Joon Bok Lee

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.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

CONTEXT Closed-loop control (CLC) relies on an individuals open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort. OBJECTIVE The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC. DESIGN This study reports a multicenter, outpatient, randomized, crossover clinical trial. PATIENTS Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites. INTERVENTIONS Each subjects insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subjects followed their usual meal-plan and had an unannounced exercise session. MAIN OUTCOMES AND MEASURES Time in the glucose range was 80-140 mg/dL, compared between both arms. RESULTS Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL. CONCLUSIONS A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

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.

Model-based personalization scheme of an artificial pancreas for Type 1 diabetes applications

Automated controllers designed to regulate blood glucose concentrations in people with Type 1 diabetes mellitus (T1DM) must avoid hypoglycemia (blood glucose <;70 mg/dl) while minimizing hyperglycemia (>180 mg/dl), a challenging task. In this paper, a model-based control design approach with a personalized scheme based on readily available clinical factors is applied to a linearized control-relevant model of subject insulin-glucose response profiles. An insulin feedback strategy is included with specific personalization settings and variations in a tuning parameter, τc. The control strategy is challenged by an unannounced meal disturbance with 50 g carbohydrate content. A set of metrics are introduced as a method of evaluating the performance of different controllers. In-silico simulations of ten subjects in the Food and Drug Administration accepted Universities of Virginia and Padova metabolic simulator indicate that the personalization strategy with a τc setting of 270 minutes gives very good controller performance. Post-prandial glucose concentration peaks of 183 mg/dl were achieved with 97% of the total simulation time spent within a safe glycemic zone (70-180 mg/dl), without hypoglycemic incidents and without requiring a time-consuming model identification process.

Novel insulin delivery profiles for mixed meals for sensor-augmented pump and closed-loop artificial pancreas therapy for type 1 diabetes mellitus.

Background: Maintaining euglycemia for people with type 1 diabetes is highly challenging, and variations in glucose absorption rates with meal composition require meal type specific insulin delivery profiles for optimal blood glucose control. Traditional basal/bolus therapy is not fully optimized for meals of varied fat contents. Thus, regimens for low- and high-fat meals were developed to improve current insulin pump therapy. Method: Simulations of meals with varied fat content demonstrably replicated published data. Subsequently, an insulin profile library with optimized delivery regimens under open and closed loop for various meal compositions was constructed using particle swarm optimization. Results: Calculations showed that the optimal basal bolus insulin profiles for low-fat meals comprise a normal bolus or a short wave. The preferred delivery for high-fat meals is typically biphasic, but can extend to multiple phases depending on meal characteristics. Results also revealed that patients that are highly sensitive to insulin could benefit from biphasic deliveries. Preliminary investigations of the optimal closed-loop regimens also display bi- or multiphasic patterns for high-fat meals. Conclusions: The novel insulin delivery profiles present new waveforms that provide better control of postprandial glucose excursions than existing schemes. Furthermore, the proposed novel regimens are also more or similarly robust to uncertainties in meal parameter estimates, with the closed-loop schemes demonstrating superior performance and robustness.

A Personalized Week-to-Week Updating Algorithm to Improve Continuous Glucose Monitoring Performance

Background: Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends. Methods: In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week. Results: The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance. Conclusions: Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

Challenges Associated With Exercise Studies in Type 1 Diabetes

In type 1 diabetes (T1D), even short bursts of physical activity can affect blood glucose. Fear of hypoglycemia is a barrier to exercise participation, and prevention of early and overnight hypoglycemia following activities is a major concern. In our development of closed-loop artificial pancreas systems, we have observed that physical activity can lead to hypoglycemia despite suspension of insulin delivery. Thus, to inform and improve upon these systems, we compared changes in glycemia following 2 sprinting bouts of differing durations. After signing informed consent, 12 recreationally active, healthy adults (7 F, 5 M) with T1D (42.2 ± 15.4 years, mean ± SD) participated. On day 1, subjects were fitted with a Dexcom G4®Platinum CGM (Dexcom®, San Diego, CA), an ActiGraph wGT3X-BT activity monitor (ActiGraph, Pensacola, FL), and a Polar® heart rate monitor (Polar Electro Oy, Finland). On Days 2 and 4, subjects consumed a Glucerna® Hunger SmartTM Shake containing 14-16 grams carbohydrate for breakfast, for which they took a usual dose of rapid-acting insulin. Three hours later, when insulin and glucose levels were stable, they performed a supervised 10or 60-second sprint in randomized order at a local track. While heart rate and energy expenditure differed between sprints trials (P < .001), no differences were noted between presprint and postsprint fingerstick blood glucose levels. However, their CGM glucose values during 30 minutes post 10-second sprints had a greater percentage of time in the 80-140 mg/dL range (median 92.9% [IQR 0-100]) than following the 60-second sprints (0% [0-21.4], P < .05). This finding was primarily the result of a higher starting glucose level before exercise, as neither the 10-second nor 60-second sprint caused a clinically significant change in median glucose levels over the 30-minute period: –7.5 mg/dL [–16, –5] (P = ns) for the 10-second sprint versus +28.5 mg/dL [–2, +34] (P = ns) for the 60second sprint (Figure 1). In addition, 5 subjects (42%) consumed carbohydrates either just before or within the hour after the sprints. There were no differences in time spent <70 mg/dL or in the number of hypoglycemic events during the 30 minutes postsprints and no differences in any 625084 DSTXXX10.1177/1932296815625084Journal of Diabetes Science and TechnologyColberg et al research-article2015

Response to Comment on Doyle et al. Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms. Diabetes Care 2014;37:1191–1197

Doyle et al. (1) eloquently set out minimum common requirements for artificial pancreas (AP) trials to accelerate progress in this important field. The authors focus on engineering aspects and glucose outcomes critical to the success of AP technology, but we would like to draw attention to the importance of psychosocial aspects of AP technology. A greater understanding of the “lived experience” is crucial to ensure that technologies develop successfully and are fit to meet the demands of living with diabetes in addition to glycemic control. Only people with type 1 diabetes know whether they are able to meet the demands of AP technologies long term. Continuous glucose monitors have received mixed reviews from people with diabetes with strong views both for and against (2). Loss of connection, short durability of sensors (particularly during physical activity when there is …