bluuebunny/crossref_metadata_2025_split huggingface dataset:
Increased Arterial Augmentation and Augmentation Index as Surrogate Parameters for Arteriosclerosis in Subjects with Diabetes Mellitus and Nondiabetic Subjects with Cardiovascular Disease
Authors: Wilhelm Birgit , Klein Johannes , Friedrich Christiane , Forst Senait , Pfützner Andreas , Kann Peter H. , Weber Matthias M. , Forst Thomas 
Published: January 2014

Background: Arterial augmentation (AP) and the augmentation index (Aix) are surrogate parameters of arterial stiffness and are commonly used as predictors for cardiovascular risk. The aim of this study is to compare these parameters in diabetic subjects and nondiabetic cardiovascular risk subjects with healthy control subjects. Methods: One hundred sixty-six nonsmoking subjects aged between 35 and 70 years were included in the study, which included 100 subjects with cardiovascular disease but not diabetes (mean age 62.73±8.75 years), 33 subjects with type 2 diabetes (66.58±2.69 years), and 33 healthy controls (51.89±8.91 years). In these subjects, arterial stiffness was measured by the difference between the second and the first systolic peak of the central pressure waveform, and the Aix was calculated as the percentage of Aix from pulse pressure. Results: Arterial augmentation was increased in subjects with diabetes (DM) with 10.21±6.97 mm Hg and in subjects with cardiovascular disease but not diabetes (CV) with 10.74±5.29 mm Hg in comparison to healthy controls (C) with 6.59±3.97 mm Hg ( p < 0.0005 DM vs C; p < 0.00005 CV vs C). Moreover, Aix was increased with 26.00±9.91% in CV subjects compared to healthy controls with 19.84±9.37% ( p < 0.02 CV vs C). The augmentation index was increased with 21.12±11.21% in subjects with type 2 diabetes mellitus compared to controls, but failed to be statistically significant. There was no statistical significance in arterial augmentation or the augmentation index between CV and diabetic subjects. Conclusion: The results of our study revealed a comparable increased augmentation index as a surrogate measure of arterial stiffness and arteriosclerosis in subjects with diabetes mellitus and in nondiabetic subjects with cardiovascular disease.
Glycemia Prediction in Critically Ill Patients Using an Adaptive Modeling Approach
Authors: Herpe Tom Van , Espinoza Marcelo , Haverbeke Niels , De Moor Bart , Van den Berghe Greet 
Published: January 2014

Background: Strict blood glucose control by applying nurse-driven protocols is common nowadays in intensive care units (ICUs). Implementation of a predictive control system can potentially reduce the workload for medical staff but requires a model for accurately predicting the glycemia signal within a certain time horizon. Methods: GlucoDay (A. Menarini Diagnostics, Italy) data coming from 19 critically ill patients (from a surgical ICU) are used to estimate the initial ICU “minimal” model (based on data of the first 24 hours) and to reestimate the model as new measurements are obtained. The reestimation is performed every hour or every 4 hours. For both approaches the optimal size of the data set for each reestimation is determined. Results: The prediction error that is obtained when applying the 1-hour reestimation strategy is significantly smaller than when the model is reestimated only every 4 hours (p < 0.001). The optimal size of the data set to be considered in each reestimation process of the ICU minimal model is found to be 4 hours. The obtained average “mean percentage error” is 7.6% (SD 3.1%) and 14.6% (SD 7.0%) when the model is reestimated every hour and 4 hours, respectively. Conclusions: Implementation of the ICU minimal model in the appropriate reestimation process results in clinically acceptable prediction errors. Therefore, the model is able to predict glycemia trends of patients admitted to the surgical ICU and can potentially be used in a predictive control system.
Inflammation and Glucose Sensors: Use of Dexamethasone to Extend Glucose Sensor Function and Life Span in Vivo
Authors: Klueh Ulrike , Kaur Manjot , Montrose David C. , Kreutzer Donald L. 
Published: January 2014

