scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

Continuous glucose monitoring: data management and evaluation by patients and health care professionals – current situation and developments

2018 ◽  
Vol 42 (6) ◽  
pp. 225-233
Author(s):  
Guido Freckmann ◽  
Jochen Mende
Keyword(s):  
Health Care ◽  
Continuous Glucose Monitoring ◽  
Health Care Professionals ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Diabetic Patients ◽  
Diabetes Therapy ◽  
Software Applications ◽  
Metabolic States ◽  
Specific Device

Abstract Continuous glucose monitoring (CGM) technology represents a valuable tool for diabetic patients to control and regulate their blood glucose (BG) levels and to reduce adverse metabolic states, for example, by defining glucose alarm thresholds that alert users if the glucose value crosses to an undesired range. Improvement of CGM technology is ongoing, but there are barriers which confine the usefulness of CGM systems. The utility is mainly defined by the operability of the specific device and also by the provided benefit of available CGM software solutions. In order to take best advantage of diabetes therapy, users should be adequately educated in how to use their CGM system and how to interpret the collected data. Different CGM software applications provide partially different CGM reports and statistics. The standardization of this information also would be conducive to the best possible diabetes management.

ANALYZING AND IMPROVING WEB APPLICATION QUALITY USING DESIGN PATTERNS

2012 ◽  
Vol 2 (2) ◽  
pp. 112-116
Author(s):  
Shikha Bhatia ◽  
Mr. Harshpreet Singh
Keyword(s):  
Web Application ◽  
Design Pattern ◽  
Design Patterns ◽  
Web Applications ◽  
Past Research ◽  
Software Systems ◽  
Interactive Software ◽  
Web Based ◽  
Software Application ◽  
Execution Speed

With the mounting demand of web applications, a number of issues allied to its quality have came in existence. In the meadow of web applications, it is very thorny to develop high quality web applications. A design pattern is a general repeatable solution to a generally stirring problem in software design. It should be noted that design pattern is not a finished product that can be directly transformed into source code. Rather design pattern is a depiction or template that describes how to find solution of a problem that can be used in many different situations. Past research has shown that design patterns greatly improved the execution speed of a software application. Design pattern are classified as creational design patterns, structural design pattern, behavioral design pattern, etc. MVC design pattern is very productive for architecting interactive software systems and web applications. This design pattern is partition-independent, because it is expressed in terms of an interactive application running in a single address space. We will design and analyze an algorithm by using MVC approach to improve the performance of web based application. The objective of our study will be to reduce one of the major object oriented features i.e. coupling between model and view segments of web based application. The implementation for the same will be done in by using .NET framework.

scholarly journals Functional data analysis and prediction tools for continuous glucose-monitoring studies

2020 ◽  
pp. 1-10
Author(s):  
Emrah Gecili ◽  
Rui Huang ◽  
Jane C. Khoury ◽  
Eileen King ◽  
Mekibib Altaye ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Functional Data ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Prediction Errors ◽  
Medical Monitoring

Abstract Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

scholarly journals G4Killer web application: a tool to design G-quadruplex mutations

2020 ◽  
Vol 36 (10) ◽  
pp. 3246-3247
Author(s):  
Vaclav Brazda ◽  
Jan Kolomaznik ◽  
Jean-Louis Mergny ◽  
Jiri Stastny
Keyword(s):  
Dna Sequences ◽  
Web Application ◽  
Rational Design ◽  
Therapeutic Potential ◽  
Supplementary Information ◽  
Web Tool ◽  
Web Based ◽  
Dna Structures ◽  
G Quadruplex ◽  
User Friendly

Abstract Motivation G-quadruplexes (G4) are important regulatory non-B DNA structures with therapeutic potential. A tool for rational design of mutations leading to decreased propensity for G4 formation should be useful in studying G4 functions. Although tools exist for G4 prediction, no easily accessible tool for the rational design of G4 mutations has been available. Results We developed a web-based tool termed G4Killer that is based on the G4Hunter algorithm. This new tool is a platform-independent and user-friendly application to design mutations crippling G4 propensity in a parsimonious way (i.e., keeping the primary sequence as close as possible to the original one). The tool is integrated into our DNA analyzer server and allows for generating mutated DNA sequences having the desired lowered G4Hunter score with minimal mutation steps. Availability and implementation The G4Killer web tool can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Data Browser Matsch | Mazia: Web Application to access microclimatic time series of an ecological research site

2021 ◽  
Vol 7 ◽  
Author(s):  
Martin Palma ◽  
Alessandro Zandonai ◽  
Luca Cattani ◽  
Johannes Klotz ◽  
Giulio Genova ◽  
...  
Keyword(s):  
Time Series ◽  
Web Application ◽  
Scientific Data ◽  
Time Range ◽  
Ecological Research ◽  
Data Accessibility ◽  
Web Based ◽  
South Tyrol ◽  
Research Site ◽  
User Friendly

