ABSTRACT
The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system. Objective: This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics. Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT). Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method. Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors.
ABSTRACT
OBJECTIVES: There are approximately 660 000 end-stage renal disease patients in the USA, with hemodialysis (HD) the primary form of treatment. High ultrafiltration rates (UFRs) are associated with intradialytic hypotension, a complication associated with adverse clinical outcomes including mortality. Individualized UFR profiles could reduce the incidence of intradialytic hypotension. METHODS: The patient's fluid dynamics during HD is described by a nonlinear model comprising intravascular and interstitial pools, whose parameters are given by the patient's estimated nominal parameter values with uncertainty ranges; the output measurement is hematocrit. We design UFR profiles that minimize the maximal UFR needed to remove a prescribed volume of fluid within a set time, with hematocrit not exceeding a specified time-varying critical profile. RESULTS: We present a novel approach to design individualized UFR profiles, and give theoretical results guaranteeing that the system remains within a predefined physiologically plausible region and does not exceed a specified time-invariant critical hematocrit level for all parameters in the uncertainty ranges. We test the performance of our design using a real patient data example. The designed UFR maintains the system below a time-varying critical hematocrit profile in the example. CONCLUSION: Theoretical results and simulations show that our designed UFR profiles can remove the target amount of fluid in a given time period while keeping the hematocrit below a specified critical profile. SIGNIFICANCE: Individualization of UFR profiles is now feasible using current HD technology and may reduce the incidence of intradialytic hypotension.
