850 Presentations

EFFECTS OF WHEAT BRAN DIET AND MAIZE BRAN DIET ON THE RANDOM BLOOD GLUCOSE AND WEIGHT OF ALLOXAN INDUCED DIABETIC RATS

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:43 - 09:44

Abstract

Background and Aims

The present study was conducted to evaluate the effect of wheat and maize brans feeds on the random blood glucose of the rats. These feeds were prepared using the the Purified Diet AIN-93-G with some modifications.

Methods

The experiment was laid out under completely randomized design. The rats (n=24) of almost uniform age and weight were divided into 4 groups each containing 6 rats. Group I comprised rats with normal blood glucose level reared on basal diet (AIN-93-G), Group II was Alloxan monohydrate (ALX monohydrate) induced diabetic rats reared on Purified Diet AIN-93G. Group III comprised Alloxan monohydrate induced diabetic rats fed a wheat bran diet and Group IV consisted of Alloxan monohydrate induced diabetic rats reared on a maize bran diet. The Random Blood Glucose (mg/dL) of these groups was monitored at the end of each week for a period of 6 weeks.

Results

Bran diets (maize and wheat) significantly lowered the Random Blood Glucose level in the experimental animals. However,the group of rats fed on maize bran diet had a significantly (p<0.05) more pronounced glucose lowering as compared to the group of rats fed on wheat bran diet.

Conclusions

The results indicated that both wheat and maize bran exert anti-diabetic effects on the ALX monohydrate induced diabetes and therefore, can be a part of diet based therapy for the management of diabetes.

Hide

REINFORCEMENT LEARNING BASED INSULIN BOLUS CALCULATOR: IN SILICO STUDY

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:44 - 09:45

Abstract

Background and Aims

Despite the development of artificial pancreas, many diabetics are using insulin injection devices. And about 80% of insulin delivery products are bolus injection devices and the rate of used as home care is higher than the rate used in hospitals and clinics. Therefore, insulin bolus calculation algorithm for insulin injection devices will be helpful for many diabetics. In this study, we propose a reinforcement learning based bolus calculation algorithm.

Methods

We determine the injection timing of insulin bolus through the PK/PD (pharmacokinetics/pharmacodynamics) curve of OGTT (Oral Glucose Tolerance Test) and the GCT (Glucose Clamp Test). And injection amount of insulin bolus is determined before each three meals using DQN (Deep Q-Networks) reinforcement learning algorithm. Basal insulin is injected with an optimal amount. Also, to learn the method to prevent hypoglycemia more effectively, snacks are not allowed before bedtime and higher penalty is given in hypoglycemia. The proposed method is evaluated on one adult from the US-FDA approved UVa/Padova simulator under a multi-meal scenario.

comparison.png

Results

The insulin bolus calculation algorithm achieves a mean glucose of 114.54 mg/dl and a time in range of 89.30%. Insulin is injected 25 minutes before each meal and the algorithm showed a tendency to change the amount of insulin injection according to the amount of meal.

Conclusions

The reinforcement learning based insulin bolus calculation algorithm is effective in determining the amount of insulin bolus according to the amount of food and the personalized insulin injection timing according to PK/PD characteristics.

Hide

MODEL CONFORMANCE IN BLOOD GLUCOSE PREDICTION TASKS

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:45 - 09:46

Abstract

Background and Aims

In recent years, neural network models (NNMs) have gained popularity for use in predicting blood glucose (BG) values. These models have the advantage of being data-driven and trained to model an individual's unique physiological characteristics. However, NNMs remain opaque and difficult to understand and interpret by human users. In this work, we address a key challenge in designing and training NNMs for BG prediction: ensuring NNMs conform to known physiological dynamics. We demonstrate how the standard learning protocol results in non-conformant models, and present a physiologically-motivated learning approach to ensure conformance is obtained without sacrificing accuracy.

Methods

Using a combination of mixed integer programming and local gradient search, maximum and minimum changes in predicted BG corresponding to increased insulin infusions are tested in three classes of NNMs: a generalized NNM structure, and two novel physiologically motivated structures. Model sensitivity of each input is computed and tested for conformance to "increased insulin results in decreased BG values". Models are trained using previously collected CGM and insulin pump data from a cohort of twenty-four subjects with T1D to predict 60min horizons.