Easily accessible data is an essential requirement for scientific data analysis. The Data Browser Matsch | Mazia was designed to provide a fast and comprehensible solution to access, visualize and download the microclimatic measurements of the IT 25 LT(S)ER Match | Mazia research site in South Tyrol, Northern Italy, with the overall aim to provide straightforward data accessibility and enhance dissemination. Data Browser Matsch | Mazia is a user-friendly web-based application to visualize and download micrometeorological and biophysical time series of the Long-Term Socio-Ecological Research site Matsch | Mazia in South Tyrol, Italy. It is designed both for the general public and researchers. The Data Browser Matsch | Mazia drop-down menus allow the user to query the InfluxDB database in the backend by selecting the measurements, time range, land use and elevation. Interactive Grafana dashboards show dynamic graphs of the time series.

scholarly journals Accuracy of Dexcom G6 Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Diabetes

2021 ◽  
Author(s):  
Georgia M. Davis ◽  
Elias K. Spanakis ◽  
Alexandra L. Migdal ◽  
Lakshmi G. Singh ◽  
Bonnie Albury ◽  
...  
Keyword(s):  
Critically Ill ◽  
Continuous Glucose Monitoring ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Hospitalized Patients ◽  
Matched Pair ◽  
Patients With Diabetes ◽  
Clarke Error Grid ◽  
Error Grid

<b>Background: </b>Advances in continuous glucose monitoring (CGM) have transformed ambulatory diabetes management. Until recently, inpatient use of CGM has remained investigational with limited data on its accuracy in the hospital setting. <p><b>Methods: </b>To analyze the accuracy of Dexcom G6 CGM,<b> </b>we compared retrospective matched-pair CGM and capillary point-of-care (POC) glucose data from three inpatient CGM studies (two interventional and one observational) in general medicine and surgery patients with diabetes treated with insulin. Analysis of accuracy metrics included mean absolute relative difference (MARD), median absolute relative difference (ARD), and proportion of CGM values within ±15, 20 and 30% or ±15, 20 and 30 mg/dL of POC reference values for blood glucose >100 mg/dL or ≤100 mg/dL, respectively (?/15, /20, 0/30). Clinical reliability was assessed using Clarke error grid analyses.</p> <p><b>Results: </b>A total of 218 patients were included (96% with type 2 diabetes) with a mean age of 60.6 ± 12 years. The overall MARD (n=4,067 matched glucose pairs) was 12.8% and median ARD was 10.1% [IQR 4.6, 17.6]. The proportion of readings meeting ?/15, /20 and 0/30 criteria were 68.7, 81.7, and 93.8%. Clarke error grid analysis showed 98.7% of all values in zones A+B. MARD and median ARD were higher in hypoglycemia (<70mg/dL) and severe anemia (hemoglobin <7g/dL).</p> <p><b>Conclusion: </b>Our results indicate that CGM technology is a reliable tool for hospital use and may help improve glucose monitoring in non-critically ill hospitalized patients with diabetes. </p>

scholarly journals Practical implementation of remote continuous glucose monitoring in hospitalized patients with diabetes

2021 ◽  
Author(s):  
Matt Baker ◽  
Megan E Musselman ◽  
Rachel Rogers ◽  
Richard Hellman
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Practical Implementation ◽  
Management Decisions ◽  
Grid Analysis ◽  
Inpatient Diabetes ◽  
Inpatient Diabetes Management ◽  
Error Grid

Abstract Disclaimer In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose Inpatient diabetes management involves frequent assessment of glucose levels for treatment decisions. Here we describe a program for inpatient real-time continuous glucose monitoring (rtCGM) at a community hospital and the accuracy of rtCGM-based glucose estimates. Methods Adult inpatients with preexisting diabetes managed with intensive insulin therapy and a diagnosis of coronavirus disease 2019 (COVID-19) were monitored via rtCGM for safety. An rtCGM system transmitted glucose concentration and trending information at 5-minute intervals to nearby smartphones, which relayed the data to a centralized monitoring station. Hypoglycemia alerts were triggered by rtCGM values of ≤85 mg/dL, but rtCGM data were otherwise not used in management decisions; insulin dosing adjustments were based on blood glucose values measured via blood sampling. Accuracy was evaluated retrospectively by comparing rtCGM values to contemporaneous point-of-care (POC) blood glucose values. Results A total of 238 pairs of rtCGM and POC data points from 10 patients showed an overall mean absolute relative difference (MARD) of 10.3%. Clarke error grid analysis showed 99.2% of points in the clinically acceptable range, and surveillance error grid analysis showed 89.1% of points in the lowest risk category. It was determined that for 25% of the rtCGM values, discordances in rtCGM and POC values would likely have resulted in different insulin doses. Insulin dose recommendations based on rtCGM values differed by 1 to 3 units from POC-based recommendations. Conclusion rtCGM for inpatient diabetes monitoring is feasible. Evaluation of individual rtCGM-POC paired values suggested that using rtCGM data for management decisions poses minimal risks to patients. Further studies to establish the safety and cost implications of using rtCGM data for inpatient diabetes management decisions are warranted.

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