bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blood Glucose Measurements in Arterial Blood of Intensive Care Unit Patients Submitted to Tight Glycemic Control: Agreement between Bedside Tests
Authors: Van Vlasselaers Dirk , Herpe Tom , Milants Ilse , Eerdekens Mona , Wouters Pieter J. , De Moor Bart , Van den Berghe Greet 
Published: January 2014

Background: Implementing tight glycemic control (TGC) in intensive care unit (ICU) patients requires accurate blood glucose (BG) monitoring. We evaluated the performance of two commercially available bedside glucometers, Accu-Chek® and HemoCue®, in patients admitted to the ICU and in whom TGC was applied. Methods: Thirty-seven adult ICU patients were prospectively included. During 48 hours, BG was determined simultaneously on the same arterial blood sample using the two point-of-care testing (POCT) glucometers as compared with the standard technique. Data of 452 paired measurements were analyzed using linear regression, Clark error grid analysis (EGA), the method of Bland-Altman, and the GLYCENSIT procedure. Results: Both tested glucometers showed satisfactory results when evaluated with linear regression and EGA. Correlation coefficients were above 0.9, and 100% of all the glucose readings were within the safe zones A and B using EGA. However, when applying more appropriate tests, both sensors failed to provide sufficient accuracy in the setting of TGC in ICU patients. The Hemocue revealed a bias of >10 mg/dl with a trend to systematically overestimate the actual BG value. The bias for the Accu-Chek was 6 mg/dl with wide limits of agreement and a variable over- and underestimation of the actual BG value depending on the level of BG (hypo-, normo-, or hyperglycemia). Conclusions: When TGC is implemented in ICU practice, caution is warranted when adjusting insulin rates based only on BG readings obtained by the tested glucometers. ICU practitioners should weigh the advantages and disadvantages of such devices: a greater bias but with a more predictable error and measurement behavior versus a somewhat lower bias but with an unpredictable direction of the difference.
Statistical Approach of Assessing the Reliability of Glucose Sensors: The GLYCENSIT Procedure
Authors: Van Herpe Tom , Pelckmans Kristiaan , De Brabanter Jos , Janssens Frizo , De Moor Bart , Van den Berghe Greet 
Published: January 2014

Background: In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability. Methods: The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay® system in the ICU is analyzed with GLYCENSIT. Results: The GLYCENSIT analysis comprises three phases: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU. Conclusions: The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.
Evaluation of Implementation of a Fully Automated Algorithm (Enhanced Model Predictive Control) in an Interacting Infusion Pump System for Establishment of Tight Glycemic Control in Medical Intensive Care Unit Patients
Authors: Kulnik Roman , Plank Johannes , Pachler Christoph , Wilinska Malgorzata E. , Groselj-Strele Andrea , Röthlein Doris , Wufka Matthias , Kachel Norman , Smolle Karl Heinz , Perl Sabine , Pieber Thomas Rudolf , Hovorka Roman , Ellmerer Martin 
Published: January 2014

Background: The objective of this study was to investigate the performance of a newly developed decision support system for the establishment of tight glycemic control in medical intensive care unit (ICU) patients for a period of 72 hours. Methods: This was a single-center, open, non-controlled feasibility trial including 10 mechanically ventilated ICU patients. The CS-1 decision support system (interacting infusion pumps with integrated enhanced model predictive control algorithm and user interface) was used to adjust the infusion rate of administered insulin to normalize blood glucose. Efficacy and safety were assessed by calculating the percentage of values within the target range (80–110 mg/dl), hyperglycemic index, mean glucose, and hypoglycemic episodes (<40 mg/dl). Results: The percentage of values in time in target was 47.0% (±13.0). The average blood glucose concentration and hyperglycemic index were 109 mg/dl (±13) and 10 mg/dl (±9), respectively. No hypoglycemic episode (<40 mg/dl) was detected. Eleven times (1.5% of all given advice) the nurses did not follow and, thus, overruled the advice of the CS-1 system. Several technical malfunctions of the device (repetitive error messages and missing data in the data log) due to communication problems between the new hardware components are shortcomings of the present version of the device. As a consequence of these technical failures of system integration, treatment had to be stopped ahead of schedule in three patients. Conclusions: Despite technical malfunctions, the performance of this prototype CS-1 decision support system was, from a clinical point of view, already effective in maintaining tight glycemic control. Accordingly, and with technical improvement required, the CS-1 system has the capacity to serve as a reliable tool for routine establishment of glycemic control in ICU patients.
Group of Signs: A New Method to Evaluate Glycemic Variability
Authors: Zaccardi Francesco , Di Stefano Paola , Busetto Elena , Federici Marco Orsini , Manto Andrea , Infusino Fabio , Lanza Gaetano Antonio , Pitocco Dario , Ghirlanda Giovanni 
Published: January 2014

