bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical Hypoglycemia Prediction
Authors: Cameron Fraser , Niemeyer Günter , Gundy-Burlet Karen , Buckingham Bruce 
Published: January 2014

Background: Hypoglycemia presents a significant risk for patients with insulin-dependent diabetes mellitus. We propose a predictive hypoglycemia detection algorithm that uses continuous glucose monitor (CGM) data with explicit certainty measures to enable early corrective action. Method: The algorithm uses multiple statistical linear predictions with regression windows between 5 and 75 minutes and prediction horizons of 0 to 20 minutes. The regressions provide standard deviations, which are mapped to predictive error distributions using their averaged statistical correlation. These error distributions give confidence levels that the CGM reading will drop below a hypoglycemic threshold. An alarm is generated if the resultant probability of hypoglycemia from our predictions rises above an appropriate, user-settable value. This level trades off the positive predictive value against lead time and missed events. Results: The algorithm was evaluated using data from 26 inpatient admissions of Navigator® 1-minute readings obtained as part of a DirecNet study. CGM readings were postprocessed to remove dropouts and calibrate against finger stick measurements. With a confidence threshold set to provide alarms that correspond to hypoglycemic events 60% of the time, our results were (1) a 23-minute mean lead time, (2) false positives averaging a lowest blood glucose value of 97 mg/dl, and (3) no missed hypoglycemic events, as defined by CGM readings. Using linearly interpolated FreeStyle capillary glucose readings to define hypoglycemic events provided (1) the lead time was 17 minutes, (2) the lowest mean glucose with false alarms was 100 mg/dl, and (3) no hypoglycemic events were missed. Conclusion: Statistical linear prediction gives significant lead time before hypoglycemic events with an explicit, tunable trade-off between longer lead times and fewer missed events versus fewer false alarms.
Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers
Authors: Percival Matthew W. , Zisser Howard , Jovanovic Lois , Doyle Francis J. 
Published: January 2014

Background: Using currently available technology, it is possible to apply modern control theory to produce a closed-loop artificial β cell. Novel use of established control techniques would improve glycemic control, thereby reducing the complications of diabetes. Two popular controller structures, proportional-integral-derivative (PID) and model predictive control (MPC), are compared first in a theoretical sense and then in two applications. Methods: The Bergman model is transformed for use in a PID equivalent model-based controller. The internal model control (IMC) structure, which makes explicit use of the model, is compared with the PID controller structure in the transfer function domain. An MPC controller is then developed as an optimization problem with restrictions on its tuning parameters and is shown to be equivalent to an IMC controller. The controllers are tuned for equivalent performance and evaluated in a simulation study as a closed-loop controller and in an advisory mode scenario on retrospective clinical data. Results: Theoretical development shows conditions under which PID and MPC controllers produce equivalent output via IMC. The simulation study showed that the single tuning parameter for the equivalent controllers relates directly to the closed-loop speed of response and robustness, an important result considering system uncertainty. The risk metric allowed easy identification of instances of inadequate control. Results of the advisory mode simulation showed that suitable tuning produces consistently appropriate delivery recommendations. Conclusion: The conditions under which PID and MPC are equivalent have been derived. The MPC framework is more suitable given the extensions necessary for a fully closed-loop artificial β cell, such as consideration of controller constraints. Formulation of the control problem in risk space is attractive, as it explicitly addresses the asymmetry of the problem; this is done easily with MPC.
A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Model Structure and Parameter Identification
Authors: Wong Jason , Chase J. Geoffrey , Hann Christopher E. , Shaw Geoffrey M. , Lotz Thomas F. , Lin Jessica , Le Compte Aaron J. 
Published: January 2014

Objective: The goal of this study was to develop a unified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role. The model must model the plasma insulin appearance of a wide range of current insulins, especially monomer insulin and insulin glargine, utilizing common chemical states and transport rates, where appropriate. Methods: A compartmental model was developed with 13 patient-specific model parameters covering six diverse insulin types [rapid-acting, regular, neutral protamine Hagedorn (NPH), lente, ultralente, and glargine insulin]. Model parameters were identified using 37 sets of mean plasma insulin time-course data from an extensive literature review via nonlinear optimization methods. Results: All fitted parameters have a coefficient of variation <100% (median 51.3%, 95th percentile 3.6–60.6%) and can be considered a posteriori identifiable. Conclusion: A model is presented to describe SC injected insulin appearance in plasma in a diabetes decision support role. Clinically current insulin types (monomeric insulin, regular insulin, NPH, insulin, and glargine) and older insulin types (lente and ultralente) are included in a unified framework that accounts for nonlinear concentration and dose dependency. Future work requires clinical validation using published pharmacokinetic studies.
A Review of Current Evidence with Continuous Glucose Monitoring in Patients with Diabetes
Authors: Block Christophe De , Manuel-y-Keenoy Begoña , Van Gaal Luc 
Published: January 2014

Devices that measure glucose on a near-continuous basis may provide a better insight into glycemic profiles, allowing patients with diabetes to make therapeutic adjustments to improve metabolic control, thereby reducing the risk of diabetic complications. Motivated and technologically adept patients with brittle diabetes, hypoglycemia unawareness, diabetic pregnancy, or who use pumps might benefit. Current evidence of continuous glucose monitoring (CGM) on health outcome in patients with diabetes is critically reviewed. No data are available on chronic complications or mortality. Therefore, surrogate endpoints need to be investigated, particularly HbA1c, number of hypo- and hyperglycemic episodes, time within normal, high, or low glucose concentrations, glycemic variability, and quality of life. Randomized controlled trials (RCTs) using CGM in a retrospective way did not show metabolic improvement. In contrast, most RCTs applying real-time CGM showed a decrease in HbA1c, reduced glycemic variability, and a diminished number and length of hypo- and hyperglycemic events. Using accurate, real-time CGM devices improves quality of life by reducing the fear of unexpected hypoglycemic events. These beneficial effects were observed despite the fact that in most studies no clear treatment algorithm based on CGM results was provided to the patients. However, most trials were too short in duration, with a variable use of CGM, and were performed in small study samples. In conclusion, real-time CGM systems can improve metabolic control, reduce hypoglycemic episodes, and improve quality of life. Whether this holds true for longer time periods and in the majority of patients remains to be proven. In the long term, CGM might help to reduce chronic diabetes complications and perhaps also mortality, thereby reducing health care costs.
The Benefits of Continuous Glucose Monitoring and a Glucose Monitoring Schedule in Individuals with Type 1 Diabetes during Recreational Diving
Authors: Adolfsson Peter , Örnhagen Hans , Jendle Johan 
Published: January 2014

Background: Our objective is to evaluate the Medtronic CGMS® continuous glucose monitoring system and plasma glucose (PG) measurement performed in a monitoring schedule as tools to identify individuals with type 1 diabetes at risk when diving. Methods: We studied 24 adults, 12 type 1 diabetes subjects and 12 controls, during 5 recreational scuba dives performed on 3 consecutive days. The CGMS was used by all participants on all the days and all the dives. Comparisons were made between PG performed in a monitoring schedule during the days of diving, self-monitored blood glucose (SMBG) performed 2 weeks prior to diving, and the CGMS during the study. Results: One hundred seventeen dives were performed. Hypoglycemia (<70 mg/dl) was found in six individuals and on nine occasions. However, no symptoms of hypoglycemia were present during or immediately postdiving. In one case, repetitive hypoglycemia prediving gave rise to a decision not to dive. None of the dives were aborted. The number of hypoglycemic episodes, 10 min prediving or immediately postdiving, were related to the duration of diabetes, r = 0.83 and p = 0.01, and the percentage of SMBG values below target (<72 mg/dl), r = 0.65 and p = 0.02. Moreover, the number of hypoglycemic episodes was also related to the total duration below low limit (<70 mg/dl), measured by the CGMS, r = 0.74 and p = 0.006. Conclusion: Safe dives are possible to achieve by well-informed, well-controlled individuals with type 1 diabetes. Using downloaded SMBG, CGMS, and repetitive PG in a monitoring schedule, it is possible to identify those subjects who are suitable for diving.
Telemedicine Process Used to Implement an Effective and Functional Screening Program for Diabetic Retinopathy
Authors: Wilson Robb R. , Silowash Russell , Anthony Leslie , Cecil R. Ann , Eller Andrew 
Published: January 2014

Background: Diabetic retinopathy, damage to the blood vessels in the retina, is the leading cause of blindness in adults 20–74 years of age. Nearly 300 million people worldwide have diabetes and nearly half of all people with diabetes will develop some degree of diabetic retinopathy during their lifetime. It has been estimated that blindness from diabetic retinopathy is preventable in at least 65% of cases, if detected early. The aim of the project was to develop a flexible, modular, mobile method for screening individuals that could be used effectively in a variety of medical and community settings. Methods: Our project created software to support a simple, effective retinal screening process for people with diabetes. The system included four separate software components: registration, imaging, grading, and tracking/reporting. The imaging component consisted of customized software running on a computer attached to a camera that drives retinal image acquisition. Results: Seven hundred and six subjects with diabetes were successfully consented, registered, imaged, and had their eye images graded. The mean time for subjects to be registered, imaged, and have eye images graded was 00:12:53. Seventy-six percent of the sample was instructed to follow-up with their eye doctor in 1 year (had no retinopathy or microaneurysms). Only six patients (0.8 %) were asked to see their eye doctor within 6 weeks (proliferative retinopathy or diabetic maculopathy). Conclusion: Our project successfully demonstrated that the retinal screening software and workflow process can be used to overcome challenges of providing adequate screening and diagnostic services for people at risk for diabetic retinopathy.
Performance Evaluation of a New Blood Glucose Monitor That Requires No Coding: The OneTouch® Vita™ System
Authors: Young Joanna K. , Ellison John M. , Marshall Robert 
Published: January 2014

Background: Improvements to blood glucose monitoring systems aim to simplify the testing process, reduce or eliminate errors, and provide additional information for patients with diabetes. New systems must continue to demonstrate high-quality analytical performance. The new OneTouch® Vita™ System (LifeScan, Inc., Milpitas, CA) offers a no-code testing process and proven technology found in the OneTouch® Ultra® System. Comparative studies were conducted with the new and established systems to evaluate their precision and accuracy. Methods: Within-run precision in blood, total precision with controls, and system accuracy were evaluated using three lots of OneTouch Vita Test Strips and one lot of OneTouch Ultra Test Strips. Accuracy was tested across a wide glucose range (38–520 mg/dl, 2.1–28.9 mmol/liter) using fingertip blood samples from 139 subjects. Reference plasma glucose values were obtained using the YSI 2300 STAT Plus Glucose & Lactate Analyzer (YSI Inc., Yellow Springs, OH). All studies were designed in accordance with requirements published by the International Organization for Standardization (ISO 15197). Results: Precision testing (within-run and total) with both systems produced coefficients of variation (CVs) of <5% for all sample types and glucose levels. Within-run precision testing with blood showed CVs of <3.1% and <4.7% for the OneTouch Vita and OneTouch Ultra Systems respectively. Total precision with control samples gave CVs of <3.0% and <3.6% for the two systems. Consensus error grid analysis showed equivalent clinical accuracy with 98.4% (821/834) and 98.2% (273/278) of results within zone A. Both systems met the ISO acceptability requirements for system accuracy. Conclusion: The OneTouch Vita System provides a simple no-code testing process with performance comparable to the OneTouch Ultra and OneTouch Ultra2 Systems.
FreeStyle Mini™ Blood Glucose Results Are Accurate and Suitable for Use in Glycemic Clamp Protocols
Authors: Cohen Ohad , Shaklai Sigal , Gabis Ephraim , Pani Michael A. 
Published: January 2014

Objective: We assessed the accuracy of the FreeStyle Mini™ (FSM) meter for use in glycemic clamp and meal protocols in comparison with the HemoCue Glucose 201 DM Analyzer (HemoCue) and the YSI 2300 STAT Glucose Oxidase Analyzer (YSI). Methods: Seven volunteers with type 2 diabetes mellitus, 35–69 years old, underwent a frequently sampled meal test and a graded hyperglycemic test, on two separate days, with one of the volunteers undergoing each test twice. Samples for glucose measurements were obtained from arterialized venous blood. A total of 420 samples (with glucose levels ranging from 63 to 388 mg/dl) were available for comparison. On average, 10 measurements were available for every 5 mg/dl increment in glucose level in the range of 130–310 mg/dl. Blood glucose measurements were done on each sample with the FSM, HemoCue, and YSI. Results: FreeStyle Mini blood glucose values correlated closely with the YSI readings. Of the FSM measurements, 99.0% were within the Clarke error grid zone A; 51.3%, 84.7%, and 96.2% of the FSM readings were within 5%, 10% and 15% of the YSI values, respectively. The FSM was significantly more accurate than the HemoCue (84.7% vs 76.6% of results within 10% of the YSI results; p = .0038). The mean average relative difference of the FSM (5.8%) was also significantly lower than that of the HemoCue (6.8%; p = .0013) Conclusions: The FSM provides accurate results and constitutes a suitable alternative for bedside blood glucose measurements in experimental procedures, helping to reduce sample size, turnaround time, and cost.
Consensus Report of the Coalition for Clinical Research—Self-Monitoring of Blood Glucose
Authors: Klonoff David C. , Bergenstal Richard , Blonde Lawrence , Boren Suzanne Austin , Church Timothy S. , Gaffaney Jenifer , Jovanovič Lois , Kendall David M. , Kollman Craig , Kovatchev Boris P. , Leippert Claudia , Owens David R. , Polonsky William H. , Reach Gérard , Renard Eric , Riddell Michael C. , Rubin Richard R. , Schnell Oliver , Siminiero Linda M. , Vigersky Robert A. , Wilson Darrell M. , Wollitzer Alison Okada 
Published: January 2014

The Coalition for Clinical Research—Self-Monitoring of Blood Glucose Scientific Board, a group of nine academic clinicians and scientists from the United States and Europe, convened in San Francisco, California, on June 11–12, 2008, to discuss the appropriate uses of self-monitoring of blood glucose (SMBG) and the measures necessary to accurately assess the potential benefit of this practice in noninsulin-treated type 2 diabetes mellitus (T2DM). Thirteen consultants from the United States, Europe, and Canada from academia, practice, and government also participated and contributed based on their fields of expertise. These experts represent a range of disciplines that include adult endocrinology, pediatric endocrinology, health education, mathematics, statistics, psychology, nutrition, exercise physiology, and nursing. This coalition was organized by Diabetes Technology Management, Inc. Among the participants, there was consensus that: protocols assessing the performance of SMBG in noninsulin treated T2DM must provide the SMBG intervention subjects with blood glucose (BG) goals and instructions on how to respond to BG data in randomized controlled trials (RCTs); intervention subjects in clinical trials of SMBG-driven interventions must aggressively titrate their therapeutic responses or lifestyle changes in response to hyperglycemia; control subjects in clinical trials of SMBG must be isolated from SMBG-driven interventions and not be contaminated by physician experience with study subjects receiving a SMBG intervention; the best endpoints to measure in a clinical trial of SMBG in T2DM include delta Hemoglobin A1c levels, hyperglycemic events, hypoglycemic events, time to titrate noninsulin therapy to a maximum necessary dosage, and quality of life indices; either individual randomization or cluster randomization may be appropriate methods for separating control subjects from SMBG intervention subjects, provided that precautions are taken to avoid bias and that the sample size is adequate; treatment algorithms for assessing SMBG in T2DM may include a dietary, exercise, and/or medication intervention, which are all titratable according to the SMBG values; the medical literature contains very little information about the performance of SMBG in T2DM from RCTs in which treatment algorithms were used for dysglycemic values; and research on the performance of SMBG in T2DM based on sound scientific principles and clinical practices is needed at this time.
Pharmacokinetics and Pharmacodynamics of Intranasal Insulin Spray (Nasulin™) Administered to Healthy Male Volunteers: Influence of the Nasal Cycle
Authors: Leary Andrew C. , Dowling Muiris , Cussen Kathleen , O'Brien Jackie , Stote Robert M. 
Published: January 2014

