Daniel A. Finan

Quest for the Artificial Pancreas: Combining Technology with Treatment

The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.

Use of Continuous Glucose Monitoring to Estimate Insulin Requirements in Patients with Type 1 Diabetes Mellitus During a Short Course of Prednisone

Background: Insulin requirements to maintain normoglycemia during glucocorticoid therapy and stress are often difficult to estimate. To simulate insulin resistance during stress, adults with type 1 diabetes mellitus (T1DM) were given a three-day course of prednisone. Methods: Ten patients (7 women, 3 men) using continuous subcutaneous insulin infusion pumps wore the Medtronic Minimed CGMS® (Northridge, CA) device. Mean (standard deviation) age was 43.1 (14.9) years, body mass index 23.9 (4.7) kg/m2, hemoglobin A1c 6.8% (1.2%), and duration of diabetes 18.7 (10.8) years. Each patient wore the CGMS for one baseline day (day 1), followed by three days of self-administered prednisone (60 mg/dl; days 2–4), and one post-prednisone day (day 5). Results: Analysis using Wilcoxon signed rank test (values are median [25th percentile, 75th percentile]) indicated a significant difference between day 1 and the mean of days on prednisone (days 2–4) for average glucose level (110.0 [81.0, 158.0] mg/dl vs 149.2 [137.7, 168.0] mg/dl; p = .022), area under the glucose curve and above the upper limit of 180 mg/dl per day (0.5 [0, 8.0] mg/dl·d vs 14.0 [7.7, 24.7] mg/dl·d; p = .002), and total daily insulin dose (TDI), (0.5 [0.4, 0.6] U/kg·d vs 0.9 [0.8, 1.0] U/kg·d; p = .002). In addition, the TDI was significantly different for day 1 vs day 5 (0.5 [0.4, 0.6] U/kg·d vs 0.6 [0.5, 0.8] U/kg·d; p = .002). Basal rates and insulin boluses were increased by an average of 69% (range: 30–100%) six hours after the first prednisone dose and returned to baseline amounts on the evening of day 4. Conclusions: For adults with T1DM, insulin requirements during prednisone induced insulin resistance may need to be increased by 70% or more to normalize blood glucose levels.

Identification of empirical dynamic models from type 1 diabetes subject data

Empirical linear dynamic models have been identified from ambulatory data from two type 1 diabetes subjects in order to determine approximately how far into the future the models could be expected to make reasonably accurate predictions. For a prediction horizon of 30 minutes, FIT values (related to R2 values) of the model predictions for validation data were 46% for one subject and 60% for the other subject. These FIT values correspond to root mean square errors of 14 and 24 mg/dL, respectively. Longer prediction horizons resulted in substantially worse predictions for these ambulatory subject data.

Closed-Loop Control Performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System in a Feasibility Study

Background: This feasibility study investigated the insulin-delivery characteristics of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System—an automated insulin delivery device—in participants with type 1 diabetes. Methods: Thirteen adults with type 1 diabetes were enrolled in this nonrandomized, uncontrolled, clinical-research-center-based feasibility study. The HHM System comprised a continuous subcutaneous insulin infusion pump, a continuous glucose monitor (CGM), and a model predictive control algorithm with a safety module, run on a laptop platform. Closed-loop control lasted approximately 20 hours, including an overnight period and two meals. Results: When attempting to minimize glucose excursions outside of a prespecified target zone, the predictive HHM System decreased insulin infusion rates below the participants’ preset basal rates in advance of below-zone excursions (CGM < 90 mg/dl), and delivered 80.4% less insulin than basal during those excursions. Similarly, the HHM System increased infusion rates above basal during above-zone excursions (CGM > 140 mg/dl), delivering 39.9% more insulin than basal during those excursions. Based on YSI, participants spent a mean ± standard deviation (SD) of 0.2 ± 0.5% of the closed-loop control time at glucose levels < 70 mg/dl, including 0.3 ± 0.9% for the overnight period. The mean ± SD glucose based on YSI for all participants was 164.5 ± 23.5 mg/dl. There were nine instances of algorithm-recommended supplemental carbohydrate administrations, and there was no severe hypoglycemia or diabetic ketoacidosis. Conclusions: Results of this study indicate that the current HHM System is a feasible foundation for development of a closed-loop insulin delivery device.

Calculating the insulin to carbohydrate ratio using the hyperinsulinaemic-euglycaemic clamp : a novel use for a proven technique

In patients with type 1 diabetes, three main variables need to be assessed to optimize meal‐related insulin boluses: pre‐meal blood glucose (BG), insulin to carbohydrate ratio (I : C), and basal insulin. We are presenting data for a novel use of the hyperinsulinaemic‐euglycaemic clamp (HEC) in patients with type 1 diabetes that minimizes the impact of these variables and can be used to determine the I : C.

Effect of Algorithm Aggressiveness on the Performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System

Background: The Hypoglycemia-Hyperglycemia Minimizer (HHM) System aims to mitigate glucose excursions by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The “aggressiveness factor” is a key parameter in the HHM System algorithm, affecting how readily the system adjusts insulin infusion in response to changing CGM levels. Methods: Twenty adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 26 hours. This analysis focused on the effect of the aggressiveness factor on the insulin dosing characteristics of the algorithm and, to a lesser extent, on the glucose control results observed. Results: As the aggressiveness factor increased from conservative to medium to aggressive: the maximum observed insulin dose delivered by the algorithm—which is designed to give doses that are corrective in nature every 5 minutes—increased (1.00 vs 1.15 vs 2.20 U, respectively); tendency to adhere to the subject’s nominal basal dose decreased (61.9% vs 56.6% vs 53.4%); and readiness to decrease insulin below basal also increased (18.4% vs 19.4% vs 25.2%). Glucose analyses by both CGM and Yellow Springs Instruments (YSI) indicated that the aggressive setting of the algorithm resulted in the least time spent at levels >180 mg/dL, and the most time spent between 70-180 mg/dL. There was no severe hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia for any of the aggressiveness values investigated. Conclusions: These analyses underscore the importance of investigating the sensitivity of the HHM System to its key parameters, such as the aggressiveness factor, to guide future development decisions.

Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor

Background: The Predictive Hypoglycemia Minimizer System (“Hypo Minimizer”), consisting of a zone model predictive controller (the “controller”) and a safety supervision module (the “safety module”), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The “aggressiveness factor,” a pivotal variable in the system, governs the speed and magnitude of the controller’s insulin dosing characteristics in response to changes in CGM levels. Methods: Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results. Results: As aggressiveness increased from “conservative” to “medium” to “aggressive,” the controller recommended less insulin (–3.3% vs –14.4% vs –19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent <70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values. Conclusion: The Hypo Minimizer’s controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics.