bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Data Processing to Achieve a Safe Telemedical Artificial Pancreas
Authors: Hernando M. Elena , García-Sáez Gema , Martínez-Sarriegui Iñaki , Rodríguez-Herrero Agustín , Pérez-Gandía Carmen , Rigla Mercedes , de Leiva Alberto , Capel Ismael , Pons Belén , Gómez Enrique J. 
Published: January 2014

Background: The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety. Methods: The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis. Results: The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump. Conclusions: Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.
Blood Glucose Controller for Neonatal Intensive Care: Virtual Trials Development and First Clinical Trials
Authors: Compte Aaron Le , Chase J. Geoffrey , Lynn Adrienne , Hann Chris , Shaw Geoffrey , Wong Xing-Wei , Lin Jessica 
Published: January 2014

Background: Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met. Methods: This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1–3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Women's Hospital. Results: Time in the 72–126 mg/dl BG band was increased by 103–161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method. Conclusions: A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.
Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial
Authors: Magni Lalo , Forgione Marco , Toffanin Chiara , Dalla Man Chiara , Kovatchev Boris , De Nicolao Giuseppe , Cobelli Claudio 
Published: January 2014

Background: The technological advancements in subcutaneous continuous glucose monitoring and insulin pump delivery systems have paved the way to clinical testing of artificial pancreas devices. The experience derived by clinical trials poses technological challenges to the automatic control expert, the most notable being the large interpatient and intrapatient variability and the inherent uncertainty of patient information. Methods: A new model predictive control (MPC) glucose control system is proposed. The starting point is an MPC algorithm applied in 20 type 1 diabetes mellitus (T1DM) subjects. Three main changes are introduced: individualization of the ARX model used for prediction; synthesis of the MPC law on top of the open-loop basal/bolus therapy; and a run-to-run approach for implementing day-by-day tuning of the algorithm. In order to individualize the ARX model, a sufficiently exciting insulin profile is imposed by splitting the premeal bolus into two smaller boluses (40% and 60%) injected 30 min before and 30 min after the meal. Results: The proposed algorithm was tested on 100 virtual subjects extracted from an in silico T1DM population. The trial simulates 44 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. For 10 days, meals are multiplied by a random variable uniformly distributed in [0.5, 1.5], while insulin delivery is based on nominal meals. Moreover, for 10 days, either a linear increase or decrease of insulin sensitivity (±25% of nominal value) is introduced. Conclusions: The ARX model identification procedure offers an automatic tool for patient model individualization. The run-to-run approach is an effective way to auto-tune the aggressiveness of the closed-loop control law, is robust to meal variation, and is also capable of adapting the regulator to slow parameter variations, e.g., on insulin sensitivity.
Clinically Significant Disagreement between Mean Blood Glucose and Estimated Average Glucose in Two Populations: Implications for Diabetes Management
Authors: Chalew Stuart A. , Hempe James M. , McCarter Robert 
Published: January 2014

Background: Hemoglobin A1c (HbA1c) is highly correlated with mean blood glucose (MBG) levels and widely used in assessment of diabetes therapy. It has been proposed to report HbA1c in terms of an estimated average glucose (eAG) derived from the population regression of MBG on HbA1c. Pertinent to the clinical utility of eAG would be the degree of agreement between eAG and MBG estimated from multiple sampled glucose measurements over time. Methods: We examined agreement between eAG and MBG by Bland-Altman analysis from two different populations of type 1 diabetes patients: 150 children at our clinic in New Orleans and publicly available data from 1440 participants in the Diabetes Control and Complications Trial (DCCT). In New Orleans, MBG was derived from the mean of each patient's self-monitored glucose records over the 3 months before the HbA1c was obtained at the patient's clinic visit. Hemoglobin A1c was traceable to the DCCT. In DCCT participants, MBG was calculated from the patient's seven-sample glucose profile set submitted during each quarterly visit. Estimated average glucose was calculated from each individual's HbA1c using a previously reported regression equation of MBG versus HbA1c, eAG = (HbA1c * 28.7) − 47.7, derived from a continuous glucose monitoring protocol over a 12-week period. Results: The analysis showed that there is frequent and clinically significant disagreement between MBG and eAG. Estimated average glucose over or under estimated MBG by 28.7 mg/dl or greater (HbA1c difference of 1% or greater) in approximately 33% of patients from both populations. The eAG overestimation of MBG was highest at lower MBG. The difference between eAG and MBG was skewed upward with increasing mean of eAG and MBG in the DCCT. Conclusions: Frequent discordance between eAG and MBG in clinical practice will likely be confusing to patients and clinicians. In patients where eAG overestimates MBG, intensive management based on eAG alone will likely lead to greater frequency of hypoglycemic episodes. To overcome these limitations of eAG, a customized assessment of HbA1c with respect to a patient's MBG should be performed using directly monitored patient glucose levels over time.
Analysis of the Comparison of Lancing Devices for Self-Monitoring of Blood Glucose regarding Lancing Pain
Authors: Lekarcyk Joyce , Ghiloni Suzanne 
Published: January 2014

Reducing barriers to self-monitoring of blood glucose (SMBG) remains an ongoing goal. One major reported barrier is lancing pain. This analysis was written in response to the article by Kocher and associates in this issue of Journal of Diabetes Science and Technology in which 157 patients with diabetes experienced in the use of SMBG compared high market share blood glucose monitoring systems and lancing devices. Upon review of their findings, we found that their conclusions—Accu-check systems and lancing devices were preferred—were valid within the limitations of the study. However, we noted some factors that would warrant further study and possibly change the outcome. Information from this and other studies on the topic will be useful as a reference for patients and providers in working towards removing barriers to SMBG.
Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements
Authors: Zisser Howard C. , Bailey Timothy S. , Schwartz Sherwyn , Ratner Robert E. , Wise Jonathan 
Published: January 2014

Background: The purpose of this study was to compare the accuracy of measurements obtained from the DexCom™ SEVEN® system with Yellow Springs Instrument (YSI) laboratory measurements of venous blood glucose. Methods: Seventy-two subjects with insulin-requiring diabetes, aged 18–71, were enrolled in a multicenter, prospective single-arm study. All participants wore the SEVEN continuous glucose monitoring (CGM) system for one, 7-day wear period. Calibration with capillary finger stick measurements was performed 2 hours after sensor insertion and once every 12 hours thereafter. A subset of subjects (28) wore two systems simultaneously to assess precision. All subjects participated in one, 10-hour in-clinic session on day 1, 4, or 7 of the study to compare CGM measurements against a laboratory method (YSI analyzer) using venous measurements taken once every 20 minutes. Carbohydrate consumption and insulin dosing were adjusted in order to obtain a broad range of glucose values. Results: Comparison of CGM measurements with the laboratory reference method ( n = 2318) gave mean and median absolute relative differences (ARDs) of 16.7 and 13.2%, respectively. The percentage was 70.4% in the clinically accurate Clarke error grid A zone and 27.5% in the benign error B zone. Performance of the SEVEN system was consistent over time with mean and median ARD lowest on day 7 as compared to YSI (13.3 and 10.2%, respectively). Average sensor time lag was 5 minutes. Conclusions: Measurements of the DexCom SEVEN system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 7 days of wear.
Measurement of Glycated Hemoglobin A1c from Dried Blood by Turbidimetric Immunoassay
Authors: Lakshmy Ramakrishnan , Gupta Ruby 
Published: January 2014

Background: Glycated hemoglobin A1c (A1C) is an important marker in the diagnosis and treatment of diabetes. Dried blood measurement of A1C is useful in large scale epidemiological evaluation of A1C, especially to assess the impact of intervention programs. The possibility of using dried blood for measurement of A1C by the immunoturbidimetric method was explored in the present study. Method: Blood was collected from 30 patients, and blood spots were prepared and dried. The dried blood spot samples were kept for different lengths of time at 4°C to assess stability. Glycated hemoglobin was measured in whole blood and dried blood on the day of collection as well as on days 10 and 15 by immunoturbidimetric method. Results: The A1C values of 30 samples analyzed for comparison between whole blood estimation and dried blood ranged from 4.6% to 9.9%. The mean A1C on the day of sample collection was 6.01% ± 1.58% in fresh whole blood samples and 5.94% ± 1.58 % in dried blood spots. A linear and highly correlated relationship was observed between dried blood A1C values and those in whole blood ( r = 0.986 and intraclass correlation value = 0.993). Glycated hemoglobin values on day 10 and day 15 were comparable with the values on day 1 with a shift in mean of just 1% on day 10 and 3.04% on day 15. Conclusion: In conclusion, dried blood can be used for measurement of A1C by immunoturbidimetric method, and further stability of A1C measurement from dried blood for up to 15 days at 4°C makes it an ideal matrix for transportation in developing countries like India.
Pilot Evaluation of a Prototype Critical Care Blood Glucose Monitor in Normal Volunteers
Authors: Torjman Marc C. , Goldberg Michael E. , Littman Jeffrey J. , Hirsh Robert A. , Dellinger Richard P. 
Published: January 2014

