Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus in the presence of model uncertainty
21 Jul 2011-
TL;DR: In this article, the problem of the identification of single individual parameters in detailed dynamic models of glucose homeostasis is considered, and the optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation.
Abstract: Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose-insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas.
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TL;DR: The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas).
Abstract: The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.
461 citations
TL;DR: Control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error, and the parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events.
Abstract: One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual's response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia ( ;180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient-model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise.
161 citations
TL;DR: It is concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia.
Abstract: Continuous glucose monitoring (CGM) is an evolving technology poised to redefine current concepts of glycemic control and optimal diabetes management. To date, there are few randomized studies examining how to most effectively use this new tool. Therefore, a group of eight diabetes specialists heard presentations on continuous glucose sensor technology and then discussed their experience with CGM in order to identify fundamental considerations, objectives, and methods for applying this technology in clinical practice. The group concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia. The need for such progress is indicated by the growing prevalence of inadequately treated hyperglycemia. Coordinating financial and educational resources and developing clear protocols for using glucose sensor technology are urgent priorities in promoting wide adoption of CGM by patients and health care providers. Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices.
130 citations
TL;DR: Existing glucose-insulin dynamic models dating back from early 1960s are reviewed along with an overview of meal absorption and exercise effect models for BG regulation in type 1 diabetic patients.
Abstract: Blood glucose (BG) regulation in type 1 diabetic patients has been investigated by researchers for a long time. Many mathematical models mimicking the physiological behavior of diabetic patients have been developed to predict BG variations. Models characterizing meal absorption and physical activities have also been developed in the literature, as they play a significant role in altering BG levels. Hence, existing glucose-insulin dynamic models dating back from early 1960s are reviewed along with an overview of meal absorption and exercise effect models. The available knowledge-driven BG models have been classified into different families based on their origin for development. Also, five knowledge-driven BG models (with at least one model from a family) have been analyzed by either varying basal insulin or meal ingestion. The available meal absorption models have also been simulated to compare and analyze them for different meal sizes. The major objective of the analysis is to study the BG dynamics of dif...
69 citations
TL;DR: In this paper, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment.
Abstract: Model-based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model-based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010
48 citations
References
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TL;DR: Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique] are described.
Abstract: Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique) are described. Hyperglycemic clamp technique. The plasma glucose concentration is acutely raised to 125 mg/dl above basal levels by a priming infusion of glucose. The desired hyperglycemic plateau is subsequently maintained by adjustment of a variable glucose infusion, based on the negative feedback principle. Because the plasma glucose concentration is held constant, the glucose infusion rate is an index of glucose metabolism. Under these conditions of constant hyperglycemia, the plasma insulin response is biphasic with an early burst of insulin release during the first 6 min followed by a gradually progressive increase in plasma insulin concentration. Euglycemic insulin clamp technique. The plasma insulin concentration is acutely raised and maintained at approximately 100 muU/ml by a prime-continuous infusion of insulin. The plasma glucose concentration is held constant at basal levels by a variable glucose infusion using the negative feedback principle. Under these steady-state conditions of euglycemia, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue sensitivity to exogenous insulin.
7,271 citations
TL;DR: A computer oriented method which assists in the construction of response surface type experimental plans takes into account constraints met in practice that standard procedures do not consider explicitly.
Abstract: A computer oriented method which assists in the construction of response surface type experimental plans is described. It takes into account constraints met in practice that standard procedures do not consider explicitly. The method is a sequential one and each step covers the experimental region uniformly. Applications to well-known situations are given to demonstrate the reasonableness of the procedure. Application to a ‘messy” design situation is given to demonstrate its novelty.
2,667 citations
Book•
08 Mar 1993
TL;DR: Experimental designs in linear models Optimal designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of moment and information matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index as discussed by the authors
Abstract: Experimental Designs in Linear Models Optimal Designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of Moment and Information Matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index.
1,823 citations
TL;DR: The feasibility of the minimal model technique to determine the etiology of impaired glucose tolerance is demonstrated and it is demonstrated that subjects (regardless of weight) could be segregated into good and lower tolerance by the product of second-phase beta-cell responsivity and insulin sensitivity.
Abstract: The quantitative contributions of pancreatic responsiveness and insulin sensitivity to glucose tolerance were measured using the "minimal modeling technique" in 18 lean and obese subjects (88-206% ideal body wt). The individual contributions of insulin secretion and action were measured by interpreting the dynamics of plasma glucose and insulin during the intravenous glucose tolerance test in terms of two mathematical models. One, the insulin kinetics model, yields parameters of first-phase (phi 1) and second-phase (phi 2) responsivity of the beta-cells to glucose. The other glucose kinetics model yields the insulin sensitivity parameters, SI. Lean and obese subjects were subdivided into good (KG greater than 1.5) and lower (KG less than 1.5) glucose tolerance groups. The etiology of lower glucose tolerance was entirely different in lean and obese subjects. Lean, lower tolerance was related to pancreatic insufficiency (phi 2 77% lower than in good tolerance controls [P less than 0.03]), but insulin sensitivity was normal (P greater than 0.5). In contrast, obese lower tolerance was entirely due to insulin resistance (SI diminished 60% [P less than 0.01]); pancreatic responsiveness was not different from lean, good tolerance controls (phi 1: P greater than 0.06; phi 2: P greater than 0.40). Subjects (regardless of weight) could be segregated into good and lower tolerance by the product of second-phase beta-cell responsivity and insulin sensitivity (phi 2 . SI). Thus, these two factors were primarily responsible for overall determination of glucose tolerance. The effect of phi 1 was to modulate the KG value within those groups whose overall tolerance was determined by phi 2 . SI. This phi 1 modulating influence was more pronounced among insulin sensitive (phi 1 vs. KG, r = 0.79) than insulin resistant (obese, low tolerance; phi 1 vs. KG, r = 0.91) subjects. This study demonstrates the feasibility of the minimal model technique to determine the etiology of impaired glucose tolerance.
1,625 citations