bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive Monitoring for Improved Management of Glucose Levels
Authors: Reifman Jaques , Rajaraman Srinivasan , Gribok Andrei , Ward W. Kenneth
Published: January 2014

Background:

Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems.

Methods:

We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period.

Results:

With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B.

Conclusions:

This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development.
Altered Disease Course after Initiation of Self-Monitoring of Blood Glucose in Noninsulin-Treated Type 2 Diabetes (ROSSO 3)
Authors: Kolb Hubert , Schneider Berthold , Heinemann Lutz , Lodwig Volker , Scherbaum Werner A. , Martin Stephan
Published: January 2014

Background:

Patients with noninsulin-treated type 2 diabetes were documented from diagnosis to determine whether patients taking up self-monitoring of blood glucose (SMBG) are distinct by baseline characteristics, exhibit a different natural disease course, and are treated differently.

Methods:

The German multicenter, retrospective cohort study (ROSSO) followed 3268 persons from diagnosis of type 2 diabetes for a mean of 6.5 years. During follow-up, 1912 persons received oral antidiabetic agents (OAD) for at least 1 year, but no insulin. Data were retrieved from patient files of randomly contacted primary care practices.

Results:

During follow-up, 742 patients (38.8%) began with SMBG prior to an end point. Initiation of SMBG was followed by improved glycemic control. Patients in the SMBG cohort were treated more often by an internist, younger by a mean of 3 years, and more often male (p < 0.001, each). A higher percentage of persons in the SMBG cohort were treated with metformin (74.7% vs 65.0%, p < 0.001) or changed OAD therapy (66.3% of patients vs 48.3% of patients, p < 0.001). SMBG was not accompanied by more comedication. In the SMBG cohort, 68 persons had a clinical end point (myocardial infarction, stroke, foot amputation, blindness, hemodialysis, or all-cause mortality) (9.2%) compared to 155 persons (13.2%) in the cohort without SMBG (p = 0.04 after multivariate adjustments).

Conclusion:

This first large documentation of OAD-treated persons from diagnosis for 6.5 years indicates that the use of SMBG is associated with younger age at diagnosis, a higher prescription rate of metformin, more frequent changes of oral therapy, and a lower risk of a clinical end point.
Metabonomics in Diabetes Research
Authors: Faber Johan H. , Malmodin Daniel , Toft Henrik , Maher Anthony D. , Crockford Derek , Holmes Elaine , Nicholson Jeremy K. , Dumas Marc E. , Baunsgaard Dorrit
Published: January 2014

Metabonomics has been defined as “quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.
Intense Exercise in Type 1 Diabetes: Exploring the Role of Continuous Glucose Monitoring
Authors: Chassin Ludovic Jean , Wilinska Malgorzata E. , Hovorka Roman
Published: January 2014

Development of the external artificial pancreas (AP) is anticipated to be incremental, starting with simple and progressing to more complex applications incorporating exercise periods of various duration and intensity. Most studies investigating the effect of exercise on glucose excursions in subjects with type 1 diabetes either explored moderate exercise, which exerts different effects compared to intense exercise, or did not adopt continuous glucose monitoring combined with frequent plasma glucose measurements. Such studies could provide vital information. Performance of continuous glucose monitors during intense exercise could be evaluated to a greater extent. Frequently sampled blood glucose would facilitate better understanding of the relationship between intense exercise and metabolic processes, providing helpful information to patients with type 1 diabetes, clinicians, and researchers involved in the development of the AP.
Endothelial Function and Arterial Stiffness in Uncomplicated Type 1 Diabetes and Healthy Controls and the Impact of Insulin on These Parameters during an Euglycemic Clamp
Authors: Wilhelm Birgit , Weber Matthias M. , Kreisselmeier Hans Peter , Kugler Matthias , Ries Claudius , Pfützner Andreas , Kann Peter H. , Forst Thomas
Published: January 2014

Background:

In addition to its role in glucose metabolism, insulin has shown to exert numerous vascular effects, and an impaired vascular function of insulin is assumed to be a major contributor in the development of vascular complications. 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 investigate the effect of insulin on arterial stiffness and parameters of endothelial function in patients with type 1 diabetes and healthy control subjects.

Methods:

Fourteen patients with type 1 diabetes (six male, eight female) with a mean age of 36.6 ± 11.8 years and 14 healthy subjects (seven male, seven female) with a mean age of 27.3 ± 5.5 years were randomized to an euglygemic clamp with either a low (0.25 mU/kg/min) or a high (1.0 mU/kg/min) insulin dose on two different days. The mean HbA1c in the diabetic subjects was 7.3 ± 0.7%. In these subjects, arterial stiffness was measured by pulse wave analysis (SphygmoCor, AtCor Medical, Australia). AP was calculated as the difference between the second and the first systolic shoulders of the central pressure wave curve, and the Aix was expressed as the percentage of AP from total pulse pressure. As parameters of endothelial function, cyclic guanosine monophosphate, nitrotyrosine, and asymmetric dimethylarginine were determined at baseline and after 120 minutes.

Results:

Patients with type 1 diabetes showed increased values for AP with 3.5 ± 3.1 mm Hg and Aix with 12.5 ± 12.5% compared to healthy controls with −0.7 ± 2.6 mm Hg for AP and −4.2 ± 10.6% for Aix. This difference was statistically significant (p < 0.01). During the euglycemic clamp, insulin improved, but did not normalize the increased values for AP and Aix in patients with type 1 diabetes. Concerning parameters of endothelial function, patients with type 1 diabetes showed statistically significant increased values for nitrotyrosine compared to healthy controls at baseline [low insulin: Diabetes mellitus (DM) 1993.12 ± 1330.85 nmol/liter vs healthy controls 803.7 ± 726.91; high insulin DM: 2208.02 ± 1736.57 nmol/liter vs healthy controls: 750.83 ± 426.03 nmol/liter] (p < 0.05).

Conclusion:

Patients with type 1 diabetes mellitus revealed an increased arterial stiffness measured as augmentation and augmentation index and increased nitrotyrosine levels as a marker of oxidative stress compared to healthy control subjects at baseline. Application of insulin improves the arterial elastic properties, but was not able to normalize the vascular function in patients with type 1 diabetes.
Type 2 Diabetes and Alzheimer's Disease: From Common Pathologies to Potential New Therapeutics
Authors: Levy Daniel E.
Published: January 2014

Type 2 diabetes constitutes a major health risk in the United States, with over 20% of individuals over the age of 60 currently suffering from this disease and an additional 1.5 million new cases diagnosed in patients over 20 years of age in 2005. Similarly, Alzheimer's disease represents a major risk to the aging population, with recent statistics estimating up to 16 million people diagnosed by 2050 in the United States alone. As type 2 diabetes and Alzheimer's disease are now recognized as members of the broader class of amyloid diseases and because the pathologies of disease progression between these indications is similar, common strategies focused on the design of next-generation therapeutics can be envisioned and are discussed herein.
Reconstruction of Glucose in Plasma from Interstitial Fluid Continuous Glucose Monitoring Data: Role of Sensor Calibration
Authors: Facchinetti Andrea , Sparacino Giovanni , Cobelli Claudio
Published: January 2014

Background:

Continuous glucose monitoring (CGM) sensors measure glucose concentration in the interstitial fluid (ISF). Equilibration between plasma and ISF glucose is not instantaneous. Therefore, ISF and plasma glucose concentrations exhibit different dynamic patterns, particularly during rapid changes. The purpose of this work was to investigate how well plasma glucose can be reconstructed from ISF CGM data.

Methods:

Six diabetic volunteers were monitored for 2 days using the TheraSense FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA), a minimally invasive device that, on the basis of an initial calibration procedure (hereafter referred to as standard calibration), returns ISF glucose concentration. Simultaneously, plasma glucose concentration was also measured every 15 minutes. First we identified, in each subject, the linear time-invariant (LTI) two-compartment model of plasma-interstitium kinetics. Then, a nonparametric regularization deconvolution method was used to reconstruct plasma from ISF glucose.

Results:

Deconvoluted profiles were always closer to plasma glucose than ISF ones. However, the quality of the reconstruction is unsatisfactory. Some visible discrepancies between average plasma and ISF time series suggest problems either in the applicability of the LTI model of plasma-interstitium kinetics to normal life conditions or in the standard calibration with which ISF glucose is determined from the sensor internal readings. Assuming that the LTI model of plasma-interstitium kinetics is correct, we focused on the influence of calibration and we employed a recently proposed method to recalibrate ISF data.

Conclusions:

After the recalibration step, the relative error in reconstructing plasma glucose was reduced significantly. Results also demonstrate that further margins of improvement of plasma glucose reconstruction are possible by developing more sophisticated recalibration procedures.
Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis
Authors: Zanderigo Francesca , Sparacino Giovanni , Kovatchev Boris , Cobelli Claudio
Published: January 2014

Aim:

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data.

Methods:

We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference.

Results:

Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges.

Conclusions:

For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.
Accuracy Requirements for a Hypoglycemia Detector: An Analytical Model to Evaluate the Effects of Bias, Precision, and Rate of Glucose Change
Authors: Noujaim Sharbel E. , Horwitz David , Sharma Manoj , Marhoul Joseph
Published: January 2014

Background:

There has been considerable debate on what constitutes a good hypoglycemia (Hypo) detector and what is the accuracy required from the continuous monitoring sensor to meet the requirements of such a detector. The performance of most continuous monitoring sensors today is characterized by the mean absolute relative difference (MARD), whereas Hypo detectors are characterized by the number of false positive and false negative alarms, which are more relevant to the performance of a Hypo detector. This article shows that the overall accuracy of the system and not just the sensor plays a key role.

Methods:

A mathematical model has been developed to investigate the relationship between the accuracy of the continuous monitoring system as described by the MARD, and the number of false negatives and false positives as a function of blood glucose rate change is established. A simulation method with N = 10,000 patients is used in developing the model and generating the results.

Results:

Based on simulation for different scenarios for rate of change (0.5, 1.0, and 5.0 mg/dl per minute), sampling rate (from 1, 2.5, 5, and 10 minutes), and MARD (5, 7.5, 10, 12.5, and 15%), the false positive and false negative ratios are computed. The following key results are from these computations. For a given glucose rate of change, there is an optimum sampling time. The optimum sampling time as defined in the critical sampling rate section gives the best combination of low false positives and low false negatives. There is a strong correlation between MARD and false positives and false negatives. For false positives of <10% and false negatives of <5%, a MARD of <7.5% is needed.

Conclusions:

Based on the model, assumptions in the model, and the simulation on N = 10,000 patients for different scenarios for rate of glucose change, sampling rate, and MARD, it is concluded that the false negative and false positive ratio will vary depending on the alarm Hypo threshold set by the patient and the MARD value. Also, to achieve a false negative ratio <5% and a false positive ratio <10% would require continuous glucose monitoring to have an MARD ≤7.5%.
Continuous Glucose Sensors: Continuing Questions about Clinical Accuracy
Authors: Clarke William L. , Kovatchev Boris
Published: January 2014

Continuous glucose sensors (CGS) offer the potential to greatly change the lives of people with diabetes. Even though two of these systems (Guardian RT, Medtronic, Northridge, CA, and DexCom STS, DexCom, San Diego, CA) have been approved by the Food and Drug Administration for use as adjuncts to self-blood glucose monitoring (SBGM), questions remain concerning the accuracy of these devices. When considering accuracy, two distinct approaches should be emphasized: (1) numerical and (2) clinical. Because CGS data are a process in time, each of these two approaches includes two subtypes of accuracy: point and rate. Conventional statistics such as correlation coefficients, mean and median relative absolute differences, and International Standards Organization criteria are measures of numerical point accuracy. A new measure, the R deviation, is introduced to quantify numerical rate accuracy. Error-grid analysis (Clarke EGA) measures clinical point accuracy. The only measure of both clinical point accuracy and rate accuracy is continuous glucose error-grid analysis. This analysis is a combination of two components, P-EGA measuring point accuracy and R-EGA measuring rate accuracy, which are designed to assess the information that distinguishes continuous glucose measurements from intermittent SBGM determinations. Further, a better understanding of the source of the error associated with time lag and its effect on CGS readings may improve sensor output. Finally, the reliability of the CGS sensors, in terms of initial calibration and long-term application, needs to be assessed carefully if current CGS systems are to be used as hypoglycemia monitors or incorporated in the future design of closed loop (artificial pancreas) systems.
Self-Measurement of Blood Glucose in Patients with Type 2 Diabetes: A Health Economic Assessment
Authors: Weber Christian , Neeser Kurt , Schneider Berthold , Lodwig Volker
Published: January 2014

Background:

The clinical role and the potential benefit of self-measurement of blood glucose (SMBG) for patients with type 2 diabetes are still under discussion. Even less information is available on the cost-effectiveness of performing SMBG by this patient group. The goal of this study was to establish cost-effectiveness ratios of performing SMBG by patients afflicted by this disease.

Methods:

We assessed the benefit and cost-effectiveness of SMBG in type 2 diabetes from a third-party payer perspective based on results of both a large epidemiologic cohort study reflecting the reality of care, and a Markov model calculation.