Background: Availability of a highly accurate in-hospital automated blood glucose (BG) monitor could facilitate implementation of intensive insulin therapy protocols through effective titration of insulin therapy, improved BG control, and avoidance of hypoglycemia. We evaluated a functional prototype BG monitor designed to perform frequent automated blood sampling for glucose monitoring. Methods: Sixteen healthy adult volunteer subjects had intravenous catheter insertions in a forearm or hand vein and were studied for 8 hours. The prototype monitor consisted of an autosampling unit with a precise computer-controlled reversible syringe pump and a glucose analytical section. BG was referenced against a Yellow Springs Instrument (YSI) laboratory analyzer. Sampling errors for automated blood draws were assessed by calculating the percent of failed draws, and BG data were analyzed using the Bland and Altman technique. Results: Out of 498 total sample draws, unsuccessful draws were categorized as follow: 11 (2.2%) were due to autosampler technical problems, 21 (4.2%) were due to catheter-related failures, and 37 (7.4%) were BG meter errors confirmed by a glucometer-generated error code. Blood draw difficulties or failures related to the catheter site (e.g., catheter occlusion or vein collapse) occurred in 6/15 (40%) subjects. Mean BG bias versus YSI was 0.20 ± 12.6 mg/dl, and mean absolute relative difference was 10.4%. Conclusions: Automated phlebotomy can be performed in healthy subjects using this prototype BG monitor. The BG measurement technology had suboptimal accuracy based on a YSI reference. A more accurate BG point-of-care testing meter and strip technology have been incorporated into the future version of this monitor. Development of such a monitor could alleviate the burden of frequent BG testing and reduce the risk of hypoglycemia in patients on insulin therapy.
Glycemic Variability and Mortality in Critically 111 Patients: The Impact of Diabetes
Authors: Krinsley James Stephen 
Published: January 2014

Background: Glycemic variability (GV) has recently been associated with mortality in critically ill patients. The impact of diabetes or its absence on GV as a risk factor for mortality is unknown. Methods: A total of 4084 adult intensive care unit (ICU) patients admitted between October 15, 1999, and June 30, 2009, with at least three central laboratory measurements of venous glucose samples during ICU stay were studied retrospectively. The patients were analyzed according to treatment era and presence or absence of diabetes: 1460 admitted before February 1, 2003, when there was no specific treatment protocol for hyperglycemia (“PRE”) and 2624 patients admitted after a glycemic control protocol was instituted (“GC”). 3142 were patients without diabetes (“NON”), and 942 were patients with diabetes (“DM”). The coefficient of variation (CV) [standard deviation (SD)/mean glucose level (MGL)] of each patient was used as a measure of GV. Patients were grouped by MGL (mg/dl) during ICU stay (70–99, 100–119, 120–139, 140–179, and 180+) as well as by CV (<15%, 15–30%, 30–50%, and 50%+). Results: Patients with diabetes had higher MGL, SD, and CV than did NON (p < .0001 for all comparisons). Mean glucose level was lower among both GC groups compared to their corresponding PRE groups (p < .0001), but CV did not change significantly between eras. Multivariable logistic regression analysis demonstrated that low CV was independently associated with decreased risk of mortality and high CV was independently associated with increased risk of mortality among NON PRE and GC patients, even after exclusion of patients with severe (<40 mg/dl) or moderate (40–59 mg/dl) hypoglycemia. There was no association between CV and mortality among DM using the same multivariable model. Mortality among NON from the entire cohort, with MGL 70–99 mg/dl during ICU stay, was 10.2% for patients with CV < 15% versus 58.3% for those with CV 50%+; for NON with MGL 100–119 mg/dl, corresponding rates were 10.6% and 55.6%. Conclusions: Low GV during ICU stay was associated with increased survival among NON, and high GV was associated with increased mortality, even after adjustment for severity of illness. There was no independent association of GV with mortality among DM. Attempts to minimize GV may have a significant beneficial impact on outcomes of critically ill patients without diabetes.
Development of Computer-Based Training to Enhance Resident Physician Management of Inpatient Diabetes
Authors: Cook Curtiss B. , Wilson Rebecca D. , Hovan Michael J. , Hull Bryan P. , Gray Richard J. , Apsey Heidi A. 
Published: January 2014