Background: Glycemic variability is an important parameter used to resolve potential clinical problems in diabetic patients. It is known that glycemic variability generates oxidative stress and potentially contributes to the development of macro- and microvascular complications in diabetes. By controlling glycemic variability, it is possible to reduce these complications and to set the therapy for all patients with diabetes. The aims of this study were to (1) propose a new standardized, objective, and flexible approach to measure glycemic variability by a continuous glucose monitoring system (CGMS)—the group of signs (GOS) method; (2) compare the correlation between mean amplitude of glucose excursion (MAGE), a well-known index of glycemic variability calculated by the physician and the MAGE defined with the GOS method, in order to validate the GOS; and (3) suggest new indexes of glycemic variability. Methods: We tested the GOS algorithm on data collected by a CGMS every 5 minutes for 24 hours on 50 patients. Consequently, for 8 patients we calculated and compared the physician's MAGE in the standard way and by the GOS method. Results: Comparison between the two methods has shown high correlations, from a minimum correlation of 86% to a maximum of 98%, with p values <0.01 (Pearson test). Conclusions: Preliminary data suggest that the proposed algorithm is a valid, efficient, and reliable method able to calculate the standard MAGE on CGMS data systematically and to create other alternative glycemic variability indexes.
System-Level Control to Optimize Glucagon Counterregulation by Switch-Off of α-Cell Suppressing Signals in β-Cell Deficiency
Authors: Farhy Leon S. , McCall Anthony L. 
Published: January 2014

Background: Glucagon counterregulation (GCR) is a key protection against hypoglycemia that is compromised in diabetes. In β-cell-deficient rats, GCR pulsatility can be amplified if insulin (INS) or somatostatin (SS) are infused in the pancreatic artery and then switched off during hypoglycemia. The data indicate that these signals act by different mechanisms, and here we analyze the differences between the two switch offs (SOs) and predict the GCR-amplifying effect of their individual or combined application. Methods: A minimal control network (MCN) of α/δ-cell interactions is approximated by differential equations to explain the GCR response to a SO and test in silico the hypotheses: (i) INS SO suppresses basal and pulsatile, while SS SO blocks only pulsatile glucagon release and (ii) simultaneous application of the two switch offs will augment the individual GCR response. Results: The mechanism postulated in (i) explains the differences in the GCR responses between the SOs. The MCN predicts that simultaneous application of INS and SS decreases basal glucagon but increases post-SO amplitude, thus doubling the response of GCR achieved by each of the individual signals. Conclusion: The current analyses predict that INS and SS SOs improve defective GCR in β-cell deficiency through different but complementary mechanisms and suggest SO strategies to maximally enhance GCR in type 1 diabetes by simultaneous manipulation of the network control. These results are clinically relevant, as they could have application to design of an artificial pancreas by providing ways to augment GCR that would not require glucagon infusion.
Physical Activity into the Meal Glucose—Insulin Model of Type 1 Diabetes: In Silico Studies
Authors: Dalla Man Chiara , Breton Marc D. , Cobelli Claudio 
Published: January 2014

Introduction: A simulation model of a glucose—insulin system accounting for physical activity is needed to reliably simulate normal life conditions, thus accelerating the development of an artificial pancreas. In fact, exercise causes a transient increase of insulin action and may lead to hypoglycemia. However, physical activity is difficult to model. In the past, it was described indirectly as a rise in insulin. Recently, a new parsimonious model of exercise effect on glucose homeostasis has been proposed that links the change in insulin action and glucose effectiveness to heart rate (HR). The aim of this study was to plug this exercise model into our recently proposed large-scale simulation model of glucose metabolism in type 1 diabetes to better describe normal life conditions. Methods: The exercise model describes changes in glucose—insulin dynamics in two phases: a rapid on-and-off change in insulin-independent glucose clearance and a rapid-on/slow-off change in insulin sensitivity. Three candidate models of glucose effectiveness and insulin sensitivity as a function of HR have been considered, both during exercise and recovery after exercise. By incorporating these three models into the type 1 diabetes model, we simulated different levels (from mild to moderate) and duration of exercise (15 and 30 minutes), both in steady-state (e.g., during euglycemic—hyperinsulinemic clamp) and in nonsteady state (e.g., after a meal) conditions. Results: One candidate exercise model was selected as the most reliable. Conclusions: A type 1 diabetes model also describing physical activity is proposed. The model represents a step forward to accurately describe glucose homeostasis in normal life conditions; however, further studies are needed to validate it against data.
Prescription Checking Device Promises to Resolve Intractable Hypoglycemia
Authors: Albisser A. Michael , Alejandro Rodolfo , Sperlich Marianne , Ricordi Camillo 
Published: January 2014

