Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the Continuous Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute one-hour glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalisable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies

The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust

Modeled Insulin Sensitivity and Interstitial Insulin Action from a Pilot Study of Dynamic Insulin Sensitivity Tests

An accurate test for insulin resistance can delay or prevent the development of Type 2 diabetes and its complications. The current gold standard test, CLAMP, is too labor intensive to be used in general practice. A recently developed dynamic insulin sensitivity test, DIST, uses a glucose-insulin-C-peptide model to calculate model-based insulin sensitivity, SI. Preliminary results show good correlation to CLAMP. However both CLAMP and DIST ignore saturation in insulin-mediated glucose removal. This study uses the data from 17 patients who underwent multiple DISTs to investigate interstitial insulin action and its influence on modeled insulin sensitivity. The critical parameters influencing interstitial insulin action are saturation in insulin receptor binding, αG, and plasma-interstitial difiusion rate, nI . Very low values of αG and very low values of nI produced the most intra-patient variability in SI. Repeatability in SI is enhanced with modeled insulin receptor saturation. Future parameter study on subjects with varying degree of insulin resistance may provide a better understanding of different contributing factors of insulin resistance

The Dynamic Insulin Sensitivity and Secretion Test (DISST) - a novel measure of insulin sensitivity

Objective: To validate the methodology for the Dynamic Insulin Sensitivity and Secretion Test (DISST) and to demonstrate its potential in clinical and research settings. Methods: 123 men and women had routine clinical and biochemical measurements, an oral glucose tolerance test and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration. Blood samples were drawn at t=0, 10, 15, 25 and 35 minutes for measurement of glucose, insulin and C-peptide. A 10g bolus of intravenous glucose at t=5 minutes and 1U of intravenous insulin immediately after the t=15 minute sample were given. Fifty participants also had a hyperinsulinaemic euglycaemic clamp. Relationships between DISST insulin sensitivity (SI) and the clamp, and both DISST SI and secretion and other metabolic variables were measured. Results: A Bland-Altman plot showed little bias in the comparison of DISST with the clamp; with DISST underestimating the glucose clamp by 0.1·10-2·mg·l·kg-1·min-1·pmol-1 (90%CI -0.2 to 0). The correlation between SI as measured by DISST and the clamp was 0.82, the c unit for the ROC analysis for the two tests was 0.96. Metabolic variables showed significant correlations with DISST IS, and the second phase of insulin release. DISST also appears able to distinguish different insulin secretion patterns in individuals with identical SI values. Conclusions: DISST is a simple, dynamic test that compares favourably with the clamp in assessing SI and allows simultaneous assessment of insulin secretion. DISST has the potential to provide even more information about the pathophysiology of diabetes than more complicated tests

Impact of sensor and measurement timing errors on model-based insulin sensitivity

peer reviewe

Insulin + nutrition control for tight critical care glycaemic regulation

A new insulin and nutrition control method for tight glycaemic control in critical care is presented from concept to clinical trials to clinical practice change. The primary results show that the method can provide very tight glycaemic control in critical care for a very critically ill cohort. More specifically, the final clinical practice change protocol provided 2100 hours of control with average blood glucose of 5.8 +/- 0.9 mmol/L for an initial 10 patient pilot study. It also used less insulin, while providing the same or greater nutritional input, as compared to retrospective hospital control for a relatively very critically ill cohort with high insulin resistance

Performance of stochastic targeted blood glucose control protocol by virtual trials in the Malaysian intensive care unit

Background and objective: Blood glucose variability is common in healthcare and it is not related or influ- enced by diabetes mellitus. To minimise the risk of high blood glucose in critically ill patients, Stochastic Targeted Blood Glucose Control Protocol is used in intensive care unit at hospitals worldwide. Thus, this study focuses on the performance of stochastic modelling protocol in comparison to the current blood glucose management protocols in the Malaysian intensive care unit. Also, this study is to assess the ef- fectiveness of Stochastic Targeted Blood Glucose Control Protocol when it is applied to a cohort of diabetic patients. Methods: Retrospective data from 210 patients were obtained from a general hospital in Malaysia from May 2014 until June 2015, where 123 patients were having comorbid diabetes mellitus. The comparison of blood glucose control protocol performance between both protocol simulations was conducted through blood glucose fitted with physiological modelling on top of virtual trial simulations, mean calculation of simulation error and several graphical comparisons using stochastic modelling. Results: Stochastic Targeted Blood Glucose Control Protocol reduces hyperglycaemia by 16% in diabetic and 9% in nondiabetic cohorts. The protocol helps to control blood glucose level in the targeted range of 4.0–10.0 mmol/L for 71.8% in diabetic and 82.7% in nondiabetic cohorts, besides minimising the treatment hour up to 71 h for 123 diabetic patients and 39 h for 87 nondiabetic patients. Conclusion: It is concluded that Stochastic Targeted Blood Glucose Control Protocol is good in reducing hyperglycaemia as compared to the current blood glucose management protocol in the Malaysian inten- sive care unit. Hence, the current Malaysian intensive care unit protocols need to be modified to enhance their performance, especially in the integration of insulin and nutrition intervention in decreasing the hyperglycaemia incidences. Improvement in Stochastic Targeted Blood Glucose Control Protocol in terms of u en model is also a must to adapt with the diabetic cohort

Integral-based identification of patient specific parameters for a minimal cardiac model

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and two disease states, Valvular Stenosis and Pulmonary Embolism, are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 minutes) to within a mean value of 4 – 8% in the presence of 5 – 15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 5% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection