Chiara Zecchin

Neural Network Incorporating Meal Information Improves Accuracy of Short-Time Prediction of Glucose Concentration

Diabetes mellitus is one of the most common chronic diseases, and a clinically important task in its management is the prevention of hypo/hyperglycemic events. This can be achieved by exploiting continuous glucose monitoring (CGM) devices and suitable short-term prediction algorithms able to infer future glycemia in real time. In the literature, several methods for short-time glucose prediction have been proposed, most of which do not exploit information on meals, and use past CGM readings only. In this paper, we propose an algorithm for short-time glucose prediction using past CGM sensor readings and information on carbohydrate intake. The predictor combines a neural network (NN) model and a first-order polynomial extrapolation algorithm, used in parallel to describe, respectively, the nonlinear and the linear components of glucose dynamics. Information on the glucose rate of appearance after a meal is described by a previously published physiological model. The method is assessed on 20 simulated datasets and on 9 real Abbott FreeStyle Navigator datasets, and its performance is successfully compared with that of a recently proposed NN glucose predictor. Results suggest that exploiting meal information improves the accuracy of short-time glucose prediction.

Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study.

BACKGROUND Hypoglycemia prevention is one of the major challenges in diabetes research. Recently, it has been suggested that continuous glucose monitoring (CGM)-based short-term glucose prediction algorithms could be exploited to generate alerts when hypoglycemia is forecasted, allowing the patient to take appropriate countermeasures to avoid/mitigate the event. However, quantifying the potential benefits of prediction in terms of reduction of number/duration of hypoglycemia requires an in silico assessment that is the object of the present article. MATERIALS AND METHODS Data for 50 virtual subjects were generated by using the University of Virginia/Padova type 1 diabetes simulator (54-h monitoring), made more credible by adding realistic measurement noise and perturbations of meals and insulin injections. CGM was assumed to be well calibrated. Occurrence and duration of hypoglycemic events were compared in three scenarios: (1) hypoglycemia was not recognized and not dealt with; (2) 15 g of carbohydrates was ingested when CGM crossed the hypoglycemia threshold; or (3) 15 g of carbohydrates was ingested when the 30-min ahead-of-time CGM prediction crossed the hypoglycemia threshold. The effectiveness of alerts was investigated also in the case of delayed/absent ingestion of carbohydrates. RESULTS In Scenario 1, each virtual subject spent 17.7% of the time in the hypoglycemic range, with a median of four events of 120 min in the 54-h period monitored. In Scenario 2, the time spent in hypoglycemia was reduced to 4.7% (four events of 40 min). In Scenario 3, the time spent in hypoglycemia was further reduced to 1.2% (one event of 15 min). Absent/delayed patients responses to alerts slightly increase these percentages, but improvements remain significant. CONCLUSIONS This in silico proof-of-concept study demonstrates that using predicted rather than measured CGM allows a significant reduction of the number of hypoglycemic events and the time spent in hypoglycemic range both by 75%, stimulating further research and clinical investigation on the generation of preventive hypoglycemic alerts exploiting glucose prediction methods.

Dexcom G4AP: An Advanced Continuous Glucose Monitor for the Artificial Pancreas

Input from continuous glucose monitors (CGMs) is a critical component of artificial pancreas (AP) systems, but CGM performance issues continue to limit progress in AP research. While G4 PLATINUM has been integrated into AP systems around the world and used in many successful AP controller feasibility studies, this system was designed to address the needs of ambulatory CGM users as an adjunctive use system. Dexcom and the University of Padova have developed an advanced CGM, called G4AP, to specifically address the heightened performance requirements for future AP studies. The G4AP employs the same sensor and transmitter as the G4 PLATINUM but contains updated denoising and calibration algorithms for improved accuracy and reliability. These algorithms were applied to raw data from an existing G4 PLATINUM clinical study using a simulated prospective procedure. The results show that mean absolute relative difference (MARD) compared with venous plasma glucose was improved from 13.2% with the G4 PLATINUM to 11.7% with the G4AP. Accuracy improvements were seen over all days of sensor wear and across the plasma glucose range (40–400 mg/dl). The greatest improvements occurred in the low glucose range (40–80 mg/dl), in euglycemia (80–120 mg/dl), and on the first day of sensor use. The percentage of sensors with a MARD <15% increased from 69% to 80%. Metrics proposed by the AP research community for addressing specific AP requirements were also computed. The G4AP consistently exhibited improved sensor performance compared with the G4 PLATINUM. These improvements are expected to enable further advances in AP research.

A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information

In the last decade, improvements in diabetes daily management have become possible thanks to the development of minimally-invasive portable sensors which allow continuous glucose monitoring (CGM) for several days. In particular, hypo and hyperglycemia can be promptly detected when glucose exceeds the normal range thresholds, and even avoided through the use of on-line glucose prediction algorithms. Several algorithms with prediction horizon (PH) of 15–30–45 min have been proposed in the literature, e.g. including AR/ARMA time-series modeling and neural networks. Most of them are fed by CGM signals only. The purpose of this work is to develop a new short-term glucose prediction algorithm based on a neural network that, in addition to past CGM readings, also exploits information on carbohydrates intakes quantitatively described through a physiological model. Results on simulated data quantitatively show that the new method outperforms other published algorithms. Qualitative preliminary results on a real diabetic subject confirm the potentialities of the new approach.

Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.

BACKGROUND In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. SUBJECTS AND METHODS In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. RESULTS Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. CONCLUSIONS Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Background: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. Methods: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. Results: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. Conclusions: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

Jump Neural Network for Real-Time Prediction of Glucose Concentration

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

“Algorithmically Smart” Continuous Glucose Sensor Concept for Diabetes Monitoring

In the last 15 years, subcutaneous continuous glucose monitoring (CGM) sensors, portable devices able to measure glycemia almost continuously (1-5 min sampling period) for several days (up to 7), opened new frontiers in the treatment of diabetes, a chronic disease affecting about more than 300 million of people worldwide. However, glucose readings provided by current CGM devices still suffer of accuracy and precision problems. The present work illustrates technical details of algorithms and implementation issues of a new conceptual architecture to deal with those problems and render any commercial CGM sensor algorithmically smarter. In particular, three modules for denoising, enhancement and prediction are assessed on a dataset collected in 24 type 1 diabetic patients during the EU project AP@home.