Background: Satisfactory glycemic control, meeting American Diabetes Association recommendations, is often accompanied by unsatisfactory hypoglycemia. The converse is also true. We hypothesize that this diabetes treatment dilemma may be resolved by repeated, objective, prescription checks. To do this, a new, two-part device has been developed. It includes a personal diabetes database for the patient and a built-in diabetes prescription checker for the provider. Its goals are to enhance diabetes education and improve patient care. Research Design and Methods: The device includes a database and supporting software, all contained in a standard USB flash drive. Using the medical prescription, body weight, and recent self-monitored blood glucose (SMBG) data, prescription checks can be done at any time. To demonstrate the device's capabilities, an observational study was performed using data from 11 patients with type 1 diabetes mellitus, on intensified therapy, with a mean glycated hemoglobin A1c <7%, and who all suffered intractable hypoglycemia. Patients had performed SMBG contours on successive days at monthly intervals. Each contour included pre- and postmeal as well as bedtime measurements. The replicated contours were used to predict the patient's glycemic profile each month. Applying a built-in simulator to each profile, changes in the prescription were explored that were consistent with reducing the recalcitrant hypoglycemia. Results: A total of 110 glycemic profiles containing 822 profile points were explored. Of these profile points, 351 (43%) showed risks of hypoglycemia, whereas 385 (47%) fell outside desired ranges. With the simulated changes in the prescription, the predicted risks of hypoglycemia were reduced 2.5-fold with insignificant increases predicted in hemoglobin A1c levels of +0.6 ± 0.9%. Conclusions: Anovel support tool for diabetes promises to resolve the diabetes treatment dilemma. Supporting the patient, it improves self-management. Supporting the provider, it reviews the medical prescription in light of objective outcomes and formalizes interventions for maximum safety and efficacy.
Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board
Authors: Ellingsen Christian , Dassau Eyal , Zisser Howard , Grosman Benyamin , Percival Matthew W. , Jovanovič Lois , Doyle Francis J. 
Published: January 2014

Background: Type 1 diabetes mellitus (T1DM) is characterized by the destruction of pancreatic β cells, resulting in the inability to produce sufficient insulin to maintain normoglycemia. As a result, people with T1DM depend on exogenous insulin that is given either by multiple daily injections or by an insulin pump to control their blood glucose. A challenging task is to design the next step in T1DM therapy: a fully automated insulin delivery system consisting of an artificial pancreatic β cell that shall provide both safe and effective therapy. The core of such a system is a control algorithm that calculates the insulin dose based on automated glucose measurements. Methods: A model predictive control (MPC) algorithm was designed to control glycemia by controlling exogenous insulin delivery. The MPC algorithm contained a dynamic safety constraint, insulin on board (IOB), which incorporated the clinical values of correction factor and insulin-to-carbohydrate ratio along with estimated insulin action decay curves as part of the optimal control solution. Results: The results emphasized the ability of the IOB constraint to significantly improve the glucose/insulin control trajectories in the presence of aggressive control actions. The simulation results indicated that 50% of the simulations conducted without the IOB constraint resulted in hypoglycemic events, compared to 10% of the simulations that included the IOB constraint. Conclusions: Achieving both efficacy and safety in an artificial pancreatic β cell calls for an IOB safety constraint that is able to override aggressive control moves (large insulin doses), thereby minimizing the risk of hypoglycemia.
Comparison of Lancing Devices for Self-Monitoring of Blood Glucose regarding Lancing Pain
Authors: Kocher Serge , Tshiananga J. K. Tshiang , Koubek Richard 
Published: January 2014

