Lauren M. Huyett

Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms

In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

OBJECTIVE To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions. RESEARCH DESIGN AND METHODS After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70–180 mg/dL. RESULTS Mean time in range 70–180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period. CONCLUSIONS This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

Glucose Sensing in the Peritoneal Space Offers Faster Kinetics than Sensing in the Subcutaneous Space

The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because subcutaneous blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), subcutaneous sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space. We compared the temporal response characteristics of simultaneously placed subcutaneous and intraperitoneal sensors during intravenous glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous intravenous sensing, blood draws, and intraarterial sensing, we found that intraperitoneal kinetics were more than twice as fast as subcutaneous kinetics (mean time constant of 5.6 min for intraperitoneal vs. 12.4 min for subcutaneous). Combined with the known faster kinetics of intraperitoneal insulin delivery over subcutaneous delivery, our findings suggest that artificial pancreas technologies may be optimized by sensing glucose and delivering insulin in the intraperitoneal space.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial

OBJECTIVE As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)–based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS AP improved percent time 70–140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70–180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. CONCLUSIONS Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

Response to Comment on Pinsker et al. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care 2016;39:1135–1142

We thank Dr. Steil (1) for critically reviewing our study comparing model predictive control (MPC) and proportional integral derivative (PID) control for the artificial pancreas (2). We agree that MPC and PID both come in many variants and that there are many successful trials of PID control for automated insulin delivery. We acknowledged in our study that both controllers performed very well overall, even after the 65-g unannounced meal was accounted for, and did so with low rates of hypoglycemia. We recognize the value in comparing results across different studies and want to emphasize that we did not intend to dismiss studies with different designs. However, it is not possible to have an equitable comparison of MPC versus PID controllers through such meta-analyses. Our study was specifically designed for as fair and balanced a comparison as possible between the …

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Minority groups and the artificial pancreas: who is (not) in line?

Technological advancements in diabetes management are underway, with industrial eff orts in place to design and assess artifi cial pancreas systems. Although it is sometimes diffi cult to separate hype from hope, the general consensus is that some form of closed-loop insulin delivery system will become commercially available as early as 2017. The present academic debate has focused on system design choices, such as whether to include glucagon and the level of system automation, but what is less clear is who is going to be fi rst in line to use the technology. At present, data show that type 1 diabetes is most prevalent in the non-Hispanic white population, although there is evidence of an increasing burden of this type of diabetes in racial minority groups as well as the non-Hispanic white population.Additionally, there are disparities in health outcomes in these patients according to ethnic, racial, and socioeconomic groups, even after accounting for deprivation and access to insulin pump therapy. For children with type 1 diabetes in particular, inequalities in access to modern treatments increases the risks of acute and long-term complications. This inequality creates an issue for study recruitment because many artifi cial pancreas trials require previous experience with insulin pumps and continuous glucose monitoring for enrolment. As a result, existing data might be limited to a specifi c cohort of patients, excluding those who do not prefer to use these technologies or cannot obtain access to them. The US National Institutes of Health (NIH) supports open enrolment so that women and ethnic and racial minorities are included in its funded clinical research trials. These demographics are reported annually to NIH as part of all NIH-funded projects, although these details are not always fully reported in publications. We surveyed the scientifi c literature for publications of artifi cial pancreas clinical studies. A total of 99 publications (with 59 supplementary fi les) were identifi ed, 29 of which were published in 2015 or 2016. Computerised text searches and manual screening for words related to race, ethnicity, gender, age, and socioeconomic status were done on the 158 fi les. Age was reported in 98% of the publications and gender in 87%. However, only six of 99 studies mentioned race or ethnicity, with only four studies reporting the number of trial participants belonging to diff erent racial or ethnic groups (fi gure). In these four studies, 43 of 46, 24 of 25, 11 of 15, and 11 of 12 participants were nonHispanic whites. There were no matches for the words “socioeconomic”, “literacy”, “numeracy”, or “income”. Our review of the scientifi c literature shows that it has been common practice in the published research on artifi cial pancreas technology to include data for age and gender, but often no additional information is given on race, ethnicity, socioeconomic status, and literacy and numeracy status in fi nal publications. From the currently published studies, it is impossible to determine the diversity of participants being served by artifi cial pancreas clinical research beyond age and gender, although it is likely that the overwhelming majority of participants are non-Hispanic white people. New technology for diabetes care might have limited value if some groups do not have access or the skills to leverage these new tools and if research has not explored potential cultural, social, and economic barriers to a particular technological innovation. There are already many economic barriers to obtaining the components of an artifi cial pancreas system, with little to no insurance coverage of continuous glucose monitors by some insurance carriers and national health systems. What is even less clear are the cultural and lifestyle barriers that manufacturers of artifi cial pancreas systems will need to overcome before such a system becomes mainstream, and whether race and ethnicity will aff ect access, uptake, and sustainability of an artifi cial pancreas system that reaches the market.

