bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control
Authors: Gillis Rachel , Palerm Cesar C. , Zisser Howard , Jovanovic Lois , Seborg Dale E. , Doyle Francis J. 
Published: January 2014

Background: A primary challenge for closed-loop glucose control in type 1 diabetes mellitus (T1DM) is the development of a control strategy that will be applicable during all daily activities, including meals, stress, and exercise. A model-based control algorithm requires a mathematical model that has the simplicity for online glucose prediction, yet retains the complexity necessary to cope with variations in insulin sensitivities and carbohydrate ingestion. Methods: A modified Bergman minimal model was linearized for Kalman filter (KF) state estimation on data from T1DM subjects, and multiple methods of parameter augmentation were developed for online adaptation. In addition, model deterioration for glucose prediction was assessed to determine an appropriate prediction horizon for model predictive control (MPC). Furthermore, MPC strategies were validated using advisory mode simulations. Results: Twenty days of continuous glucose data, which included 97 meals, were evaluated for three subjects. A constant parameter minimal model was used to predict glucose levels for normal days with meal announcement and with a maximum prediction horizon of approximately 45 minutes. In order to attain this prediction horizon in the absence of meal announcement, parameter adaptation was necessary to capture the glucose disturbance. Evaluation of advisory mode MPC permitted effective tuning for a moderately aggressive controller that responded well to meal disturbances. Conclusions: Estimation and prediction of glucose were accomplished using a KF based on a modified Bergman model. For a model with no meal announcement, parameter adaptation provided the means for closed-loop implementation. This state estimation and model validation scheme established the necessary framework for advisory mode MPC.
Blood-Induced Interference of Glucose Sensor Function in Vitro: Implications for in Vivo Sensor Function
Authors: Klueh Ulrike , Liu Zenghe , Ouyang Tianmei , Cho Brian , Feldman Ben , Henning Timothy P. , Kreutzer Don 
Published: January 2014

Background: Although tissue hemorrhages, with resulting blood clots, are associated with glucose sensor implantation, virtually nothing known is about the impact of red blood cells and red blood cell clots on sensor function in vitro or in vivo. In these studies, we tested the hypothesis that blood can directly interfere with glucose sensor function in vitro. Methods: To test this hypothesis, heparinized human whole blood (HWB) and nonheparinized human whole blood (WB) were obtained from normal individuals. Aliquots of HWB and WB samples were also fractionated into plasma, serum, and total leukocyte (TL) components. Resulting HWB, WB, and WB components were incubated in vitro with an amperometric glucose sensor for 24 hours at 37°C. During incubation, blood glucose levels were determined periodically using a glucose monitor, and glucose sensor function (GSF) was monitored continuously as nanoampere output. Results: Heparinized human whole blood had no significant effect on GSF in vitro, nor did TL, serum, or plasma-derived clots from WB. Sensors incubated with WB displayed a rapid signal loss associated with clot formation at 37°C. The half-life was 0.8 ± 0.2 hours (n = 16) for sensors incubated with WB compared to 3.2 ± 0.5 (n = 12) for sensors incubated with HWB with a blood glucose level of approximately 100 mg/dl. Conclusion: These studies demonstrated that human whole blood interfered with GSF in vitro. These studies further demonstrated that this interference was related to blood clot formation, as HWB, serum, plasma-derived clots, or TL did not interfere with GSF in vitro in the same way that WB did. These in vitro studies supported the concept that the formation of blood clots at sites of glucose sensor implantation could have a negative impact on GSF in vivo.
A1cNow® InView™: A New Simple Method for Office-Based Glycohemoglobin Measurement
Authors: Mattewal Amarbir , Aldasouqi Saleh , Solomon David , Gossain Ved , Koller Anthony 
Published: January 2014

Background: Glycohemoglobin A1c (HbAlc) is a universally accepted tool for glycemic control. Portable HbAlc devices for use in physicians' offices are desirable because they provide immediate results that physicians can share with their patients. This has been shown to enhance self-management in patients with diabetes. We undertook this study to evaluate the accuracy and precision of a recently introduced device, the AlcNow® InView™ capillary monitor. Method: Previously tested EDTA-preserved whole blood samples from our laboratory pool were preselected based on the results of HbAlc to cover a range from 4 to 13%. HbAlc was then measured using an AlcNow InView capillary monitor. Blinded aliquots of these samples were then sent to a National Glycohemoglobin Standardization Program (NGSP)-certified reference laboratory for comparison. One sample with a laboratory HbAlc result of 9.2% was measured with the InView device nine successive times to assess the device precision. The consistency between the measurement of HbAlc measured by the reference laboratory and the AlcNow InView device was analyzed via linear regression. Results: Thirty-five samples were tested. The correlation between HbAlc measured by the InView device and the reference laboratory, as well as our own laboratory, was 0.96. The coefficient of variation was 2.7l°%. Conclusions: Results of this study confirm the accuracy and precision of the InView capillary HbAlc monitor. However, the feasibility, reproducibility, and cost-effectiveness of this promising device in the real-life settings of physicians' offices must be verified by prospective clinical studies.
Parameters Affecting Postprandial Blood Glucose: Effects of Blood Glucose Measurement Errors
Authors: Koschinsky Theodor , Heckermann Sascha , Heinemann Lutz 
Published: January 2014

Background: The Diabetes Error Test Model (DETM) has been developed to characterize the clinical relevance of the large and varying margins of error of parameters affecting postprandial blood glucose (BG) levels, which increase the risk for hypo- or hyperglycemia. Methods: The DETM is based on a treatment concept aimed at normoglycemia after meals. The model includes as parameters (a) preprandial BG measurement by patient self-monitoring (SMBG), (b) patient estimate of carbohydrate amounts (CARB-P) in food, (c) effect of CARB-P on maximum BG increase, (d) effect of insulin on maximum BG decrease, and (e) insulin dosage. Covering the relevant range of preprandial BG (30–330 mg/dl), the DETM simulates the maximum effect of these parameters and their margins of error on postprandial BG values. Results: According to the DETM, a SMBG error of +20% results in normoglycemia (BG range: 60–160 mg/dl) as the postprandial outcome if preprandial BG values are in the range of 30–130 or 260–330 mg/dl, but can unexpectedly result in hypoglycemia if preprandial BG values are between 131 and 259 mg/dl. If the SMBG error of +20% is combined, e.g., with an error of CARB-P estimate in the food of +20%, hypoglycemia as the postprandial outcome is worsened. If one combines the effects of errors of more than two parameters, even with errors that are so small that they have no clinically relevant dysglycemic effect on postprandial BG per se (e.g., ±6%), this can result in postprandial hypo- or hyperglycemic values. Conclusion: The DETM simulates the effects of errors of parameters affecting postprandial BG within the clinically relevant BG range. The DETM offers the opportunity to evaluate the clinical relevance of these errors and their contribution to the increased risk of meal-related excessive glucose excursions during intensified insulin therapy.
How Much Do Forgotten Insulin Injections Matter to Hemoglobin A1c in People with Diabetes? a Simulation Study
Authors: Randløv Jette , Poulsen Jens Ulrik 
Published: January 2014

Background: Forgotten or omitted insulin injections are an important contributing factor to poor glycemic control in people with type 1 diabetes. This study uses mathematical modeling and examines the impact on hemoglobin A1c (HbA1c) levels if insulin injections are forgotten. The simulation concerns people with type 1 diabetes on intensive insulin therapy. Methods: Five sets of blood glucose profiles with and without a forgotten injection were obtained. The difference to HbA1c was calculated using an HbA1c estimator on the profiles and was multiplied by the frequency of forgotten events. A frequency of 2.1 forgotten injections per week was found in the literature. Results: Calculations showed that forgetting 2.1 meal-related injections per week would lead to an increase in HbA1c of at least 0.3–0.4% points, and similarly 0.2–0.3% points related to forgotten injections of the long-acting insulin. In case of even more pronounced nonadherence (e.g., if 39% of all injections are forgotten) there is a possible increase of HbA1c of 1.8% points. Conclusions: The magnitude of the possible improvement in HbA1c agrees well with other studies in the relation between adherence and HbA1c levels. The estimated numbers suggest that missing injections are an important reason for suboptimal treatment.
Utilization of Nerve Conduction Studies for the Diagnosis of Polyneuropathy in Patients with Diabetes: A Retrospective Analysis of a Large Patient Series
Authors: Kong Xuan , Lesser Eugene A. , Potts Frisso A. , Gozani Shai N. 
Published: January 2014

