Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care

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

Development of a model-based clinical sepsis biomarker for critically ill patients

Invited. online 15 May 2010.Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (SI) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity SI values were calculated from glycemic control data of 36 patients with sepsis. The hourly SI is compared to the hourly sepsis score (ss) for these patients (ss = 0–4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss=2) are created for both SI and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an SI cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining SI, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, themultivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value

A model-based control protocol for transition from ICU to HDU: Robustness analysis

“© © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”The robustness of a model-based control protocol as a less intensive TGC protocol using insulin Glargine for provision of basal insulin is simulated in this study. To quantify the performance and robustness of the protocol to errors, namely physiological variability and sensor errors, an in-silico Monte Carlo analysis is performed. Actual patient data from Christchurch Hospital, New Zealand were used as virtual trial patients

Monte Carlo Analysis Of A Glycaemic Control Protocol For Less Acute Wards

Tight glycaemic control (TGC) benefits medical and surgical intensive care unit (ICU) patients by reducing complications associated with hyperglycemia. However, when patients transfer to less acute wards, continuing the same level of TGC is difficult and they get “rebound hyperglycemia” and may return to ICU. Primarily due to a lack of nursing resources. The SPRINT+Glargine protocol was developed to support the transition of patients from ICU to less acute wards. Glargine is injected 1-2x/day, so it can potentially reduce the workload to match clinical resources

Monte Carlo Analysis of a Subcutaneous Absorption Insulin Glargine Model: Variability in Plasma Insulin Concentrations

4-pagesAbsorption kinetics of long acting insulin such as Glargine often shows significant intra and inter-individual variability. To add this variability to the pharmacokinetics model of Glargine, ranges of variation for Glargine model parameters were introduced into 1000 Monte Carlo simulations. This assessment and analysis portray the likely intra-individual and inter-individual variability that could be expected clinically. The Monte Carlo analysis thus defines a range and distribution of identified and validated model parameter variations to consider in designing a glycaemic control protocol using Glargine

Intensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patients

A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition- Glucose (ICING) Model is presented and validated using data from critically ill patients. Glucose utilisation and its endogenous production in particular, are more distinctly expressed. A more robust glucose absorption model through ingestion is also added. Finally, this model also includes explicit pathways of insulin kinetics, clearance and utilisation. Identification of critical constant population parameters is carried out parametrically, optimising one hour forward prediction errors, while avoiding model identifiability issues. The identified population values are pG = 0.006 min-1, EGPb = 1.16 mmol/min and nI = 0.003 min-1, all of which are within reported physiological ranges. Insulin sensitivity, SI , is identified hourly for each individual. All other model parameters are kept at well-known population values or functions of body weight or surface area. A sensitivity study confirms the validity of limiting time-varying parameters to SI only. The model achieves median fitting error <1% in data from 173 patients (N = 42,941 hrs in total) who received insulin while in the Intensive Care Unit (ICU) and stayed for more than 72 hrs. Most importantly, the median per patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is now within the lower bound of typical glucometer measurement errors of 7-12%. This result further confirms that the model is suitable for developing model-based insulin therapies, and capable of delivering tight blood glucose control, in a real-time model based control framework with a tight prediction error range

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

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care units (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identi ability issues are carefully considered for clinical settings. This article also contains signi cant reference to relevant physiology and clinical literature, as well as some references to the modeling e orts in this eld. Identi cation of critical constant population parameters were performed Preprint submitted to Computer Methods and Programs in BiomedicineSeptember 16, 2010 in two stages, thus addressing model identi ability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI , the only dynamic, time-varying parameter, is identi ed hourly for each individual. All population parameters are justi ed physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study con rms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median tting error of <1% over data from 173 patients (N = 42,941 hrs in total) who received insulin while in the ICU and stayed for 72 hrs. Most importantly, the median per-patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is signi cant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7{12%. These results con rm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care

Statistical Optimization and Kinetic Modeling of Lipase-Catalyzed Synthesis of Diacylglycerol in the Mixed Solvent System of Acetone/ tert-Butanol

Diacylglycerols have been extensively used in food, medicine, cosmetic, and pharmaceutical industries due to their outstanding properties as emulsifiers and stabilizers as well as their nutritional benefits to humans. This study investigated the optimization and kinetics of lipase-catalyzed synthesis of glycerol dioleates (GDO) through esterification of glycerol with oleic acid in a mixed solvent system of acetone/tert-butanol. Process variables, namely, solvent ratio, temperature, and enzyme loading, were optimized to acquire maximum conversion and selectivity. A high oleic acid conversion of 73% and GDO selectivity of 77% were obtained in an acetone/tert-butanol 70:30 (v/v) mixture at 45 °C and an enzyme load of 0.15 g. The bi substrates Ping-Pong Bi-Bi kinetic model was employed for initial rate determination, which was fitted to the experimental results. At an applied temperature of 45 °C, the Ping-Pong Bi-Bi model with no substrate inhibition gives the best fit with parameter values of Vmax = 1.21 M h–1, Km[G] = 1.81 M, and Km[OA] = 6.81 M for glycerol and oleic acid concentrations between 0.31 to 1.32 M and 1.88 to 2.13 M, respectively