Background: Treating hyperglycemia promotes better outcomes among inpatients. Knowledge deficits about management of inpatient diabetes are prevalent among resident physicians, which may affect the care of a substantial number of these patients. Methods: A computer-based training (CBT) curriculum on inpatient diabetes and hyperglycemia was developed and implemented for use by resident physicians and focuses on several aspects of the management of inpatient diabetes and hyperglycemia: (1) review of importance of inpatient glucose control, (2) overview of institution-specific data, (3) triaging and initial admission actions for diabetes or hyperglycemia, (4) overview of pharmacologic management, (5) insulin-dosing calculations and ordering simulations, (6) review of existing policies and procedures, and (7) discharge planning. The curriculum was first provided as a series of lectures, then formatted and placed on the institutional intranet as a CBT program. Results: Residents began using the inpatient CBT in September 2008. By August 2009, a total of 29 residents had participated in CBT: 8 in family medicine, 12 in internal medicine, and 9 in general surgery. Most of the 29 residents confirmed that module content met stated objectives, considered the information valuable to their inpatient practices, and believed that the quality of the online modules met expectations. The majority reported that the modules took just the right amount of time to complete (typically 30 min each). Conclusions: Improvement in inpatient diabetes care requires continuous educational efforts. The CBT format and curriculum content were well accepted by the resident physicians. Ongoing assessment must determine whether resident practice patterns are influenced by such training.
Treatment Satisfaction and Quality of Life for an Integrated Continuous Glucose Monitoring/Insulin Pump System Compared to Self-Monitoring plus an Insulin Pump
Authors: Rubin Richard R. , Peyrot Mark 
Published: January 2014

Background: Little is known about how the most advanced technology affects treatment satisfaction and health-related quality of life (HRQOL) in adults with diabetes. This study was designed to assess treatment satisfaction and HRQOL among users of an integrated real-time (RT) continuous glucose monitoring (CGM)/continuous subcutaneous insulin infusion (CSII) system compared with those using self-monitoring of blood glucose (SMBG) with CSII. Methods: Participants were 311 adult respondents to an Internet survey, 162 using RT-CGM/CSII, 149 using SMBG + CSII (median age 43 years; type 1 diabetes 94%; diabetes duration >15 years 61%; median insulin use 15 years). Respondents completed instruments assessing glucose monitoring system and insulin delivery system convenience, interference, burden, glucose control efficacy, cost satisfaction, overall satisfaction, and treatment preference, as well as quality of life (diabetes-related worries, social burden, and psychological well-being). Real-time CGM/CSII users also assessed specific elements of the RT-CGM/CSII system. Group differences were assessed using analysis of covariance controlling for respondent characteristics. Results: The RT-CGM/CSII group gave significantly better ratings than the SMBG + CSII group for their glucose monitoring system's glucose control efficacy, overall satisfaction, desire to switch, and willingness to recommend, and significantly worse ratings for interference with daily activities. The RT-CGM/CSII group gave significantly better ratings than the SMBG + CSII group for their insulin delivery system's convenience and glucose control efficacy, overall satisfaction, desire to switch, and willingness to recommend. Real-time CGM/CSII users gave positive ratings of all system features. Conclusions: Users of the integrated RT-CGM/CSII system reported more benefits of treatment, higher treatment satisfaction and quality of life, and greater preference for this system than SMBG + CSII users.
A Benchtop Closed-loop System Controlled by a Bio-Inspired Silicon Implementation of the Pancreatic β Cell
Authors: Oliver Nick , Georgiou Pantelis , Johnston Desmond , Toumazou Christofer 
Published: January 2014

The normal pancreatic f-cell membrane depolarizes in response to increasing concentrations of glucose in a bursting pattern. At <7 mM (126 mg/dl), the cell is electrically silent. The bursting pulse width increases as glucose rises >7 m M (126 mg/dl) until a continuous train of bursting is seen at >25 m M (450 mg/dl). A bio-inspired silicon device has been developed using analogue electronics to implement membrane depolarization of the β cell. The device is ultralow powered, miniaturized (5 × 5 mm), and produces a bursting output identical to that characterized in electrophysiological studies. Objective: The goal of this study was to demonstrate the ability of silicon implementation of β-cell electrophysiology to respond to a simulated glucose input and to drive an infusion pump in vitro. Method: The silicon device response to a current source was recorded at varying simulated glucose concentrations. Subsequently, the bursting response to a changing analyte concentration measured by an amperometric enzyme electrode was converted to a voltage, driving a syringe pump loaded with a 50-ml syringe containing water. Results: Bursting responses are comparable to those recorded in electrophysiology. Silicon β-cell implementation bursts with a pulse width proportional to concentration and is able to drive an infusion pump. Conclusion: This is the first in vitro demonstration of closed loop insulin delivery utilizing miniaturized silicon implementation of β-cell physiology in analogue electronics.