Background: Diabetic polyneuropathy (DPN) is a disabling complication of diabetes mellitus. A population-based analysis of physician utilization of nerve conduction studies (NCS) for the assessment of DPN was conducted. Methods: All electrodiagnostic encounters over a 30-month period using a computer-based neurodiagnostic instrument linked to a data registry were analyzed retrospectively. The DPN case definition was abnormal sural and peroneal nerve conduction. Results: The study cohort consisted of a total of 63,779 electrodiagnostic encounters performed by 3468 physician practices. Primary care and internal medicine physicians represented 80.1% of the practices and accounted for 65.7% of the encounters. Endocrinologists represented 4.6% of the practices and 20.1% of the encounters. The demographics of patients were 52.7% female; 63.4 ± 11.8 (mean ± standard deviation) years (age); 168.1 ± 10.9 cm (height); 92.2 ± 22.6 kg (weight); and 32.6 ± 7.2 kg/m2 (body mass index). The most common peroneal abnormality was F-wave latency (33.6%). The sural nerve response latency and amplitude parameters had similar abnormality rates (58.3 and 62.7%). DPN was identified in 52.6% of the encounters; in another 19.3% no neuropathy was found. Conclusions: For over 70% of the patients, the specific diagnostic question of the presence of DPN was addressed by NCS with evidence-based criteria. The demographic features were strongly associated with risk of diabetes and DPN, suggesting that NCS were applied to appropriate demographic subgroups. The rate of DPN was also comparable to levels seen by academic electromyography laboratories. In 32.6% of the encounters the NCS suggested a posttest diagnosis other than DPN. This rate was similar to the results of referral to traditional electromyography laboratories. This study demonstrated that NCS using computer-based electrodiagnostic equipment was a suitable tool for the diagnosis of DPN. Furthermore, this technology permits examination of DPN in large populations.
Improving Hyperglycemia in the Hospital: Outcomes of a Nursing In-Service to Evaluate Acceptance of a Web-Based Insulin Infusion Calculator
Authors: Hermayer Kathie L. , Hushion Timothy V. , Arnold Pamela C. , Wojciechowski Barbara 
Published: January 2014

Background: Many insulin infusion protocols are available for clinical use. We developed a Web-based, online intravenous insulin infusion calculator (IVIIC) for use in our intensive care and medical—surgical units. Methods: In September 2006, we implemented a quality improvement project: an online survey to evaluate the acceptance of this protocol by the nursing staff. Of the 103 registered nurses (RNs) who participated, there was no difference among experience levels of the RNs (≥ or <5 years) or among durations that RNs had been working within their unit (≥ or <2 years). Results: The nurses were surveyed regarding the use and interpretation of the protocol, their comfort with, confidence in, and experience in using the protocol. More than 80% of the RNs found the protocol easy to implement, easy to interpret, and successful in controlling the blood glucose levels. Approximately 71% (±9%) of the RNs were comfortable with the tight blood glucose levels of the protocol. The nurses' confidence with the protocol was 82% (±8%), likely because 70% (±9%) of the nurses believed the training to be adequate. Significantly less than 25% of the RNs (18 ± 7%) believed it was necessary to deviate from the protocol. More than 85% of the RNs appreciated the ability to make changes at their level of practice (92 ± 5%). Conclusions: In summary, the IVIIC is well accepted by RNs for care of hyperglycemia in a hospital setting.
Improvement in Glycemic Control and Outcome Corresponding to Intensive Insulin Therapy Protocol Development
Authors: Shulman Rob , Finney Simon J. , Shah Neelam , Ali Md Shawkat , Greene Russell , Glynne Paul A. 
Published: January 2014