Background: It has been generally accepted that the acute loss of sensor function is the consequence of sensor biofouling as a result of inflammation induced at sites of sensor implantation, as well as tissue trauma induced by the sensor and its implantation. Because anti-inflammatory therapies are used routinely to control inflammation in a wide variety of diseases, we hypothesized that anti-inflammatory therapy would likely extend glucose sensor function in vivo. To test this hypothesis, we utilized our recently developed mouse model of implantable glucose sensors and the potent anti-inflammatory steroid dexamethasone (DEX). Method: For this study, glucose sensors were implanted subcutaneously into the head and neck area of mice and sensor function was determined up to 14 days postimplantation. These mice received a daily intraperitoneal injection of DEX at a dose of 1, 6, or 10 mg/kg body weight. Results: Mice not treated with DEX lost sensor functionality very rapidly, usually within the first 24 hours postimplantation. Mice treated with DEX at the various doses had an increased sensor life span of up to 2 weeks postimplantation. Additionally, sensitivity was maintained in DEX-treated mice as compared to control mice (non-DEX treated). Histologic evaluation of tissue surrounding the site of sensor implantation had almost no inflammatory cells in DEX-treated mice, whereas control mice had an intense band of inflammation surrounding the site of sensor implantation. Conclusion: To our knowledge this is the first study directly demonstrating that anti-inflammatory therapy can extend glucose sensor function in vivo and supports the key role of inflammation in loss of sensor function in vivo, as well as the uses of anti-inflammatory therapy as a potential key adjuvant in enhancing glucose sensor function and life span in vivo.
Effect of Duration of Disease on Ventilatory Function in an Ethnic Saudi Group of Diabetic Patients
Authors: Meo Sultan A. , Al Drees Abdul Majeed , Ahmed Jehangeer , Shah Sayed Fayaz Ahmed , Al-Regaiey Khalid , Husain Ashraf , Al-Rubean Khalid 
Published: January 2014

Background: Diabetes mellitus is a leading cause of illness and death across the world and is responsible for a growing proportion of global health care expenditures. The present study was designed to observe the effect of diabetes mellitus on lung function in patients with diabetes belonging to a specific ethnic group, namely Saudis. Method: In this study, a group of 47 apparently healthy volunteer male Saudi patients with diabetes was randomly selected. Their ages ranged from 20 to 70 years. The patients were matched with another group of 50 healthy male control subjects in terms of age, height, weight, ethnicity, and socioeconomic status. Both groups met exclusion criteria as per standard. Spirometry was performed with an electronic spirometer (Schiller AT-2 Plus, Switzerland), and results were compared by a Student's t test. Results: Subjects with diabetes showed a significant reduction in forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1) relative to their matched controls. However, there were no significant differences in the forced expiratory ratio (FE1/FVC%) and the middle half of the FVC (FEF25–75%) between the groups. We observed a significantly negative correlation between duration of disease and pulmonary function, as measured by FEV1 ( r = 0.258, p = 0.04), FVC ( r = 0.282, p = 0.28), and the middle half of the FVC (FEF25–75%) ( r = 0.321, p = 0.014). Conclusions: Pulmonary function in a specific ethnic group of patients with diabetes was impaired as evidenced by a decrease in FVC and FEV1 compared to pulmonary function in matched controls. Stratification of results by years of disease revealed a significant correlation between duration of disease and a decline in pulmonary function.
Incorporating a Generic Model of Subcutaneous Insulin Absorption into the AIDA v4 Diabetes Simulator 2. Preliminary Bench Testing
Authors: Lehmann Eldon D. , Tarín Cristina , Bondia Jorge , Teufel Edgar , Deutsch Tibor 
Published: January 2014