Results:

Analysis of cohort study data revealed that total costs cumulated over the observation period of 8 years were lower in the SMBG group than in the non-SMBG group according to savings of € 1'714 [oral antidiabetic drugs (OAD) only] and € 13'815 (OAD + insulin) per patient. Several scenarios were considered in the model-based calculation. The cost-effectiveness ratio varied from € 20'768/life year gained to domination of SMBG use compared to nonusers in OAD treated patients and from € 59'057/life year gained to domination of SMBG use compared to nonusers in OAD + insulin treated patients.

Conclusion:

Results indicate that SMBG in type 2 diabetes offers an excellent opportunity to get a high investment-outcome ratio in the treatment of this pandemic disease.
Numerical Simulation of the Effect of Rate of Change of Glucose on Measurement Error of Continuous Glucose Monitors
Authors: Taub Marc B. , Peyser Thomas A. , Rosenquist J. Erik
Published: January 2014

Background:

A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator® Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg·dl−1·min−1 (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg·dl−1·min−1 (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone.

Method:

Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy.

Results:

The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose.

Conclusions:

The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.
Continuous Glucose Profiles in Healthy Subjects under Everyday Life Conditions and after Different Meals
Authors: Freckmann Guido , Hagenlocher Sven , Baumstark Annette , Jendrike Nina , Gillen Ralph C. , Rössner Katja , Haug Cornelia
Published: January 2014

Background:

This study investigated continuous glucose profiles in nondiabetic subjects.

Methods:

Continuous interstitial glucose measurement was performed under everyday life conditions (2 days) and after ingestion of four meals with standardized carbohydrate content (50 grams), but with different types of carbohydrates and variable protein and fat content. Twenty-four healthy volunteers (12 female, 12 male, age 27.1 ± 3.6 years) participated in the study. Each subject wore two microdialysis devices (SCGM1, Roche Diagnostics) simultaneously.

Results:

The mean 24-hour interstitial glucose concentration under everyday life conditions was 89.3 ± 6.2 mg/dl (mean ± SD, n = 21), and mean interstitial glucose concentrations at daytime and during the night were 93.0 ± 7.0 and 81.8 ± 6.3 mg/dl, respectively. The highest postprandial glucose concentrations were observed after breakfast: 132.3 ± 16.7 mg/dl (range 101–168 mg/dl); peak concentrations after lunch and dinner were 118.2 ± 13.4 and 123.0 ± 16.9 mg/dl, respectively. Mean time to peak glucose concentration was between 46 and 50 minutes. After ingestion of standardized meals with fast absorption characteristics, peak interstitial glucose concentrations were 133.2 ± 14.4 and 137.2 ± 21.1 mg/dl, respectively. Meals with a higher fiber, protein, and fat content induced a smaller increase and a slower decrease of postprandial glucose concentrations with peak values of 99.2 ± 10.5 and 122.1 ± 20.4 mg/dl, respectively.

Conclusions:

This study provided continuous glucose profiles in nondiabetic subjects and demonstrated that differences in meal composition are reflected in postprandial interstitial glucose concentrations. Regarding the increasing application of continuous glucose monitoring in diabetic patients, these data suggest that detailed information about the ingested meals is important for adequate interpretation of postprandial glucose profiles.
Going Mobile with a Multiaccess Service for the Management of Diabetic Patients
Authors: Lanzola Giordano , Capozzi Davide , D'Annunzio Giuseppe , Ferrari Pietro , Bellazzi Riccardo , Larizza Cristiana
Published: January 2014

Background:

Diabetes mellitus is one of the chronic diseases exploiting the largest number of telemedicine systems. Our research group has been involved since 1996 in two projects funded by the European Union proposing innovative architectures and services according to the best current medical practices and advances in the information technology area.

Method:

We propose an enhanced architecture for telemedicine giving rise to a multitier application. The lower tier is represented by a mobile phone hosting the patient unit able to acquire data and provide first-level advice to the patient. The patient unit also facilitates interaction with the health care center, representing the higher tier, by automatically uploading data and receiving back any therapeutic plan supplied by the physician. On the patient's side the mobile phone exploits Bluetooth technology and therefore acts as a hub for a wireless network, possibly including several devices in addition to the glucometer.

Results:

A new system architecture based on mobile technology is being used to implement several prototypes for assessing its functionality. A subsequent effort will be undertaken to exploit the new system within a pilot study for the follow-up of patients cared at a major hospital located in northern Italy.