Background: Intensive insulin therapy (IIT) has been shown to reduce mortality and morbidity in longer stay, critically ill patients. However, this has been demonstrated in a single site, whereas two multicentric studies have been terminated prematurely mainly due to hypoglycemia. Other difficulties with IIT include efficacy of glycemic control. This report describes how IIT can be improved by protocol simplification and removal of glucose supplementation. Methods: A clinical information system established at each bedspace guided staff through the IIT algorithms. Time spent within predefined glycemic ranges was calculated assuming a linear trend between successive measurements. Three groups were investigated retrospectively: IIT1 protocol,1 an updated IIT2 version, and intuitive nurse dosing of conventional insulin therapy (CIT). Results: Fifty consecutive, critically ill patients were included in each study group. Patient characteristics were similar in each group. The frequency of CIT and IIT2 blood glucose measurements were 11.6 and 11.5 measurements per day, respectively, while the IIT1 measurements were more frequent (14.5 measurements per day). The mean proportion of time spent in the target glycemic range (4.4–6.1 mmol/liter) was highest in the IIT2 group (34.9%), as compared to the IIT1 (22.9%) and CIT groups (20.3%) ( p <.001). Survival at 28 days was 74.5% for IIT2 (highest), 68% for IIT1, and 48% for CIT ( p = .02). There were a similar number of those experiencing a severe hypoglycemic event in each group. Conclusions: IIT protocol optimization was associated with increased glycemic control and improved 28–day survival. The better optimized IIT2 protocol provided tighter control than either the IIT1 or CIT protocol, without increased sampling or incidence of hypoglycemia. The clinical effectiveness of the IIT algorithm appeared to be improved by simplifying the protocol to meet the needs of the critical care unit.
In Silico Testing—Impact on the Progress of the Closed Loop Insulin Infusion for Critically Ill Patients Project
Authors: Wilinska Malgorzata E. , Chassin Ludovic J. , Hovorka Roman 
Published: January 2014

Background: In silico testing was used extensively in the European Commission-funded Closed Loop Insulin Infusion for Critically Ill Patients (Clinicip) project, which aimed to develop prototype systems for closed loop glucose control in the critically ill. This article presents two examples of how the simulation environment was utilized in this project. Methods: The in silico simulation environment was used to simulate a 48-hour clinical trial in a surgical intensive care unit to achieve tight glycemic control. A set of 10 critically ill synthetic subjects was selected for two different studies. In the first study, two sets of clinical trials were simulated using two versions of a model predictive control (MPC)-based glucose control algorithm: MPC Version 0.1.5 with hourly glucose measurements and updated MPC Version 1.4.3 with variable 1- to 4-hour glucose sampling. In the second study, four sets of clinical trials were simulated with four levels of measurement error at 2, 5, 7, and 15% coefficient of variation corresponding to the measurement error of commercially available glucose measuring devices. Results: In the first study, more frequent glucose measurements associated with MPC Version 0.1.5 facilitated more efficacious and safer glucose control compared to that obtained with the prolonged and variable glucose sampling rate associated with MPC Version 1.4.3. In the second study, a marked deterioration in safety measures was observed in studies performed with a measurement error of 15%. Conclusions: The presented simulation studies highlighted two important uses of in silico simulation environment in the Clinicip project. The impressive progress and successful completion of the Clinicip project would not be possible without computer-based simulations.
Model-Based Insulin Sensitivity as a Sepsis Diagnostic in Critical Care
Authors: Blakemore Amy , Wang Sheng-Hui , Le Compte Aaron , Shaw Geoffrey M. , Wong Xing-Wei , Lin Jessica , Lotz Thomas , Hann Christopher E. , Chase J. Geoffrey 
Published: January 2014

