bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combined Pioglitazone and Metformin Treatment Maintains the Beneficial Effect of Short-Term Insulin Infusion in Patients with Type 2 Diabetes: Results from a Pilot Study
Authors: Musholt Petra B. , Schöndorf Thomas , Pfützner Andreas , Hohberg Cloth , Kleine Iris , Fuchs Winfried , Hehenwarter Silvia , Dikta Gerhard , Kerschgens Benedikt , Forst Thomas 
Published: January 2014

Background: The aim of our study was to examine the efficacy of short-term intravenous insulin intervention followed by oral pioglitazone/metformin therapy to prevent patients from continuous insulin application. Methods: This prospective, open-label, 4-month pilot study comprised of 14 diabetes patients (5 female, 9 male; age 60 ± 2 years; body mass index 29 ± 3.2 kg/m2; hemoglobin A1c [HbAlc] 7.6 ± 1.1%) with (1) insufficient glycemic control under a dose of metformin >1700 mg/day and/or metformin plus additional oral antidiabetes drugs (OADs) and (2) appropriate residual β-cell function. Initially, an inpatient 34 h continuous intravenous insulin infusion was performed, and metformin was given (2× 850 mg/day). Insulin was stopped, and pioglitazone 30 mg/day was added at the second inpatient day. Patients were followed for four months. Efficacy parameters [change of HbA1c, fasting blood glucose [FBG], intact proinsulin, adiponectin, and high-sensitivity C-reactive protein (hsCRP)] were assessed after initial normalization of blood glucose values by intravenous insulin and at the study end point. Results: During the acute insulin intervention, FBG levels were stabilized in all study subjects. In the following OAD treatment period, five patients showed an improvement of HbA1c > 0.5% [35.7%; seven patients remained stable (50.0%), two patients were nonresponders (14.3%)]. Fasting glucose values dropped after insulin infusion (−17.7%; p < .001). This effect was maintained during the consecutive OAD treatment period (glucose +0.3%, not significant (NS); HbA1c −6.0%; p < .05). The initial decrease in fasting intact proinsulin levels was also maintained during the study (end value −41%, p < .05). Improvements in hsCRP values (postinsulin value, −15%, NS; end value −37%; p < .05) and adiponectin values (postinsulin value +15%, NS; end value +128%; p < .001) were demonstrated at end point only after continued glitazone intake. Conclusions: Our pilot study demonstrated that a beneficial effect of a short-term intravenous insulin application on glycemic control was effectively maintained by pioglitazone/metformin treatment for at least 4 months. In addition, the oral therapy significantly improved cardiovascular risk parameters.
Hypoglycemia Prediction with Subject-Specific Recursive Time-Series Models
Authors: Eren-Oruklu Meriyan , Cinar Ali , Quinn Lauretta 
Published: January 2014

Background: Avoiding hypoglycemia while keeping glucose within the narrow normoglycemic range (70–120 mg/dl) is a major challenge for patients with type 1 diabetes. Continuous glucose monitors can provide hypoglycemic alarms when the measured glucose decreases below a threshold. However, a better approach is to provide an early alarm that predicts a hypoglycemic episode before it occurs, allowing enough time for the patient to take the necessary precaution to avoid hypoglycemia. Methods: We have previously proposed subject-specific recursive models for the prediction of future glucose concentrations and evaluated their prediction performance. In this work, our objective was to evaluate this algorithm further to predict hypoglycemia and provide early hypoglycemic alarms. Three different methods were proposed for alarm decision, where (A) absolute predicted glucose values, (B) cumulative-sum (CUSUM) control chart, and (C) exponentially weighted moving-average (EWMA) control chart were used. Each method was validated using data from the Diabetes Research in Children Network, which consist of measurements from a continuous glucose sensor during an insulin-induced hypoglycemia. Reference serum glucose measurements were used to determine the sensitivity to predict hypoglycemia and the false alarm rate. Results: With the hypoglycemic threshold set to 60 mg/dl, sensitivity of 89, 87.5, and 89% and specificity of 67, 74, and 78% were reported for methods A, B, and C, respectively. Mean values for time to detection were 30 ± 5.51 (A), 25.8 ± 6.46 (B), and 27.7 ± 5.32 (C) minutes. Conclusions: Compared to the absolute value method, both CUSUM and EWMA methods behaved more conservatively before raising an alarm (reduced time to detection), which significantly decreased the false alarm rate and increased the specificity.
Hypoglycemia Alarm Enhancement Using Data Fusion
Authors: Skladnev Victor N. , Tarnavskii Stanislav , McGregor Thomas , Ghevondian Nejhdeh , Gourlay Steve , Jones Timothy W. 
Published: January 2014