Conclusion:

We expect that the new architecture will enhance the interaction between patient and caring physician, simplifying and improving metabolic control. In addition to sending glycemic data to the caring center, we also plan to automatically download the therapeutic protocols provided by the physician to the insulin pump and collect data from multiple sensors.
Recent Advances in Continuous Glucose Monitoring: Biocompatibility of Glucose Sensors for Implantation in Subcutis
Authors: Kvist Peter H. , Jensen Henrik E.
Published: January 2014

Tight glycemic control slows or prevents the development of short- and long-term complications of diabetes mellitus. Continuous glucose measurements provide improved glycemic control and potentially prevent these diabetic complications. Glucose sensors, especially implantable devices, offer an alternative to classical self-monitored blood glucose levels and have shown promising glucose-sensing properties. However, the ultimate goal of implementing the glucose sensor as the glucose-sensing part of a closed loop system (artificial pancreas) is still years ahead because of malfunctions of the implanted sensor. The malfunction is partly a consequence of the subcutaneous inflammatory reaction caused by the implanted sensor. In order to improve sensor measurements and thereby close the loop, it is crucial to understand what happens at the tissue-sensor interface.
Metabolic Syndrome in Insects Triggered by Gut Microbes
Authors: Schilder Rudolf J. , Marden James H.
Published: January 2014

Emerging research suggests that intestinal microbiome composition is an important factor in the development of obesity. However, little is known about the mechanistic details of this relationship. A recent insect study demonstrated for the first time that metabolic syndrome and symptoms such as obesity and insulin resistance are not restricted to mammals and can be induced by means of a protozoan intestinal infection. This article describes the findings of this study and integrates them with findings from studies relating obesity to the gut microbiota of mammals.
Analysis of Algorithms for Intensive Care Unit Blood Glucose Control
Authors: Bequette B. Wayne
Published: January 2014

Intensive care unit (ICU) blood glucose control algorithms were reviewed and analyzed in the context of linear systems theory and classical feedback control algorithms. Closed-loop performance was illustrated by applying the algorithms in simulation studies using an in silico model of an ICU patient. Steady-state and dynamic input-output analysis was used to provide insight about controller design and potential closed-loop performance. The proportional-integral-derivative, columnar insulin dosing (CID, Glucommander-like), and glucose regulation for intensive care patients (GRIP) algorithms were shown to have similar features and performance. The CID strategy is a time-varying proportional-only controller (no integral action), whereas the GRIP algorithm is a nonlinear controller with integral action. A minor modification to the GRIP algorithm was suggested to improve the closed-loop performance. Recommendations were made to guide control theorists on important ICU control topics worthy of further study.
Linear Quadratic Gaussian-Based Closed-Loop Control of Type 1 Diabetes
Authors: Patek Stephen D. , Breton Marc D. , Chen Yuanda , Solomon Chad , Kovatchev Boris
Published: January 2014

Background:

We investigated the applicability of linear quadratic Gaussian (LQG) methodology to the subcutaneous blood glucose regulation problem. We designed an LQG-based feedback control algorithm using linearization of a previously published metabolic model of type 1 diabetes. A key feature of the controller is a Kalman filter used to estimate metabolic states of the patient based on continuous glucose monitoring. Insulin infusion is computed from linear quadratic regulator feedback gains applied to these estimates, generally seeking to minimize squared deviations from a target glucose concentration and basal insulin rate. We evaluated in silico subject-specific LQG control and compared it to preexisting proportional-integral-derivative control.
A Cause-and-Effect-Based Mathematical Curvilinear Model That Predicts the Effects of Self-Monitoring of Blood Glucose Frequency on Hemoglobin A1c and is Suitable for Statistical Correlations
Authors: Davidson Paul C. , Bode Bruce W. , Steed R. Dennis , Hebblewhite Harry R.
Published: January 2014

Background:

Previous studies have shown an association between the frequency of self-monitored blood glucose (SMBG) and hemoglobin A1c. Randomized controlled trials (RCTs) have shown this to be a causal correlation for insulin-using patients. Several studies have used linear regression, but a straight line will descend into negative hemoglobin A1c values (an impossibility). This study developed a cause-and-effect-based nonlinear model to predict the outcome of RCTs on this subject, tested this model with clinical data, and offered this model in place of linear regression, especially for the still-debated case of noninsulin-using patients.