Background: Timely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time. Methods: Receiver operating characteristic curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model-based insulin sensitivity ( S I) and a simpler metric ( SS I) that was estimated from glycemic control data of 30 patients with sepsis and can be calculated in real time without use of a computer. Results were compared to the insulin sensitivity profiles of a general intensive care unit population of 113 patients without sepsis and 30 patients with sepsis, comprising a total of 26,453 patient hours. Patients with sepsis were identified as having sepsis based on a sepsis score ( ss) of 3 or higher ( ss = 0–4 for increasing severity). Patients with type I or type II diabetes were excluded. Ethics approval for this study was granted by the South Island Regional Ethics Committee. Results: Receiver operating characteristic cutoff values of S I = 8 × 10–5 liter mU−1 min−1 and SS I = 2.8 × 10−4 liter mU−1 min−1 were determined for ss ≥ 3. The model-based S I fell below this value in 15% of all patient hours. The S I test had a negative predictive value of 99.8%. The test sensitivity was 78% and specificity was 82%. However, the positive predictor value was 2.8%. Slightly lower sensitivity (68.8%) and specificity (81.7%), but equally good negative prediction (99.7%), were obtained for the estimated SS I. Conclusions: Insulin sensitivity provides a negative predictive diagnostic for sepsis. High insulin sensitivity rules out sepsis for the majority of patient hours and may be determined noninvasively in real time from glycemic control protocol data. Low insulin sensitivity is not an effective diagnostic, as it can equally mark the presence of sepsis or other conditions.
Diabetes Self-Management Care via Cell Phone: A Systematic Review
Authors: Krishna Santosh , Boren Suzanne Austin 
Published: January 2014

Background: The objective of this study was to evaluate the evidence on the impact of cell phone interventions for persons with diabetes and/or obesity in improving health outcomes and/or processes of care for persons with diabetes and/or obesity. Methods: We searched Medline (1966–2007) and reviewed reference lists from included studies and relevant reviews to identify additional studies. We extracted descriptions of the study design, sample size, patient age, duration of study, technology, educational content and delivery environment, intervention and control groups, process and outcome measures, and statistical significance. Results: In this review, we included 20 articles, representing 18 studies, evaluating the use of a cell phone for health information for persons with diabetes or obesity. Thirteen of 18 studies measured health outcomes and the remaining 5 studies evaluated processes of care. Outcomes were grouped into learning, behavior change, clinical improvement, and improved health status. Nine out of 10 studies that measured hemoglobin A1c reported significant improvement among those receiving education and care support. Cell phone and text message interventions increased patient—provider and parent—child communication and satisfaction with care. Conclusions: Providing care and support with cell phones and text message interventions can improve clinically relevant diabetes-related health outcomes by increasing knowledge and self-efficacy to carry out self-management behaviors.
Skin Advanced Glycation End Product Accumulation is Poorly Reflected by Glycemic Control in Type 2 Diabetic Patients (ZODIAC-9)
Authors: Gerrits Esther G. , Lutgers Helen L. , Kleefstra Nanne , Groenier Klaas H. , Smit Andries J. , Gans Rijk O. B. , Bilo Henk J. G. 
Published: January 2014

Background: Glycemic memory can be reflected by tissue accumulation of advanced glycation end products (AGEs). In type 1 diabetes mellitus (T1DM) patients, hemoglobin A1c (HbA1c) levels over various time periods poorly predicted the accumulation of different AGEs in skin biopsies. Our aim was to investigate whether HbA1c assessments can predict the change in skin AGEs during time in type 2 diabetes mellitus (T2DM). Methods: We included 452 T2DM patients participating in a shared-care setting, who are screened annually for HbA1c and diabetic complications. Baseline and follow-up levels of skin AGEs were assessed with a validated noninvasive autofluorescence (AF) method, which is based on the fluorescence characteristics of certain AGEs. Results: Our study population had a mean age of 65 years and 54% were female. After a mean follow-up duration of 3.3 years, linear regression analyses showed weak relationships among different assessments of HbA1c (baseline, maximum, mean, and variance of HbA1c) and skin AF at follow-up. Baseline skin AF and age were predictors of skin AF at follow-up, but diabetes duration, smoking, and creatinine were of less or no predictive value for skin AF at follow-up. Conclusions: In our T2DM population, integrated HbA1c assessments over years poorly predict the change in skin AGE level measured by skin AF. These findings agree with results in patients with T1DM. This suggests either the need for longer exposure to glucose disturbances to change tissue AGEs or other mechanisms, such as oxidative stress, leading to AGE accumulation.