Background: The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon®). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. Methods: Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of ± standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of ± SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. Results: The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. Conclusion: Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.
Alarm Characterization for Continuous Glucose Monitors Used as Adjuncts to Self-Monitoring of Blood Glucose
Authors: McGarraugh Geoffrey 
Published: January 2014

Background: Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG). Alarm evaluation in the Clinical and Laboratory Standards Institute (CLSI) guideline for CGM does not specifically address devices that employ both CGM and SMBG. In this report, an alarm evaluation method is proposed for these devices. Method: The proposed method builds on the CLSI method using data from an in-clinic study of subjects with type 1 diabetes. CGM was used to detect glycemic events, and SMBG was used to determine treatment. To optimize detection of a single glucose level, such as 70 mg/dl, a range of alarm threshold settings was evaluated. The alarm characterization provides a choice of alarm settings that trade off detection and false alarms. Detection of a range of high glucose levels was similarly evaluated. Results: Using low glucose alarms, detection of 70 mg/dl within 30 minutes increased from 64 to 97% as alarm settings increased from 70 to100 mg/dl, and alarms that did not require treatment (SMBG >85 mg/dl) increased from 18 to 52%. Using high glucose alarms, detection of 180 mg/dl within 30 minutes increased from 87 to 96% as alarm settings decreased from 180 to 165 mg/dl, and alarms that did not require treatment (SMBG <180 mg/dl) increased from 24 to 42%. Conclusion: The proposed alarm evaluation method provides information for choosing appropriate alarm thresholds and reflects the clinical utility of CGM alarms.
Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring
Authors: McGarraugh Geoffrey 
Published: January 2014

Background: Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG); all CGM alarms require SMBG confirmation before treatment. In this report, an analysis method is proposed to determine the CGM threshold alarm accuracy required to eliminate SMBG confirmation. Method: The proposed method builds on the Clinical and Laboratory Standards Institute (CLSI) guideline for evaluating CGM threshold alarms using data from an in-clinic study of subjects with type 1 diabetes. The CLSI method proposes a maximum time limit of ±30 minutes for the detection of hypo- and hyperglycemic events but does not include limits for glucose measurement accuracy. The International Standards Organization (ISO) standard for SMBG glucose measurement accuracy (ISO 15197) is ±15 mg/dl for glucose <75 mg/dl and ±20% for glucose ≥75 mg/dl. This standard was combined with the CLSI method to more completely characterize the accuracy of CGM alarms. Results: Incorporating the ISO 15197 accuracy margins, FreeStyle Navigator® CGM system alarms detected 70 mg/dl hypoglycemia within 30 minutes at a rate of 70.3%, with a false alarm rate of 11.4%. The device detected high glucose in the range of 140–300 mg/dl within 30 minutes at an average rate of 99.2%, with a false alarm rate of 2.1%. Conclusion: Self-monitoring of blood glucose confirmation is necessary for detecting and treating hypoglycemia with the FreeStyle Navigator CGM system, but at high glucose levels, SMBG confirmation adds little incremental value to CGM alarms.
Analytical and Clinical Performance of Blood Glucose Monitors
Authors: Boren Suzanne Austin , Clarke William L. 
Published: January 2014

Background: The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature. Methods: Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors. Results: Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed. Conclusions: When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.
Siphon Effects on Continuous Subcutaneous Insulin Infusion Pump Delivery Performance
Authors: Zisser Howard C. , Bevier Wendy , Dassau Eyal , Jovanovič Lois 
Published: January 2014