Method:

The model was developed from cause-and-effect principles. The clinical study utilized retrospective data from patient histories of a large endocrine practice. Data sets were obtained for five treatment regimens: continuous subcutaneous insulin infusion (CSII), subcutaneous insulin (SC), no insulin (NI), oral medication (OM), and no medication (NM). OM and NM are subgroups of NI. The model was fitted to each group using nonlinear least-squares methods. Each group was ordered by SMBG tests per day (BGpd) and was divided in half; t tests were run between the AlC's of the two halves.

Results:

Self-monitored blood glucose readings from 1255 subjects were analyzed (CSII, N = 417; SC, N = 286; NI, N = 552; OM, N = 505; NM, N = 47). The CSII, SC, NI, and OM groups showed the expected declining statistically fitted curve and a significant association of BGpd with hemoglobin A1c (P < 0.004). The NM group showed insignificant results.

Conclusions:

The nonlinear model is based on cause-and-effect principles and mathematics. It yields a prediction that RCTs will be able to reveal that higher SMBG frequency causes lower hemoglobin A1c.
Continuous Subcutaneous Glucose Monitoring Shows a Close Correlation between Mean Glucose and Time Spent in Hyperglycemia and Hemoglobin A1c
Authors: Nielsen Jannik Kruse , Gravholt Claus Hojbjerg , Djurhuus Christian Born , Brandt Derek , Becker Joern , Heinemann Lutz , Christiansen Jens Sandahl
Published: January 2014

Background:

The Diabetes Control and Complications Trial and United Kingdom Prospective Diabetes Study highlighted hemoglobin A1c (HbAlc) as the main predictor of diabetic complications. Currently, diabetes is managed by frequent capillary spot glucose measurements, but continuous monitoring systems may have the capacity of improving diabetic control. The SCGM 1 system is microdialysis based and allows for monitoring of changes in interstitial fluid glucose levels every minute. The aim of this study was to evaluate the correlation between HbAlc and short-term glucose excursions in patients with type 1 diabetes.

Material and Methods:

We investigated 91 patients with type 1 diabetes (mean ± standard deviation (SD); age 34 ± 10 years, body mass index 24.2 ± 4.1 kg/m2) with a duration of diabetes of 17 ± 11 years for 4.8 ± 0.4 days. The average HbA1c was 7.9 ± 1.4%. From the monitoring profiles we determined individual mean glucose, the SD of glucose, and the relative time spent in hyperglycemia and hypoglycemia calculated as the area under the curve (AUC) with arbitrary cutoffs of 180 and 70 mg/dl, respectively.

Results:

Mean glucose, SD glucose, and hyperglycemic and hypoglycemic AUC all correlated with HbA1c, but with decreasing statistical power. In multiple linear regression analysis, mean glucose was the sole independent variable ( r = 0.626, p < 0.0001). A close correlation between HbA1c and various measures of short-term hyperglycemic values was observed. There was a close correlation between mean glucose and SD glucose, pointing toward increased variability with increasing mean glucose.

Conclusion:

Mean glucose generated after short-term continuous monitoring is the main predictor of HbA1c and reveals increased lability of glucose with increasing mean glucose and HbA1c.
Analysis of Office-Based Glycohemoglobin Measurement
Authors: Higgins Trefor
Published: January 2014

Hemoglobin A1c (HbAlc) measurements are important for monitoring glycemic control in patients with diabetes. The positive impact of point-of-care HbAlc measurements on patient management is described in the literature. The general analytical requirements for HbAlc testing are described. The analytical evaluation of a new point-of-care HbAlc analyzer, suitable for a physician's office, against these requirements is described.
Veterans Affairs Research on Health Information Technologies for Diabetes Self-Management Support
Authors: Piette John D. , Kerr Eve , Richardson Caroline , Heisler Michele
Published: January 2014

Background:

Like many patients with diabetes, Department of Veterans Affairs (VA) patients frequently fall short of self-management goals and experience multiple barriers to self-care. Health information technologies (HITs) may provide the tools that patients need to manage their illness under the direction of their primary care team.

Methods:

We describe several ongoing projects focused on HIT resources for self-management in VA. VA researchers are developing HITs that seek to bolster a variety of potential avenues for self-management support, including patients' relationships with other patients, connections with their informal care networks, and communication with their health care teams.

Results:

Veterans Affairs HIT research projects are developing services that can address the needs of patients with multiple challenges to disease self-care, including multimorbidity, health literacy deficits, and limited treatment access. These services include patient-to-patient interactive voice response (IVR) calling systems, IVR assessments with feedback to informal caregivers, novel information supports for clinical pharmacists based on medication refill data, and enhanced pedometers.