Intraperitoneal Insulin Delivery Provides Superior Glycemic Regulation to Subcutaneous Insulin Delivery in Model Predictive Control-based Fully-automated Artificial Pancreas in Patients with Type 1 Diabetes: A Pilot Study.

To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP).

Preliminary Evaluation of a Long-Term Intraperitoneal Glucose Sensor With Flushing Mechanism.

Encapsulation is known to deteriorate the performance of subcutaneous (SQ) continuous glucose monitors (CGMs), preventing these devices from meeting the long-term functionality requirements for widespread use and creating a bottleneck in artificial pancreas (AP) development. While recent studies of implanted SQ sensors have shown promising results, there is still much room for improvement, including the reduction of encapsulation-induced sensor lag. We present a proof-of-concept study of a novel flushing assembly to routinely clean the sensor surface, thereby prolonging its lifetime. Placing the sensor in the intraperitoneal (IP) space allows flushing with saline that would not be possible in the restricted SQ space. Fluorescent glucose sensors were implanted in the SQ or IP space of sheep. Sensors were provided by the manufacturer in a lengthened, tethered format. The IP sensors were modified with silicone tubing, flush port, Dacron cuff, and adaptors to allow flushing with saline solution. Experiments were conducted under an IACUC-approved protocol by BioSurg, Inc (Davis, CA). After preliminary testing to optimize the flushing procedure, long-term responsiveness was evaluated with an IP sensor placed in 1 sheep and an SQ sensor placed in a second sheep. The IP sensor was flushed weekly with saline. Glucose response challenges were performed periodically over 3 months by infusing 0.5 g/kg dextrose through an ear vein over 60 s (13 challenges over 114 days for IP, 9 challenges over 91 days for SQ). The results are summarized in Figure 1. The IP sensor demonstrated anomalously slow response during the first challenge (day 8) due to tissue trauma following implantation, which is known to cause inflammatory response. Excluding day 8, the IP sensor maintained consistent responsiveness throughout the 114-day period, with time to half-maximum (t 1/2 ) between 2.7 and 4.7 min and time to maximum (t max ) between 11.6 and 17.2 min. Conversely, the nonflushed sensor in the SQ space gradually lost responsiveness, with t 1/2 between 2.6 and 13.5 min and t max between 9.7 and 72 min. By 91 days following implantation, the SQ sensor signal did not peak within the 60-min testing period (see Figure 1B). The development of long-term implantable CGMs is a key step toward making this technology more practical; however, CGM performance is hindered by diffusion lag and loss of sensitivity caused by encapsulation driven by the foreign body response. The IP space has already been shown to be valuable to AP applications, with experimental evidence showing both faster insulin action and faster glucose sensing in this space. The performance of the flushed IP sensor presented here far exceeded that of the conventional SQ sensor after long implantation periods, showing promise for further investigation of the flushing method. This proof-of-concept study introduces the use of a flushing mechanism to allow CGM in the IP space with consistent responsiveness during 3 months in vivo. Future iterations of this system will utilize automated flushing of the sensing element with small volumes of fluid drawn from the patient’s bodily fluids. Data generated from this study will guide the development of an IP CGM to enable an implantable AP and improve practicality of CGM use for day-to-day diabetes therapy. 640542 DSTXXX10.1177/1932296816640542Journal of Diabetes Science and TechnologyHuyett et al letter2016

The impact of glucose sensing dynamics on the closed-loop artificial pancreas

A closed-loop artificial pancreas (AP) to provide automated treatment for people with type 1 diabetes mellitus has the potential to improve patient health outcomes; however, the systems success hinges on its ability to quickly detect and react to changing blood glucose concentrations (BG). In this study, the impact of measurement lag on AP robust stability, performance, and time-domain disturbance rejection was investigated and compared to the case of an ideal BG sensor. The analysis was performed for an AP using either intraperitoneal (IP) or subcutaneous (SC) insulin delivery routes. Decreasing the sensor lag resulted in a higher tolerance for model uncertainty for robust stability and performance. In the case of a 20 min sensor lag, the time spent in hyperglycemia after a meal disturbance was 59±19 min and 120 ±22 min for IP and SC insulin, respectively. Switching the sensor to the ideal case decreased the time spent in hyperglycemia by 21±8 min for IP insulin and by 13±3 min for SC insulin. Since the SC system already contains large actuation delays, a faster sensor is not as important to improved performance as it is in the IP case. Significant gains in AP performance can be achieved with the use of IP insulin, but these improvements will not be fully realized unless faster glucose sensing is implemented as well.