Background: The AIDA interactive educational diabetes simulator has been available without charge for over a decade via the Internet (see www.2aida.org ). Part 1 of this report [J Diabetes Sci Technol. 2007;1(3):423–35] described the model components to be integrated to enhance the utility of the software, with the aim being to provide enhanced functionality and educational simulations of regimens utilizing insulin analogues, as well as insulin doses greater than 40 units. This report provides some preliminary subcutaneous insulin absorption bench testing results for the updated modeling prototype. Methods: An analysis has been done of the spatial distribution of insulin in the region of the injection site for different classes of insulin preparations and times after the administration of a set insulin injection. Demonstrations of the proportion of residual insulin in depot versus time after a subcutaneous bolus have also been simulated for different insulin injection volumes and concentrations, as well as to show the proportions of hexameric, dimeric, and bound insulin over time after an injection. Results: Some early bench testing results are highlighted following subcutaneous injections of a rapidly acting insulin analogue (such as lispro/Humalog or aspart/NovoLog), a short-acting (regular) insulin preparation (e.g., Actrapid), intermediate-acting insulins (both Semilente and neutral protamine Hagedorn types), and a very long-acting insulin analogue (such as glargine/Lantus). The transformation, dissociation/association, and absorption processes by which insulin moves from the subcutaneous injection site to the plasma are also illustrated. Discussion: This report demonstrates how enhanced capabilities may be added to AIDA once a new model of subcutaneous insulin absorption is incorporated. The revised approach, once fully implemented, should permit the simulation of plasma insulin profiles for rapidly acting and very long-acting insulin analogues, as well as insulin injections greater than 40 units.
Automated Computation of Glycemic Index for Foodstuffs Using Continuous Glucose Monitoring
Authors: Chlup Rudolf , Sečkař Pavel , Zapletalová Jana , Langová Kateřina , Kudlová Pavla , Chlupová Karolina , Bartek Josef , Jelenová Daniela 
Published: January 2014

Background: The glycemic index (GI) is a measure of the ability of a food to raise glucose levels after it is eaten. Continuous glucose monitoring (CGM) has been shown to give identical values of GI when compared to traditional methods. However, there has been no standardized protocol for measuring GI that takes into account interindividual variability and chronophysiological glycemic response to food. Our aim was (1) to create and describe software based on a Microsoft Excel 2000 spreadsheet to facilitate rapid, automated, accurate, and standardized processing of data obtained using recent CGM methodology to measure GI and its variability and (2) to assess the benefits of this new approach. Method: Twenty healthy subjects consumed 50 grams of glucose or four alternative foodstuffs (chocolate, apple baby food, rice squares, or yogurt) at breakfast and dinner during 1 week, resulting in 300 CGMS glucose profiles; 92% of meal tests were satisfactory for evaluation. Application and functions of the software DegifXL are described. Results: Using the new spreadsheet software DegifXL, time required for data processing for the 15 data sets for each subject was reduced from 2000 to 160 minutes relative to previously used manual methods. We characterized the GI for four foodstuffs with three replicate measurements in each of 20 subjects and evaluated between person, between time period, and between replicate GI variabilities. Conclusion: DegifXL, combined with CGM, was an efficient and effective tool for routine measurement of group- and subject-related GI.
Indication-Based Ordering: A New Paradigm for Glycemic Control in Hospitalized Inpatients
Authors: Lee Joshua , Clay Brian , Zelazny Ziband , Maynard Gregory 
Published: January 2014