Conclusion:

Large health care systems such as the VA can play a critical role in developing HITs for diabetes self-care. To be truly effective, these efforts should include a continuum of studies: observational research to identify barriers to self-management, developmental studies (e.g., usability testing), efficacy trials, and implementation studies to evaluate utility in real-world settings. VA HIT researchers partner with operations to promote the dissemination of efficacious services, and such relationships will be critical to move HIT innovations into practice.
Longitudinal Approaches to Evaluate Health Care Quality and Outcomes: The Veterans Health Administration Diabetes Epidemiology Cohorts
Authors: Miller Donald R. , Pogach Leonard
Published: January 2014

Objective:

The Institute of Medicine proposed recently that, while current pay for performance measures should target multiple dimensions of care, including measures of technical quality, they should transition toward longitudinal and health-outcome measures across systems of care. This article describes the development of the Diabetes Epidemiology Cohorts (DEpiC), which facilitates evaluation of intermediate “quality of care” outcomes and surveillance of adverse outcomes for veterans with diabetes served by the Veterans Health Administration (VHA) over multiple years.

Methods:

The Diabetes Epidemiology Cohorts is a longitudinal research database containing records for all diabetes patients in the VHA since 1998. It is constructed using data from a variety of national computerized data files in the VHA (including medical encounters, prescriptions, laboratory tests, and mortality files), Medicare claims data for VHA patients, and large patient surveys conducted by the VHA. Rigorous methodology is applied in linking and processing data into longitudinal patient records to assure data quality.

Results:

Validity is demonstrated in the construction of the DEpiC. Adjusted comparisons of disease prevalence with general population estimates are made. Further analyses of intermediate outcomes of care demonstrate the utility of the database. In the first example, using growth curve models, we demonstrated that hemoglobin A1c trends exhibit marked seasonality and that serial cross-sectional outcomes overestimate the improvement in population A1c levels compared to longitudinal cohort evaluation. In the second example, the use of individual level data enabled the mapping of regional performance in amputation prevention into four quadrants using calculated observed to expected major and minor amputation rates. Simultaneous evaluation of outliers in all categories of amputation may improve the oversight of foot care surveillance programs.

Conclusions:

The use of linked, patient level longitudinal data resolves confounding case mix issues inherent in the use of serial cross-sectional data. Policy makers should be aware of the limitations of cross-sectional data and should make use of longitudinal patient databases to evaluate clinical outcomes.
Type 2 Diabetes Phenotype and Progression is Significantly Different if Diagnosed before versus after 65 Years of Age
Authors: Kolb Hubert , Schneider Berthold , Heinemann Lutz , Heise Tim , Lodwig Volker , Tshiananga Jacques K. Tshiang , Weber Christian , Scherbaum Werner A. , Martin Stephan
Published: January 2014

Background:

The incidence of type 2 diabetes is increasing disproportionately in individuals <65 years of age. It is not known whether disease characteristics in these younger patients are similar to “classic” late-onset type 2 diabetes.

Methods:

In the epidemiological cohort study entitled “Retrolective Study: Self-Monitoring of Blood Glucose and Outcome in Patients with Type 2 Diabetes,” a total of 3268 patients from randomly contacted primary care practices were documented during a mean follow-up period of 6.5 years. All newly diagnosed patients of these practices were included.

Results:

At diagnosis, 64.2% of the patients were aged ≤65 years. Thereof, 57.2% were male, whereas in the age group >65 years only 35.0% were male ( p < 0.001). The younger group exhibited more severe metabolic deterioration at diagnosis and in the following years than the older group. Conversely, the older group presented at diagnosis with a higher prevalence of cardiovascular risk factors. Self-monitoring of blood glucose (SMBG) was more prominent in the younger group. In both age groups, the use of SMBG was associated with a significantly lower risk ( p = 0.003) of a combined end point (severe diabetic complication or all-cause mortality).

