Peter Pesl

Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.

Feasibility Study of a Bio-inspired Artificial Pancreas in Adults with Type 1 Diabetes

BACKGROUND This study assesses proof of concept and safety of a novel bio-inspired artificial pancreas (BiAP) system in adults with type 1 diabetes during fasting, overnight, and postprandial conditions. In contrast to existing glucose controllers in artificial pancreas systems, the BiAP uses a control algorithm based on a mathematical model of β-cell physiology. The algorithm is implemented on a miniature silicon microchip within a portable hand-held device that interfaces the components of the artificial pancreas. MATERIALS AND METHODS In this nonrandomized open-label study each subject attended for a 6-h fasting study followed by a 13-h overnight and post-breakfast study on a separate occasion. During both study sessions the BiAP system was used, and microboluses of insulin were recommended every 5 min by the control algorithm according to subcutaneous sensor glucose levels. The primary outcome was percentage time spent in the glucose target range (3.9-10.0 mmol/L). RESULTS Twenty subjects (55% male; mean [SD] age, 44 [10] years; duration of diabetes, 22 [12] years; glycosylated hemoglobin, 7.4% [0.7%] [57 (7) mmol/mol]; body mass index, 25 [4] kg/m(2)) participated in the fasting study, and the median (interquartile range) percentage time in target range was 98.0% (90.8-100.0%). Seventeen of these subjects then participated in the overnight/postprandial study, where 70.7% (63.9-77.4%) of time was spent in the target range and, reassuringly, 0.0% (0.0-2.3%) of time was spent in hypoglycemia (<3.9 mmol/L). CONCLUSIONS The BiAP achieves safe glycemic control during fasting, overnight, and postprandial conditions.

Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes: A 24-Hour Randomized Controlled Crossover Study.

Background: The Bio-inspired Artificial Pancreas (BiAP) is a closed-loop insulin delivery system based on a mathematical model of beta-cell physiology and implemented in a microchip within a low-powered handheld device. We aimed to evaluate the safety and efficacy of the BiAP over 24 hours, followed by a substudy assessing the safety of the algorithm without and with partial meal announcement. Changes in lactate and 3-hydroxybutyrate concentrations were investigated for the first time during closed-loop. Methods: This is a prospective randomized controlled open-label crossover study. Participants were randomly assigned to attend either a 24-hour closed-loop visit connected to the BiAP system or a 24-hour open-loop visit (standard insulin pump therapy). The primary outcome was percentage time spent in target range (3.9-10 mmol/l) measured by sensor glucose. Secondary outcomes included percentage time in hypoglycemia (<3.9 mmol/l) and hyperglycemia (>10 mmol/l). Participants were invited to attend for an additional visit to assess the BiAP without and with partial meal announcements. Results: A total of 12 adults with type 1 diabetes completed the study (58% female, mean [SD] age 45 [10] years, BMI 25 [4] kg/m2, duration of diabetes 22 [12] years and HbA1c 7.4 [0.7]% [58 (8) mmol/mol]). The median (IQR) percentage time in target did not differ between closed-loop and open-loop (71% vs 66.9%, P = .9). Closed-loop reduced time spent in hypoglycemia from 17.9% to 3.0% (P < .01), but increased time was spent in hyperglycemia (10% vs 28.9%, P = .01). The percentage time in target was higher when all meals were announced during closed-loop compared to no or partial meal announcement (65.7% [53.6-80.5] vs 45.5% [38.2-68.3], P = .12). Conclusions: The BiAP is safe and achieved equivalent time in target as measured by sensor glucose, with improvement in hypoglycemia, when compared to standard pump therapy.

Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability

BACKGROUND AND OBJECTIVE Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose. METHODS To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment. RESULTS The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change. CONCLUSIONS A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115

Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study.

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Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability

21 insulin recommendations, of which 103

Case-Based Reasoning for Insulin Bolus Advice: Evaluation of Case Parameters in a Six-Week Pilot Study

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Live demonstration: A handheld Bio-inspired Artificial pancreas for treatment of diabetes

28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

Live demonstration: An advanced bolus calculator for diabetes management - A clinical and patient platform

BACKGROUND The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks. METHODS Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia. RESULTS Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study. CONCLUSION The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.