Background: The pharmacokinetics and pharmacodynamics of a Bentley Pharmaceuticals proprietary intranasal (IN) insulin formulation (Nasulin™) were studied in healthy volunteers. Methods: Thirteen fasting healthy male volunteers received five doses of medication (one dose of 4 international units [IU] subcutaneous (SC) regular insulin and four doses of 25 IU IN insulin) at least 48 h apart. Serum insulin, serum C-peptide, and plasma glucose were measured in the 4 h after dosing. Profiles were compared for IN insulin spray following administration into the dominant nostril (more open at time of dosing) and into the nondominant nostril (less open at time of dosing). Results: The formulation was generally well tolerated. A rise in serum insulin levels accompanied by a decrease in plasma glucose was seen following all doses. For IN doses, peak insulin levels were generally attained in 10–20 min and remained elevated for approximately 40–50 min; the resultant effect on glucose peaked at 40 min and waned approximately 2 h postdosing. As reported in other studies, the interindividual response to insulin was variable. The comparative absorption of IN insulin relative to SC insulin was 12.0% (dominant nostril) or 15.4% (nondominant nostril) over 2 h. This increased somewhat if sneezers and volunteers with moderately blocked nostrils were removed from the analysis. Conclusions: This IN formulation was generally well tolerated and relatively well absorbed. While both insulin data (maximal plasma concentration and area under the plasma concentration time curve) and glucose data (% fall) support a trend toward better absorption from the nondominant nostril, this did not reach statistical significance. Nasulin can be administered without reference to the nasal cycle.
The Median is Not the Only Message: A Clinician's Perspective on Mathematical Analysis of Glycemic Variability and Modeling in Diabetes Mellitus
Authors: McCall Anthony L. , Kovatchev Boris P. 
Published: January 2014

Hemoglobin A1c (HbA1c), a long-term, integrated average of tissue exposure to hyperglycemia, is the best reflection of average glucose concentrations and the best proven predictor of microvascular complications of diabetes mellitus. However, HbA1c fails to capture glycemic variability and the risks associated with extremes of hypoglycemia and hyperglycemia. These risks are the primary barrier to achieving the level of average glucose control that will minimize both the microvascular and the long-term macrovascular complications of type 1 diabetes. High blood glucose levels largely due to prandial excursions produce oxidative and inflammatory stress with potential acceleration of preexisting atherosclerosis and increased cardiovascular risk. Moreover, some temporal aspects of glycemic variation, including the rates of rise and fall of glucose, are associated with adverse cognitive and mood symptoms in those with diabetes. Methods to quantify the risk of glycemic extremes, both high and low, and the variability including its temporal aspects are now more precise than ever. These important endpoints should be included for use in clinical trials as useful metrics and recognized by regulatory agencies, which has not been the case in the past. Precise evaluation of glycemic variability and its attendant risks are essential in the design of optimal therapies; for these reasons, inclusion of these metrics and the pulsatile hormone patterns in mathematical models may be essential. For the clinician, the incursion of mathematical models that simulate normal and pathophysiological mechanisms of glycemic control is a reality and should be also gradually incorporated into clinical practice.