Journal of Diabetes Science and Technology This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

A Multicenter Performance Evaluation of a Blood Glucose Monitoring System in 21 Leading Hospitals in Spain
Conclusions: This is the largest multicenter study of Contour XT BGMS to date, and shows that this BGMS meets the ISO 15197:2013 accuracy limit criteria under local routine conditions in 21 leading Spanish hospitals. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Bedini, J. L., Wallace, J. F., Petruschke, T., Pardo, S. Tags: Original Articles Source Type: research

Performance Evaluation of Three Blood Glucose Monitoring Systems Using ISO 15197: 2013 Accuracy Criteria, Consensus and Surveillance Error Grid Analyses, and Insulin Dosing Error Modeling in a Hospital Setting
Conclusions: All BGMS fulfilled ISO 15197:2013 accuracy limit criteria and CEG criterion. However, taking together all analyses, differences in performance of potential clinical relevance may be observed. Results showed that Contour Next USB had lowest MARD values across the tested glucose range, as compared with the 2 other BGMS. CEG and SEG analyses as well as calculation of the hypothetical bolus insulin dosing error suggest a high accuracy of the Contour Next USB. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Bedini, J. L., Wallace, J. F., Pardo, S., Petruschke, T. Tags: Original Articles Source Type: research

Improving the Quality of Outpatient Diabetes Care Using an Information Management System: Results From the Observational VISION Study
Conclusions: Integration of the IMS into outpatient care facilitates significant improvements in glycemic control. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Weissmann, J., Mueller, A., Messinger, D., Parkin, C. G., Amann-Zalan, I. Tags: Original Articles Source Type: research

The Glucose Measurement Industry and Hemoglobin A1c: An Opportunity for Creative Destruction
The MyStar Extra self-monitoring blood glucose (SMBG) system provides moving estimates of the patient’s hemoglobin A1c (HbA1c). There is a treasure trove of highly accurate glucose data available from highly accurate SMBG, CGM and FGM along with highly accurate HPLC HbA1c. If Nathan’s criteria are used to select subjects whose glucoses can be correlated to the HbA1c, then algorithms can be developed for robustly transforming glucose into HbA1c. These algorithms can then be implemented in any SMBG or with the CGM and FGM software. This calculated HbA1c would even be accurate with Nathan’s excluded populati...
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Cembrowski, G. Tags: Original Articles Source Type: research

Evaluation of a Methodology for Estimating HbA1c Value by a New Glucose Meter
Conclusions: Accuracy of the eA1c feature in this clinical setting was similar to the performance in silico. The majority of subjects found this tool helpful and agreed it may motivate to manage their diabetes better. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Sieber, J., Flacke, F., Dumais, B., Peters, C. C., Mallery, E. B., Taylor, L. Tags: Original Articles Source Type: research

Glycemic Variability and Its Impact on Quality of Life in Adults With Type 1 Diabetes
Conclusions: Treatment with CSII is associated with lower glycemic variability compared to MDI. Despite this, and contrary to findings in type 2 diabetes, this study did not find an association between glycemic variability and QoL in adults with relatively well-controlled type 1 diabetes, irrespective of whether they are on MDI or CSII. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Reddy, M., Godsland, I. F., Barnard, K. D., Herrero, P., Georgiou, P., Thomson, H., Johnston, D. G., Oliver, N. S. Tags: Original Articles Source Type: research

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?
Conclusions: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Fabris, C., Patek, S. D., Breton, M. D. Tags: Original Articles Source Type: research

The Transformation of Diabetes Care Through the Use of Person-Centered Data
The health care industry is undergoing a major transformation. Despite spending more on health care than any other country, the United States has not seen a commensurate improvement in the quality of care. Chronic disease management puts the greatest burden on the health care system with estimates suggesting that 3 of 4 health care dollars are spent on managing chronic disease. Moreover, the number of older patients with chronic conditions, like diabetes, is rising as expected, which only serves to worsen the physician shortage problem we are currently experiencing, and further increase health care costs. Unless new models...
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Mastrototaro, J. J. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research

A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps
The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much re...
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Heintzman, N. D. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-te...
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Cichosz, S. L., Johansen, M. D., Hejlesen, O. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research

Integration of Administrative, Clinical, and Environmental Data to Support the Management of Type 2 Diabetes Mellitus: From Satellites to Clinical Care
A very interesting perspective of "big data" in diabetes management stands in the integration of environmental information with data gathered for clinical and administrative purposes, to increase the capability of understanding spatial and temporal patterns of diseases. Within the MOSAIC project, funded by the European Union with the goal to design new diabetes analytics, we have jointly analyzed a clinical-administrative dataset of nearly 1.000 type 2 diabetes patients with environmental information derived from air quality maps acquired from remote sensing (satellite) data. Within this context we have adopted a general a...
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Dagliati, A., Marinoni, A., Cerra, C., Decata, P., Chiovato, L., Gamba, P., Bellazzi, R. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research

Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records
Conclusions: We constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research. (Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Anderson, J. P., Parikh, J. R., Shenfeld, D. K., Ivanov, V., Marks, C., Church, B. W., Laramie, J. M., Mardekian, J., Piper, B. A., Willke, R. J., Rublee, D. A. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research

Telemedicine for Diabetes: Current and Future Trends
(Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Klonoff, D. C. Tags: Editorial Source Type: research

Accuracy Evaluation of Two Blood Glucose Monitoring Systems Following ISO 15197:2013
(Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - October 30, 2015 Category: Endocrinology Authors: Berti, F., Scuffi, C., Valgimigli, F. Tags: Letters to the Editor Source Type: research

Influence of Inspiratory Muscle Training on Changes in Fasting Hyperglycemia in the Older Adult: The Epidoso Project
(Source: Journal of Diabetes Science and Technology)
Source: Journal of Diabetes Science and Technology - October 30, 2015 Category: Endocrinology Authors: dos Santos Silva, M., Ramos, L. R., Tufik, S., Togeiro, S. M., Lopes, G. S. Tags: Letters to the Editor Source Type: research