Results

Accuracy, computed by root mean square error, remains consistent across all models [Table 1]. However, only the constrained, physiologically motivated model fully conforms to "increased insulin decreases BG" [Fig. 1].

attd2020_table1.pngattd2020_fig1.png

Conclusions

A novel test for conformance of NNMs to known physiology is presented. We demonstrate how standard NNMs learned for BG prediction can fail to conform, and present a physiologically motivated design to learning which improves conformance while maintaining accuracy.

Hide

TRANSLATING STANDARD CLINICAL PROTOCOL INTO TUNING PROCEDURES FOR HYBRID CLOSED LOOP SYSTEMS: 670G, CONTROL-IQ AND LOOP

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:46 - 09:47

Abstract

Background and Aims

The transition of new hybrid closed loop (HCL) artificial pancreas systems from research into widespread clinical use brings exciting advances to diabetes care. However, tuning protocols for current and in-trial systems vary significantly. This work seeks to establish a comprehensive guide for clinicians for translating clinical practice into tuning procedures for patients on the Medtronic 670G, Tandem Control-IQ, and Do-It Yourself (DIY) Loop systems.

Methods

Retroactive patient data, in-silico HCL implementation, algorithmic analysis and clinical experience is utilized to correlate features in blood glucose profiles with adjustments in HCL parameters leading to improved control. Parameters and tuning protocols for the Medtronic 670G, Control-IQ, and DIY Loop systems are compared against standard clinical guidelines for setting basal rates, insulin sensitivity, correction factors, carb ratio, and additional control set points when applicable.

Results

Tuning procedures for each parameter setting across devices, with detailed descriptions and simplified tables will be presented. Figures demonstrating effects of parameter changes on insulin dosing for each system, along with cases for when to adjust a certain parameter will be shown. In all we find 2 adjustable parameters in 670G, 5 in Control IQ and 10 in Loop. Fig. 1 depicts an example table for one parameter: "Target Range".

hclcompare.png

Conclusions

As additional HCL systems enter the clinical market and popularity of DIY systems grows, it is vital for clinicians to understand differences and similarities between systems. This work helps translate standard clinical adjustments into sequences of parameter tuning for each HCL system in order to provide a comprehensive guide for clinicians.

Hide

COMPARING DIY FULL CLOSED-LOOP PERFORMANCE IN PIGS WITH STREPTOZOCIN-INDUCED DIABETES

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:47 - 09:48

Abstract

Background and Aims

Do-It-Yourself (DIY) algorithms for closed-loop insulin delivery are increasingly popular but infrequently studied in humans, outside of observational studies, due to lack of regulatory approval. We therefore conducted studies in pigs comparing AndroidAPS and Loop without meal announcement, leveraging faster insulin pharmacokinetics inherent to swine.

Methods

Pigs with streptozocin-induced diabetes were started on AndroidAPS running oref1 (with super-microbolus enabled) and Loop (with integral retrospective correction enabled). Insulin dosing including basal rate, insulin-to-carbohydrate ratio (ICR) and insulin sensitivity factor (ISF) were determined clinically prior to closed-loop initiation. Insulin pharmacokinetics were derived by ELISA and observation of glucose dynamics. Basal rate testing was conducted overnight without insulin-on-board (IOB) or carbs-on-board (COB) and rates were titrated to maintain glucose. ISF was calculated by administering 1 unit insulin under hyperglycemic conditions with no IOB or COB. ICR was calculated and then titrated such that post-meal blood sugar matched pre-meal.

Results

6 pigs were started on AndroidAPS followed by Loop. Insulin pharmacokinetics are more rapid in pigs with peak serum concentrations within 20-25 minutes and near complete absorption by 2 hours, modeled in both systems. In total, there were 23 days of AndroidAPS and 18 days of Loop data. Time-in-Range (70-180mg/dL) was significantly greater (p < 0.001) with AndroidAPS (63.7 ± 13.4%) versus Loop (40.5 ± 17.2%).

Conclusions

For unannounced meals, Time-in-Range was greater with AndroidAPS than with Loop. oref1 with super-microbolus is designed for unannounced meals, whereas Loop is a model predictive controller with short-term adaptation more dependent on meal data.