Conclusions:

There are considerable differences in disease characteristics between people diagnosed with type 2 diabetes during 45–65 years of age versus diagnosis at a later age. Type 2 diabetes diagnosed before the age of 65 years disproportionately affected men and exhibited a more severe disease course, but was characterized by significantly less cardiovascular risk factors in comparison to type 2 diabetes diagnosed at a later age. The use of SMBG was associated with a better clinical outcome in both age groups.
The Next Generation of Artificial Pancreas Control Algorithms
Authors: Teixeira Rodrigo E. , Malin Stephen
Published: January 2014

Creating a wearable artificial pancreas (AP) by closing the loop between a glucose sensor and an insulin infusion pump has the potential to significantly impact the complications associated with and improve the quality of life of diabetic individuals. Despite recent progress on glucose sensor and insulin infusion technologies, control algorithms built on the simple glucose value efferent and insulin dose afferent model are not efficient and reliable. Based on glucose regulatory mechanisms known to date, their impairment in the diabetic state, and fundamental principles of control theory, some corrections to the present course of research are proposed to facilitate the removal of this barrier. A greater emphasis on model predictive controllers or controllers that exploit a mathematical representation, or model, of the patient's own physiology is proposed. Whole-body physiologically based pharmacokinetics-pharmacodynamics-type models hold the best odds for enabling a successful closed-loop AP. However, two major improvements to the diabetes modeling state of the art are required to make them practical for daily care: integrating hypothalamus—pituitary–adrenal axis and gastrointestinal tract submodels. Although there are simple representations of these in current existence, large concerted efforts between experimentalists and modelers will be required to enhance their accuracy. Finally, changes in hardware that complements controller performance are suggested. For instance, the development of dual control inputs of insulin and glucagon could relax tolerances on controller accuracy.
Can Wireless Technology Enable New Diabetes Management Tools?
Authors: Hedtke Paul A.
Published: January 2014

Mobile computing and communications technology embodied in the modern cell phone device can be employed to improve the lives of diabetes patients by giving them better tools for self-management. Several companies are working on the development of diabetes management tools that leverage the ubiquitous cell phone to bring self-management tools to the hand of the diabetes patient. Integration of blood glucose monitoring (BGM) technology with the cell phone platform adds a level of convenience for the person with diabetes, but, more importantly, allows BGM data to be automatically captured, logged, and processed in near real time in order to provide the diabetes patient with assistance in managing their blood glucose levels. Other automatic measurements can estimate physical activity, and information regarding medication events and food intake can be captured and analyzed in order to provide the diabetes patient with continual assistance in managing their therapy and behaviors in order to improve glycemic control. The path to realization of such solutions is not, however, without obstacles.
Systems Biology: The Case for a Systems Science Approach to Diabetes
Authors: Petrasek Danny
Published: January 2014

The unprecedented accumulation of biological data in recent decades has underscored the need to organize and integrate the massive collection of information. In addition, there is rising agreement among biologists that a complete understanding of a single cell will not lead directly to a complete understanding of a system of cells. The success of a systems science approach in engineering and physics may be of great value in the evolution of biological science. This article reviews some examples that suggest the importance of a systems biology approach and, in addition, advance one specific systems science principle, the conservation of uncertainty, which may give insight into the emergent behavior of numerous biological and physiological phenomena.
Diabetes: Medical vs Surgical Disease?
Authors: Gurel Ogan
Published: January 2014

The distinction between the medical and the surgical approach to disease has been a cornerstone of medical practice, and indeed with respect to the business and technology of medicine. It is common knowledge that diabetes is a medical disease—namely that drug therapy, whether it be via insulin or other medications, is the primary approach to therapy. This article argues that a reevaluation of the generalized (e.g., medication-based) approach to systemic blood sugar control may be in order. A consideration of the growing importance of interventional, device-based, or other surgical approaches to the primary management of diabetes has enormous implications for clinical practice as well as, of course, the business and technology of diabetes care.
Computerized Learning Technologies for Diabetes: A Systematic Review
Authors: Boren Suzanne Austin , Gunlock Teira L. , Peeples Malinda M. , Krishna Santosh
Published: January 2014

Background:

The objective of this study was to evaluate computerized learning technology interventions that can empower patients in the self-management of diabetes and support diabetes education over a distance.

Methods:

We searched Medline (1966–2006), CINAHL (1982–2006), and the Cochrane Central Register of Controlled Trials (first quarter 2007) databases. We also reviewed reference lists from included studies to identify additional studies. We included 25 articles representing 21 randomized controlled trials that evaluated a computerized learning technology and measured the outcome of patient care. We extracted patient sample, intervention, educational content topics, outcome measures, and statistical significance.

Results:

Of 21 eligible trials, 18 trials (85.7%) reported significant positive outcomes. Almost 44% (43.8%) of the outcomes demonstrated significant improvements (49 of 112 outcomes).

Conclusions:

Patient self-management behaviors are important in chronic disease management, and initial evidence suggests that computerized learning technology interventions can play a significant role in the future.