Stress Testing of an Artificial Pancreas System With Pizza and Exercise Leads to Improvements in the System's Fuzzy Logic Controller.

BACKGROUND: Under controlled conditions, the Dose Safety artificial pancreas (AP) system controller, which utilizes "fuzzy logic" (FL) methodology to calculate and deliver appropriate insulin dosages based on changes in blood glucose, successfully managed glycemic excursions. The aim of this study was to show whether stressing the system with pizza (high carbohydrate/high fat) meals and exercise would reveal deficits in the performance of the Dose Safety FL controller (FLC) and lead to improvements in the dosing matrix. METHODS: Ten subjects with type 1 diabetes (T1D) were enrolled and participated in 30 studies (17 meal, 13 exercise) using 2 versions of the FLC. After conducting 13 studies with the first version (FLC v2.0), interim results were evaluated and the FLC insulin-dosing matrix was modified to create a new controller version (FLC v2.1) that was validated through regression testing using v2.0 CGM datasets prior to its use in clinical studies. The subsequent 17 studies were performed using FLC v2.1. RESULTS: Use of FLC v2.1 vs FLC v2.0 in the pizza meal tests showed improvements in mean blood glucose (205 mg/dL vs 232 mg/dL, P = .04). FLC v2.1 versus FLC v2.0 in exercise tests showed improvements in mean blood glucose (146 mg/dL vs 201 mg/dL, P = .004), percentage time spent >180 mg/dL (19.3% vs 46.7%, P = .001), and percentage time spent 70-180 mg/dL (80.0% vs 53.3%, P = .002). CONCLUSION: Stress testing the AP system revealed deficits in the FLC performance, which led to adjustments to the dosing matrix followed by improved FLC performance when retested.

Use of a "fuzzy logic" controller in a closed-loop artificial pancreas.

BACKGROUND: Most current model-based approaches to closed-loop artificial pancreas systems rely on mathematical equations describing the human glucoregulatory system; however, incorporating the various physiological parameters (e.g., illness, stress) into these models has been problematic. We evaluated a fully automated "fuzzy logic" (FL) closed-loop insulin dosing controller that does not require differential equations of the glucoregulatory system and allows clinicians to personalize dosing aggressiveness to meet individual patient requirements. SUBJECTS AND METHODS: This pilot study evaluated the FL controller in the setting of bed rest in a very controlled environment. Two carbohydrate-controlled meals were given (30 g at 8 a.m. and 60 g at 2 p.m. without meal announcement or premeal bolus. The primary end point of the study was avoidance of hypoglycemia, defined at <60 mg/dL. Multiple end points related to the frequency and severity of hyperglycemia and hypoglycemia were also assessed. RESULTS: Of the 12 subjects we recruited, 10 were enrolled, and seven completed the study. Two of the enrolled subjects were discontinued because of hypoglycemia; the other was discontinued because of sensor failure. Seven of the 10 subjects who completed the study had average blood glucose values of 165 mg/dL and were within a specified target blood glucose range (70-200 mg/dL) for 76% of the 24-h study period. CONCLUSIONS: Our findings suggest that the FL controller provides a viable alternative to model-based controllers as a component of a closed-loop insulin delivery system. Furthermore, our FL controller allows clinicians to easily specify the level of glucose control based on each patient's clinical needs.

Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor.

BACKGROUND: Physicians tailor insulin dosing based on blood glucose goals, response to insulin, compliance, lifestyle, eating habits, daily schedule, and fear of and ability to detect hypoglycemia. METHOD: We introduce a method that allows a physician to tune a fuzzy logic controller (FLC) artificial pancreas (AP) for a particular patient. It utilizes the physician's judgment and weighing of various factors. The personalization factor (PF) is a scaling of the dose produced by the FLC and is used to customize the dosing. The PF has discrete values of 1 through 5. The proposed method was developed using a database of results from 30 University of Virginia/Padova Metabolic Simulator in silico subjects (10 adults, 10 adolescents, and 10 children). Various meal sizes and timing were used to provide the physician information on which to base an initial dosing regimen and PF. Future decisions on dosing aggressiveness using the PF would be based on the patient's data at follow-up. RESULTS: Three examples of a wide variation in diabetes situations are given to illustrate the physician's thought process when initially configuring the AP system for a specific patient. CONCLUSIONS: Fuzzy logic controllers are developed by encoding human expertise into the design of the controller. The FLC methodology allows for the real-time scaling of doses without compromising the integrity of the dosing rules matrix. The use of the PF to individualize the AP system is enabled by the fuzzy logic development methodology.

Goiter and multiple food allergies.

Severe iodine deficiency results in impaired thyroid hormone synthesis and thyroid enlargement. In the United States, adequate iodine intake is a concern for women of childbearing age and pregnant women. Beyond this high risk group iodine deficiency is not considered to be a significant problem. This case report describes a 12-year-old male with severe iodine deficiency disorder (IDD) resulting from restricted dietary intake due to multiple food allergies. We describe iodine replacement for this patient and continued monitoring for iodine sufficiency. Children with multiple food allergies, in particular those with restrictions to iodized salt and seafood, should be considered high risk for severe iodine deficiency.