Hide

REINFORCEMENT LEARNING BASED AUTOMATED INSULIN INFUSION: IN SILICO FEASIBILITY STUDY

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:48 - 09:49

Abstract

Background and Aims

For an advanced artificial pancreas, the automation of insulin treatment is of great interest in clinical and research aspects. Despite recent algorithmic advances, there are fundamental challenges to overcome, such as uncertainties from unannounced meals and delays from "insulin stacking." In this study, we propose a reinforcement based automated insulin infusion algorithm.

Methods

We devise a bio-inspired reinforcement learning approach where a reward function mimics the temporal homeostatic objective of β-cells and a discount rate reflects individual specific pharmacokinetics/pharmacodynamics (PK/PD) characteristics. The proposed method is evaluated on 10 adults from the US-FDA approved UVa/Padova simulator under a single-meal with preprandial fasting scenario.

Results

The trained algorithm achieves a mean glucose of 117.21 mg/dl. At the beginning of the fasting phase, the average patient's sensor glucose is 121.61 mg/dl, and it is stabilized to basal state with 100.41 mg/dl under the automated regulation. For the postprandial phase without any meal announcement, the trained algorithm automatically regulates the postprandial glucose with a sudden peak of the insulin infusion rate which is followed by a gradually decreasing rate. About three hours after meal-intakes, the infusion rate becomes the lowest and results the patients to avoid hypoglycemia.

singlemeal2.pngsinglemealtable.png

Conclusions

The proposed insulin infusion algorithm based on the bio-inspired reinforcement learning approach allows for fully automated glucose control with hypoglycemic avoidance.

Acknowledgments: This work was supported by the MSIT, Korea, under the ICT Consilience Creative program (IITP-2019-2011-1-00783), the Basic Science Research Program through the NRF funded by the MSIT (NRF-2017R1A5A1015596), and the POSCO Green Science Project funded by POSCO, Korea.

Hide

INDIVIDUAL DAILY CARBOHYDRATE INTAKE IS INVERSELY ASSOCIATED WITH GLYCAEMIC CONTROL IN ADULTS WITH TYPE 1 DIABETES USING A HYBRID CLOSED-LOOP SYSTEM

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:49 - 09:50
Presenter

Abstract

Background and Aims

With the clinical implementation of hybrid closed-loop (HCL) systems, efforts are moving towards personalised medicine. However, sparse evidence exists on how individual carbohydrate (CHO) intake affects glycaemic control in type 1 diabetes (T1D). The HCL-device MiniMed 670G requires CHO-input for meal-time bolus calculation while in “auto-mode”. We aimed at assessing glycaemic control as a function of individual daily CHO-intake.

Methods

Between 11/2018 and 06/2019, we evaluated CHO-intake (g/day) and CGM data in adults with T1D using the MiniMed 670G system at our tertiary referral centre. Mean individual daily CHO-intake (MIDC) was assessed for each participant. For each day, the relative deviation from MIDC (rMIDC) was calculated, and days were stratified into low, medium and high CHO-days (80%, 81–120% and >120% rMIDC, respectively). CGM read-outs were used to calculate time in target range (TIR, 3.9-10.0 mmol/l), time above target range, and time below target range.

Results

We included 21 patients with T1D (11 male, 10 female; age 39.2±14.7y; HbA1c 7.0±0.9%) providing a total of 879 days of data (mean 42.0±39.2d per patient, 9-186d). Mean individual time in auto-mode was 97.5±4.7%. Time in target range (TIR) for the low, medium and high CHO-days was 82.1±13.9%, 79.2±14.9% and 75.8±14.6%, respectively (p<0.001). Time above target range was 15.8±13.1%, 18.8±14.3% and 22.6±14.7%, respectively (p<0.001). There was no significant difference for time below target range.

Conclusions

Individual daily CHO-intake was inversely associated with glycaemic control in adults with T1D using the MiniMed 670G HCL-system, corroborating the importance of personalised treatment recommendations.

Hide

COMPARISON OF TWO PREGNANCIES OF A WOMAN WITH TYPE 1 DIABETES WITHOUT AND WITH AAPS (ANDROID ARTIFICIAL PANCREAS SYSTEM) SUPPORT

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:50 - 09:51

Abstract

Background and Aims

The tight metabolism targets of women with type 1 diabetes (T1D) during pregnancy are associated with a high frequency of hypoglycaemia which impose a significant burden on these women’s lives. Currently no commercial artificial pancreas system (APS), that could mitigate this burden, is approved during pregnancy.