Background: Inpatient glycemic control is a constant challenge. Institutional insulin management protocols and structured order sets are commonly advocated but poorly studied. Effective and validated methods to integrate algorithmic protocol guidance into the insulin ordering process are needed. Methods: We introduced a basic structured set of computerized insulin orders (Version 1), and later introduced a paper insulin management protocol, to assist users with the order set. Metrics were devised to assess the impact of the protocol on insulin use, glycemic control, and hypoglycemia using pharmacy data and point of care glucose tests. When incremental improvement was seen (as described in the results), Version 2 of the insulin orders was created to further streamline the process. Results: The percentage of regimens containing basal insulin improved with Version 1. The percentage of patient days with hypoglycemia improved from 3.68% at baseline to 2.59% with Version 1 plus the paper insulin management protocol, representing a relative risk for hypoglycemic day of 0.70 [confidence interval (CI) 0.62, 0.80]. The relative risk of an uncontrolled (mean glucose over 180 mg/dl) patient stay was reduced to 0.84 (CI 0.77, 0.91) with Version 1 and was reduced further to 0.73 (CI 0.66, 0.81) with the paper protocol. Version 2 used clinician-entered patient parameters to guide protocol-based insulin ordering and simultaneously improved the flexibility and ease of ordering over Version 1. Conclusion: Patient parameter and protocol-based clinical decision support, added to computerized provider order entry, has a track record of improving glycemic control indices. This justifies the incorporation of these algorithms into online order management.
Multicenter Validation of a Computer-Based Clinical Decision Support Tool for Glucose Control in Adult and Pediatric Intensive Care Units
Authors: Thompson B. Taylor , Orme James F. , Zheng Hui , Luckett Peter M. , Truwit Jonathon D. , Willson Douglas F. , Hite R. Duncan , Brower Roy G. , Bernard Gordon R. , Curley Martha A. Q. , Steingrub Jay S. , Sorenson Dean K. , Sward Kathy , Hirshberg Ellie , Morris Alan H. ,   for the Reengineering Critical Care Clinical Research Investigators
Published: January 2014

Introduction: Hyperglycemia during critical illness is common, and intravenous insulin therapy (IIT) to normalize blood glucose improves outcomes in selected populations. Methods differ widely in complexity, insulin dosing approaches, efficacy, and rates of hypoglycemia. We developed a simple bedside-computerized decision support protocol (eProtocol-insulin) that yields promising results in the development center. We examined the effectiveness and safety of this tool in six adult and five pediatric intensive care units (ICUs) in other centers. Methods: We required attending physicians of eligible patients to independently intend to use intravenous insulin to normalize blood glucose. We used eProtocol-insulin for glucose control for a duration determined by the clinical caregivers. Adults had an anticipated length of stay of 3 or more days. In pediatric ICUs, we also required support or intended support with mechanical ventilation for greater than 24 hours or with a vasoactive infusion. We recorded all instances in which eProtocol-insulin instructions were not accepted and all blood glucose values. An independent data safety and monitoring board monitored study results and subject safety. Bedside nurses were selected randomly to complete a paper survey describing their perceptions of quality of care and workload related to eProtocol-insulin use. Results: Clinicians accepted 93% of eProtocol-insulin instructions (11,773/12,645) in 100 adult and 48 pediatric subjects. Forty-eight percent of glucose values were in the target range. Both of these results met a priori-defined efficacy thresholds. Only 0.18% of glucose values were ≤40 mg/dl. This is lower than values reported in prior IIT studies. Although nurses reported eProtocol-insulin required as much work as managing a mechanical ventilator, most nurses felt eProtocol-insulin had a low impact on their ability to complete non-IIT nursing activities. Conclusions: A multicenter validation demonstrated that eProtocol-insulin is a valid, exportable tool that can assist clinicians in achieving control of glucose in critically ill adults and children.
Safety and Effectiveness of a Computerized Subcutaneous Insulin Program to Treat Inpatient Hyperglycemia
Authors: Juneja Rattan , Golas Adam A. , Carroll Joni , Nelson Deborah , Abad Victor J. , Roudebush Corbin P. , Flanders Samuel J. 
Published: January 2014

Background: This proof of concept study was designed to evaluate the safety and effectiveness of a computerized insulin program, the Clarian GlucoStabilizer™ Subcutaneous Insulin Program (CGS-SQ). This paper discusses the CGS-SQ's impact on the glycemic control of hospitalized patients with hyperglycemia. Methods: Patients at Methodist and Indiana University Hospitals requiring subcutaneous insulin were treated using the CGS-SQ. This program calculates subcutaneous bolus insulin doses based on the current blood glucose (BG), using an insulin sensitivity factor, the number of grams of carbohydrates eaten, and an insulin-to-carbohydrate ratio, with a goal of maintaining the patient's BG in a prespecified target range. The target range, insulin sensitivity factor, and insulin-to-carbohydrate ratio are established by the physician. Results: From April 2006 to September 2007, the CGS-SQ treated 1772 patients at Methodist and Indiana University Hospitals, with 46,575 BGs in its database. For these patients, the average BG was 158.3 mg/dl, 40.5% percent of BGs were in the default target range of 100–150 mg/dl, and 69.8% were in the wider range of 70–180 mg/dl. The hypoglycemia (BG <40 mg/dl) rate was 0.18%. Conclusions: The CGS-SQ provided a means to deliver insulin in a standardized manner, resulting in satisfactory BG control with a low hypoglycemia rate, thus serving as a tool for safe and effective insulin therapy for hospitalized patients.
Impact of Human Factors on Clinical Protocol Performance: A Proposed Assessment Framework and Case Examples
Authors: Chase J. Geoffrey , Andreassen Steen , Jensen Karsten , Shaw Geoffrey M. 
Published: January 2014