Background: Self-monitoring of blood glucose empowers diabetes patients to effectively control their blood glucose (BG) levels. A potential barrier to frequent BG controls is lancing pain, intrinsically linked to pricking the finger several times a day. In this study, we compared different state-of-the-art lancing devices from leading manufacturers regarding lancing pain, and we intended to identify lancing devices that are less painful. Methods: First, 165 subjects compared 6 different BG monitoring systems—consisting of a lancing device and a BG meter—at home for 36 days and at least 3 BG tests per day. Second, the subjects directly compared 6 different lancing devices—independent from a BG meter—in a laboratory setting. The test results were collected in questionnaires, and lancing pain was rated on a numerical rating scale. Results: One hundred fifty-seven subjects were included in the analysis. Accu-Chek BG monitoring systems were significantly ( p ≤ .006) preferred to competitor BG monitoring systems and were rated by >50% of the subjects as “less painful” than competitor BG monitoring systems. Accu-Chek lancing devices were significantly ( p < .001) preferred to competitor lancing devices and were rated by >60% of the subjects as “less painful” than competitor lancing devices. Conclusions: We found significant differences in lancing pain between lancing devices. Diabetes patients clearly preferred lancing devices that cause less lancing pain. In order to improve patient compliance with respect to an adequate glycemic control, the medical staff should preferentially prescribe lancing devices that cause less lancing pain.
Inspiratory Efforts Achieved in Use of the Technosphere® Insulin Inhalation System
Authors: Smutney Chad C. , Friedman Emil M. , Polidoro John M. , Amin Nikhil 
Published: January 2014

Objective: The Technosphere® Insulin (IT) inhalation system comprises TI powder premetered into unit dose cartridges and the patient-friendly, reusable, breath-powered MedTone® inhaler. This high-resistance system uses a patient's inspiratory effort to effect TI powder de-agglomeration and promote subsequent deep-lung delivery. This study reports on flow and pressure data achieved by patients with diabetes using the MedTone system. Method: MedTone inhalers containing empty cartridges were adapted with pneumotach measuring devices to capture inhalation profiles. The measuring apparatuses had negligible impact on the nominal MedTone system resistance level of 0.117 kPa0.5/liters/min. Each of 56 subjects inhaled twice to mimic TI clinical study dosing instructions. Achieved inhalation profiles were characterized by peak inspiratory flow (PIF), peak inspiratory pressure (PIP), and average pressure drop from the time of PIP to 4 s ( Pavg) Results: The achieved mean PIF (± standard deviation [SD]) in all subjects was 26.74 (±6.06) liters/min after the first inhalation and was similar to the mean PIF of 26.25 (±6.23) liters/min achieved after the second inhalation. Mean PIP (±SD) achieved by subjects was 8.49 (±2.86) and 8.1 (±2.99) kPa, and mean Pavg drop (±SD) in all subjects was 6.53 (±2.24) and 6.09 (±2.08) kPa after the respective inhalations. Conclusion: Patients with diabetes demonstrated consistent inhalation efforts over two inhalations using the MedTone system. The achieved PIFs and PIPs demonstrate the capacity of this population to obtain sufficient inspiratory effort necessary for delivery of TI using the MedTone inhaler. Adequate postpeak pressures were also revealed, further supporting reliable and sustained inhalation efforts.
Display of Glucose Distributions by Date, Time of Day, and Day of Week: New and Improved Methods
Authors: Rodbard David 
Published: January 2014

Objective: There is a need for improved methods for display of glucose distributions to facilitate comparisons by date, time of day, day of the week, and other variables for data obtained using self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM). Method: Stacked bar charts are utilized for multiple ranges of glucose values, e.g., very low, low, borderline low, target range, borderline high, high, and very high. Glucose ranges for these categories can be defined by the user, e.g., <40, 40–70, 71–80, 81–140, 141–180, 181–250, and 251–400 mg/dl. Glucose distributions can be displayed by time of day, in relation to meals, by date, or by day of week. The graphic display can be generated using general purpose spreadsheet software such as Microsoft Excel or with special purpose software. Result: Stacked bar charts are extremely compact and effective. They facilitate comparison of multiple days, multiple time segments within a day, preprandial and postprandial glucose levels, days of the week, treatment periods, patients, and groups of patients. They are superior to use of pie charts in terms of compactness and in their ability to facilitate comparisons using multiple criteria and multiple subsets of the data. One can identify episodes of hypoglycemia and hyperglycemia and can display standard errors of estimates of percentages. Interpretation of these graphs is readily learned and requires minimal training. Conclusion: Use of stacked bar charts is generally superior to use of pie charts for display of glucose distributions and can potentially facilitate the analysis and interpretation of SMBG and CGM data.