Methods

A woman with 30 years of T1D underwent two subsequent pregnancies: Pregnancy one at age 35 using multiple daily injections (MDI) therapy (insulin glargine, Sanofi and insulin lispro, Eli-Lilly) with flash glucose monitoring (FGM; Abbott Libre Flash). Pregnancy 2 at age 37 using OpenAPS software algorithm in combination with established hardware (AC Combo, Roche Diabetes Care and Dexcom G5, Dexcom) using insulin (lispro, ELI-Lilly). Data on HbA1c, the woman’s hypoglycaemia perception, maternal weight development during the pregnancy, insulin doses (before, during and after pregnancy), childbirths, complications and birth weight of the children were collected from medical records, mother-child-passes and Nightscout downloads. Data were retrospectively analysed.

Results

No differences in above 6 of the 7 mentioned parameters were found. However, the first pregnancy with MDI and FGM showed severe hypoglycaemias, while in the second one (AAPS) no severe hypoglycaemia or hypoglycaemia unawareness occurred.

Conclusions

AAPS in pregnant women with T1D can improve metabolic control at reduced risk of hypoglycaemia leading to substantially improved quality of life. Pregnant women with T1D are not willing to wait for commercially available closed loop systems.

Hide

HYBRID CLOSED LOOP AND ALGORITHMS IMPROVE METABOLIC CONTROL IN TYPE 1 DIABETIC PATIENTS.

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:51 - 09:52

Abstract

Background and Aims

Predictive low-glucose suspend (PLGS) insulin delivery system and hybrid closed loop (HCL) systems may improve glucose control in type 1 diabetic individuals. This is a single-center, retrospective, observational study to evaluate the effect on metabolic control and glucose variability of PLGS and HCL systems compared to sensor-augmented pump (SAP) therapy.

Methods

We retrospectively analyzed seventy-nine adults with type 1 diabetes on insulin pump therapy, matched for age, gender and BMI. The mean follow-up was 3.5±1.9 years.

Results

Forty patients (mean age 51,2±16,1 years, F/M 19/21), who are in treatment with HCL system (Minimed 670G, Medtronic, Northridge, CA) or with PLGS feature (Minimed 640G, Medtronic, Northridge, CA) (Group 1), were compared to 39 subjects in SAP therapy (mean age 47,8±10,5 years, F/M 15/24) (Group 2). Group 1, compared to Group 2, showed lower HbA1c levels (7.2±0.8% vs 7.6±0.9%, p=0.07), and a statistically significant higher percentage of time that interstitial glucose level was within the target range, defined as 70 to 180 mg per deciliter (65.3±12.4% versus 56.1±16.4%, p=0.03). Moreover, Group 1 showed a significant lower time spent in hypoglycemic range compared to Group 2 (2.0±1.7% versus 6.7±5.6%, p=0.001).

Conclusions

PLGS and HCL systems were more effective in improving glucose control and in reducing the risk of hypoglycaemia in patients with type 1 diabetes, thereby mitigating risk for acute and chronic complications.

Hide

ACCURACY ANALYSIS OF AN AUTONOMOUS SYSTEM TO PERSONALIZE BLOOD GLUCOSE PREDICTION FOR T1DM PATIENTS WITH REAL WORLD DATA

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:52 - 09:53

Abstract

Background and Aims

BGL predictive algorithms can improve T1DM treatment preventing glucose excursions. An autonomous system based on neural networks was developed to personalize blood glucose predictions based on blood glucose, insulin infusion, nutrient intakes and heart rates in a real world scenario.

Methods

20 T1DM patients were monitored with flash glucose monitor, activity tracker and mobile app (GlucoTrends) to collect meal and insulin data. Personalized prediction models were trained independently, without requiring specific settings for each individual. Patient's characteristics: 9 female, age: 32.4(SD:10.5), BMI: 26.0(3.8), BGL: 159.1(34.0), under the following therapies: 45% under fixed doses, 40% carbohydrate-count and 15% insulin pumps. Patients were monitored during on average 29.3 days (SD: 7.9), and the last 20% measurements were reserved for evaluation. Prediction accuracy for 1-hour prediction horizon was evaluated by Clarke Error Grid (CEG) compareding to BGL measured with flash monitor.