Background: The objective was to quantify hydrostatic effects on continuous subcutaneous insulin infusion (CSII) pumps during basal and bolus insulin delivery. Methods: We tested CSII pumps from Medtronic Diabetes (MiniMed 512 and 515), Smiths Medical (Deltec Cozmo 1700), and Insulet (OmniPod) using insulin aspart (Novolog, Novo Nordisk). Pumps were filled and primed per manufacturer's instructions. The fluid level change was measured using an inline graduated glass pipette (100 μl) when the pipette was moved in relation to the pump (80 cm Cosmo and 110 cm Medtronics) and when level. Pumps were compared during 1 and 5 U boluses and basal insulin delivery of 1.0 and 1.5 U/h. Results: Pronounced differences were seen during basal delivery in pumps using 80–100 cm tubing. For the 1 U/h rate, differences ranged from 74.5% of the expected delivery when the pumps were below the pipettes and pumping upward to 123.3% when the pumps were above the pipettes and pumping downward. For the 1.5 U/h rate, differences ranged from 86.7% to 117.0% when the pumps were below or above the pipettes, respectively. Compared to pumps with tubing, OmniPod performed with significantly less variation in insulin delivery. Conclusions: Changing position of a conventional CSII pump in relation to its tubing results in significant changes in insulin delivery. The siphon effect in the tubing may affect the accuracy of insulin delivery, especially during low basal rates. This effect has been reported when syringe pumps were moved in relation to infusion sites but has not been reported with CSII pumps.
No Higher Dose Requirements with Insulin Detemir than Glargine in Type 2 Diabetes: A Crossover, Double-Blind, and Randomized Study Using Continuous Glucose Monitoring
Authors: King Allen B. 
Published: January 2014

Background: In a previous publication we reported no difference in the 24-hour glucose response between two basal analog insulins, detemir and glargine, when taken once a day in type 2 diabetes mellitus (T2DM). We now report the dose comparison observed within this randomized, double-blind, crossover study. Method: Of 36 patients on basal insulin and other noninsulin treatments, 29 completed the study. Both insulins were given once a day at 8 pm and no food was taken between 6 pm and the following morning. The dose was titrated daily by continuous glucose monitoring (CGM) until the basal glucose (between 12 and 6 am) was <120 mg/dl but not >5% of CGM readings <70 mg/dl. Subjects were then crossed over to the other insulin and titrated similarly. Results: Glucose goals were achieved in all subjects. The mean dosage was 0.26 U/kg with very few subjects requiring >0.4 U/kg. Only 2 required an absolute dose less than 10 U/day and all others required more, some considerably higher. Of the 29 subjects, 7 required a greater, 6 a smaller, and 16 the same dose of detemir compared to glargine. Conclusions: When given once daily in T2DM and titrated using CGM to the same fasting glucose, there was no difference in the glucose response between basal insulins during the basal titration period (4–10 hours after injection) nor during the entire 24-hour period following the injection. Further, the mean dosage to achieve this glucose goal was the same with both insulins.
Aluminum Gallium Nitride (GaN)/GaN High Electron Mobility Transistor-Based Sensors for Glucose Detection in Exhaled Breath Condensate
Authors: Chu Byung Hwan , Kang Byoung Sam , Hung Sheng Chun , Chen Ke Hung , Ren Fan , Sciullo Andrew , Gila Brent P. , Pearton Stephen J. 
Published: January 2014

Background: Immobilized aluminum gallium nitride (AlGaN)/GaN high electron mobility transistors (HEMTs) have shown great potential in the areas of pH, chloride ion, and glucose detection in exhaled breath condensate (EBC). HEMT sensors can be integrated into a wireless data transmission system that allows for remote monitoring. This technology offers the possibility of using AlGaN/GaN HEMTs for extended investigations of airway pathology of detecting glucose in EBC without the need for clinical visits. Methods: HEMT structures, consisting of a 3-μm-thick undoped GaN buffer, 30-Å-thick Al03Ga07N spacer, and 220-Å-thick silicon-doped Al03Ga07N cap layer, were used for fabricating the HEMT sensors. The gate area of the pH, chloride ion, and glucose detection was immobilized with scandium oxide (Sc2O3), silver chloride (AgCl) thin film, and zinc oxide (ZnO) nanorods, respectively. Results: The Sc2O3-gated sensor could detect the pH of solutions ranging from 3 to 10 with a resolution of ∼0.1 pH. A chloride ion detection limit of 10−8 M was achievedt with a HEMT sensor immobilized with the AgCl thin film. The drain-source current of the ZnO nanorod-gated AlGaN/GaN HEMT sensor immobilized with glucose oxidase showed a rapid response of less than 5 seconds when the sensor was exposed to the target glucose in a buffer with a pH value of 7.4. The sensor could detect a wide range of concentrations from 0.5 nM to 125 μ M. Conclusion: There is great promise for using HEMT-based sensors to enhance the detection sensitivity for glucose detection in EBC. Depending on the immobilized material, HEMT-based sensors can be used for sensingt different materials. These electronic detection approaches with rapid response and good repeatability show potential for the investigation of airway pathology. The devices can also be integrated into a wireless data transmission system for remote monitoring applications. This sensor technology could use the exhaled breath condensate to measure the glucose concentration for diabetic applications.
An Intensive Insulinotherapy Mobile Phone Application Built on Artificial Intelligence Techniques
Authors: Curran Kevin , Nichols Eric , Xie Ermai , Harper Roy 
Published: January 2014

