancreas or closed-loop control (CLC),[6,7] have accelerated the achievement of this goal. Although the traditional therapeutic strategies target long-term average BG reduction measured by HbA1c,[1,8] CLC aims to minimize, in real time, glucose variability and prevent extreme glucose excursions (e.g., hypo- and hyperglycemia).[9] This objective is achieved via frequent insulin adjustment modulated by a CLC algorithm, which takes into account CGM readings and the effects of previous insulin infusions to continuously compute the amount of insulin dose to be administered.[10]

Historically (rev. in [7]), systems controlling BG automatically can be traced back decades ago to when the possibility for external BG regulation was demonstrated using intravenous BG measurements and intravenous infusions of insulin and glucose.[11,12] However, these systems were cumbersome and unsuitable for long-term, or outpatient, use. The development of both CGM and portable devices for continuous subcutaneous insulin infusion (CSII) incited the implementation of subcutaneous CLC systems.[13] Promising results have been reported by several research groups.[7,13–22] Most of these studies point out the superiority of CLC over standard CSII therapy in terms of increased time within target glucose range (typically 3.9–10 mmol/L), reduced incidence of hypoglycemia, and better overnight control.

However, to date, there are no randomized crossover studies of fully integrated CLC, defined as having all of the following three components: 1) automated data transfer from the CGM to the controller, 2) real-time control action, and 3) automated command of the insulin pump. Only one previously reported study has a state-of-the-art randomized crossover design,[18] but it lacks automated data transfer.[15] Conversely, the studies that use fully integrated glucose control[13,14,17,19–22] do not follow a randomized crossover design.

We have developed a novel approach to CLC algorithm design based on a modular architecture concept.[7,23,24] Such a modular architecture would allow diverse components to be seamlessly integrated in a functional hierarchical system that can be sequentially deployed in clinical and ambulatory studies. Modularity allows a stepwise regulated approach: first, algorithmic modules designed to improve patient safety are implemented; and second, increasingly complex modules designed to optimally modulate insulin delivery in real time are used.

With this background in mind, we now present two multicenter randomized crossover trials using two fully integrated subcutaneous CLC systems based on the modular architecture concept (Fig. 2). Both systems aimed at maintaining near normoglycemia in the 3.9–10 mmol/L target range and implemented a strategy known as control to range (CTR). The first system, standard CTR (sCTR), included a safety supervision module (SSM) mitigating the risk for hypoglycemia, and an sCTR algorithm activated when hyperglycemia was predicted. The task of sCTR was to prevent hypoglycemia and mitigate extreme hyperglycemia, without truly aiming for optimal glucose control. The second system, enhanced CTR (eCTR), included the same SSM to prevent hypoglycemia but coupled with a more sophisticated model predictive control (MPC) algorithm. The task of eCTR was optimal glucose control within a target range.

For both algorithms, we assess effectiveness of the system as reflected by time spent in near normoglycemia (3.9–10 mmol/L), average glucose, and glucose variability. In addition, we include algorithm-specific metrics corresponding to the design of the two algorithms: degree of mitigation of hypoglycemia for sCTR and time spent in tight glycemic control (4.4–7.8 mmol/L) for eCTR.

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