Experimental blood glucose interval identification of patients with type 1 diabetes

Background: Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. Method:Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure.Results: Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. Conclusions:The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

Section: Original Articlementioning

confidence: 99%

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

Sanz

Doyle

Dassau

2016

“…Recently, new identification techniques have been investigated [23][24][25][26][27][28][29][30][31][32][33] ; for a comprehensive literature review, we refer the reader to Zarkogianni et al 34 and Oviedo et al 35 Promising results have been obtained on this topic by our group. [36][37][38][39][40][41] The main goal of this paper is to use the algorithm described in Wang et al 32 and Soru et al 36 to identify individualized models from free-living data for seven patients studied at the University of Amsterdam Medical Centre (AMC) and to validate individualized hypoglycemia predictive alerts (IHPAs) on a rather long period (one month) 9 for all these patients.…”

Section: Introductionmentioning

confidence: 99%

Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions

Toffanin

Aiello

Cobelli

et al. 2019

Background: The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial. Methods: A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs. Results: The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson’s correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results. Conclusion: The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.

“…20 Model identification of control-oriented models in diabetes technology has been typically carried out in an informal way, giving most priority to insulin sensitivity (the gain of insulin action onto glucose uptake) for model individualization without assessing the role and impact of this and other parameters on the system output (measurements). [14][15][16][17] , [21][22][23][24][25][26][27][28][29][30][31] For example, in Patek et al 14 the insulin sensitivity is the only parameter used for model individualization of SOGMM with the remaining parameters fixed at population values. In Lin et al, 15 the authors used an alternative representation of the minimal model for glucose control in patients at the intensive care unit (ICU).…”

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confidence: 99%

“…Alternative methods have also been presented. 29,30 In Herrero et al, 29 the minimal model is reparametrized to render globally structural identifiable. The parameter identification is performed through the set inversion via interval analysis (SIVIA) assuming a set of acceptable errors (intervals) from standard intravenous glucose tolerance test (IVGTT) data.…”

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confidence: 99%

“…The authors claimed the use of a computationally efficient implementation to overcome the extensive computational complexity derived from the method. In Laguna et al, 30 the authors used the Cambridge model 12 (Hovorka’s model) together with interval analysis, tackling the model uncertainties with interval models. Unlike the above methods, interval analysis provides an envelope of glucose covering all the possible glucose trajectories given an allowed parameter variability.…”

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confidence: 99%

See 1 more Smart Citation

Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems

García-Tirado

Zuluaga-Bedoya

Breton

2018

This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.