Background: Software to help control diabetes is currently an embryonic market with the main activity to date focused mainly on the development of noncomputerized solutions, such as cardboard calculators or computerized solutions that use “flat” computer models, which are applied to each person without taking into account their individual lifestyles. The development of true, mobile device-driven health applications has been hindered by the lack of tools available in the past and the sheer lack of mobile devices on the market. This has now changed, however, with the availability of pocket personal computer handsets. Method: This article describes a solution in the form of an intelligent neural network running on mobile devices, allowing people with diabetes access to it regardless of their location. Utilizing an easy to learn and use multipanel user interface, people with diabetes can run the software in real time via an easy to use graphical user interface. The neural network consists of four neurons. The first is glucose. If the user's current glucose level is within the target range, the glucose weight is then multiplied by zero. If the glucose level is high, then there will be a positive value multiplied to the weight, resulting in a positive amount of insulin to be injected. If the user's glucose level is low, then the weights will be multiplied by a negative value, resulting in a decrease in the overall insulin dose. Results: A minifeasibility trial was carried out at a local hospital under a consultant endocrinologist in Belfast. The short study ran for 2 weeks with six patients. The main objectives were to investigate the user interface, test the remote sending of data over a 3G network to a centralized server at the university, and record patient data for further proofing of the neural network. We also received useful feedback regarding the user interface and the feasibility of handing real-world patients a new mobile phone. Results of this short trial confirmed to a large degree that our approach (which also can be known as intensive insulinotherapy) has value and perhaps that our neural network approach has implications for future intelligent insulin pumps. Conclusions: Currently, there is no software available to tell people with diabetes how much insulin to inject in accordance with their lifestyle and individual inputs, which leads to adjustments in software predictions on the amount of insulin to inject. We have taken initial steps to supplement the knowledge and skills of health care professionals in controlling insulin levels on a daily basis using a mobile device for people who are less able to manage their disease, especially children and young adults.
Basal Insulin Requirements on Continuous Subcutaneous Insulin Infusion during the First 12 Months after Diagnosis of Type 1 Diabetes Mellitus
Authors: Ramchandani Neesha , Ellis Mary Kristine , Jain Shobhit , Bhandari Sonal , Anhalt Henry , Maclaren Noel K. , Ten Svetlana 
Published: January 2014

Introduction: While the endogenous first-phase insulin response has disappeared by the time of diagnosis of type 1 diabetes mellitus (T1DM), anecdotal evidence suggests that these patients can continue to have a second-phase insulin response during the first 12 months after diagnosis. We hypothesized that patients who are started on continuous subcutaneous insulin infusion (CSII) at the time of diagnosis of T1DM would have a lower basal insulin requirement than the 40–60% usually expected. Methods: We analyzed 38 patients with T1DM, age 9.9 ± 6.4 years, 71% male, who were started on CSII within the first month of diagnosis. Results: Average basal insulin requirements were 47–49% of total daily dose during the first 12 months after diagnosis and decreased from 0.30 U/kg/day at diagnosis to 0.20 U/kg/day by 12 months. Baseline percentage of basal insulin was significantly correlated with hemoglobin A1c at baseline and at six months. The percentage of basal insulin requirement at 12 months after diagnosis was significantly correlated with baseline body mass index (BMI) and current BMI. No other correlations between percentage of basal insulin requirements and any other factors were seen. Conclusion: Our data suggest that, even though some endogenous insulin production remains during the first year after diagnosis of T1DM, the distribution of basal versus total daily insulin requirements remains the same as in the general population of people with diabetes. There may be benefits to starting patients on a higher basal rate at time of diagnosis for overall glycemic control during the first six months. Further research is needed to optimize starting insulin doses to maximize their potential in preserving beta-cell function.