Background: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and can often produce highly variable results. Thus, tight control remains elusive as there is not enough understanding of the relationship between control performance and protocol design, particularly with regard to how a given protocol is implemented. Methods: This article examines the role of human factors and how individuals relate to technological protocols in clinical settings. The study consists of an overall brief review that is used to create a first graphical representation of the impact of human factors in clinical medical protocol implementations. This initial framework is examined in the context of two similar, but different, case studies—the specialized relative insulin and nutrition tables glycemic control protocol and the TREAT system for antibiotic selection. Results: A graphical framework relating the human factors impact on medical protocol implementation is created. This framework describes the primary impacts on performance as resulting from clinical burden and protocol transparency. Their primary effect is on compliance with the protocol, which directly affects performance and outcome, particularly in long-term studies versus short pilot studies. Summary: Compliance is a key element in obtaining the best clinical outcome that a given protocol can provide. The issues that most affect compliance are quite often unrelated to the patient or treatment, but are a function of the protocol design and its ability to integrate (by its design) into a given clinical setting. A framework for examining these issues in design and in post-hoc assessment is therefore proposed and examined in two brief case studies.
Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool
Authors: Wong Xing-Wei , Chase J. Geoffrey , Hann Christopher E. , Lotz Thomas F. , Lin Jessica , Le Compte Aaron J. , Shaw Geoffrey M. 
Published: January 2014

Objectives: The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes. Methods: The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients ( n = 40). Integral-based identification was used to develop ( n = 40) insulin sensitivity profiles. Results: The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08–7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01–0.56 mmol/liter). The model and integral-based identification of S I captured all patient dynamics with low error, which would lead to more physiological behavior simulation. Conclusions: A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.
Concealed Maternal Blood Glucose Excursions Correlate with Birth Weight Centile
Authors: Taslimi Masoud Mark , Navabi Kasra , Acosta Reinaldo , Helmer Amy , El-Sayed Yasser Y. 
Published: January 2014

Background: The objective of this study was to test the hypothesis that maternal blood glucose excursions correlate with deviation from optimized birth weight. Methods: Patients were recruited for 3-day continuous glucose monitoring (CGM) plus self-blood glucose monitoring followed by routine diabetes screening at 26–28 weeks gestation. Patients and caregivers were blinded to CGM results. The magnitude and duration of blood glucose (BG) excursions were measured as a “glycemia index.” A customized birth weight centile was calculated. Results: Twenty-three patients consented, 21 completed the study: 5 diabetic and 16 nondiabetic individuals. The duration of CGM was 72 (±7.2) hours, and each patient performed self-BG monitoring ≥3 times per day. All diabetic and 10 nondiabetic patients had several measured BG excursions above 130 mg/dl. A positive correlation was observed between birth weight centile and glycemia index above 130 ( p < 0.03); the trend persisted for nondiabetic patients alone ( p < 0.05). No significant correlation was noted between birth weight centile and average 3-day CGM values, 3-day fasting BG, average 3-day self-BG monitoring values, or diabetes screening BG value. Conclusions: The glycemia index has a better correlation with birth weight centile than BG measured by conventional methods in a mixed diabetic and nondiabetic population. Fetal exposure to maternal blood glucose excursions correlates positively with fetal growth, even in nondiabetic patients with apparently normal glucose tolerance.