Results

The percentage of predictions within AB zones of CEG for all-day and only for night-time periods are 92.9% and 94.0%, respectively (Figure 1). Patients using Insulin Pumps, Analog insulin and Human insulin had 97.5%(0.7), 93.6%(4.5) and 90.0%(8.2) predictions within AB zone on average, respectively.

allday.png

night.png

attd2020-boxplot-clarkezones-therapy.png

Conclusions

Our proposed algorithm has demonstrated to be capable to personalize glucose prediction in a real world scenario and poses as a potential solution for an autonomous support system. In our study different insulin regimen and molecules confirmed expected results. Higher than desired predictions in zone D may be caused by sensor's error biases within minimum glucose levels.

Hide

TOWARD A PERSONALIZED DECISION SUPPORT SYSTEM FOR BLOOD GLUCOSE MANAGEMENT DURING AND AFTER PHYSICAL ACTIVITIES IN PATIENTS WITH TYPE 1 DIABETES

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:53 - 09:54

Abstract

Background and Aims

Physical activities have a significant impact on blood glucose homeostasis of patients with type 1 diabetes. The risk of hypoglycemia (low blood glucose) is significantly higher during and after physical activities, especially for individuals who experience hypoglycemia unawareness. Our research aims to reduce the risk of hypoglycemia and empower type 1 diabetes patients in making decisions regarding food choices, insulin, exercise intensities and other factors in connection with physical activities.

Methods

Using historical physical activity data, a feedforward neural network is trained to provide a prediction of the blood glucose outcome during and after physical activities. Based on the personalized neural network and Q learning (a model free reinforcement learning), optimal actions that minimize the risk of hypoglycemia and improve blood glucose regulation are provided to the patient.

Results

In-silico results on a blood glucose simulator with the Breton’s physical activity model show that the proposed methodology is capable of maintaining the blood glucose in the healthy range during and after physical activities. No hypoglycemia has occured when following the recommendation of the decision support system.

Conclusions

The research shows the potential of using machine learning in reducing the risk of hypoglycemia and better glycemic control during and after physical activities. Important research are identified and conducted to reduce the roller coaster effect and provide better combination of food intake, insulin and other factors for patients in their daily management of type 1 diabetes.

Hide

BLOOD GLUCOSE PREDICTION WITH A FRACTIONAL ORDER NEURAL NETWORK

Session Name
CLOSED-LOOP SYSTEM AND ALGORITHM
Session Type
E-POSTER VIEWING (EXHIBITION HOURS)
Date
20.02.2020, Thursday
Session Time
09:30 - 15:30
Channel
E-Poster Area
Lecture Time
09:54 - 09:55

Abstract

Background and Aims

Blood Glucose (BG) prediction models need to be improved to ameliorate BG regulation. Neural Networks (NN) and fractional order calculus are powerful tools for black box modeling. This work combines both approaches to propose the first NN model with fractional order learning algorithm to improve BG prediction.

Methods

The NN model (Fig. 1A), which uses BG, Insulin on board, and carbohydrates on board as inputs, consists of a three-layer NN with 2-2-1 neurons in the input, hidden and output layers, respectively. The NN was trained by a learning algorithm using the Grünwald-Letnikov fractional derivative (2).

Evaluation is performed on 10 T1D patients (aged > 18 years). Training is performed using 10 days of data and test is performed using 5 days of data unseen by the NN during training. RMSE is computed to evaluate accuracy on BG prediction 30-, and 60-min-ahead.

Results

Table 1 displays results reached by the proposed model and results reported in the literature. Fig. 1B shows RMSE reached by the proposed NN-based model on the 10 validation subsets (10 patients, 5 days). As expected, RMSE ± std increases when prediction horizon increases.

fttd2020.png

Conclusions

This work presented a NN-based BG prediction model trained by a fractional order learning algorithm. The proposed model reached the best performance on predictions 30- and 60-min-ahead, compared with results reported in the literature. Furthemore, the proposed model is the simplest of the compared approaches. In future works, the possibility to predict hypo- and hyperglycemia events by the proposed model will be evaluated.

ref_carlos.png

Hide