ArticleJournal of diabetes science and technology2013
A consensus perceived glycemic variability metric.
Article in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
- Evaluation of folliculogenesis and oxidative stress parameters in type 1 diabetes mellitus women with different glycemic profiles.Endocrine · 2024Article
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- Insight into continuous glucose monitoring: from medical basics to commercialized devices.Mikrochimica acta · 2023Review
- Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead.Frontiers in clinical diabetes and healthcare · 2023Review
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management.Journal of diabetes science and technology · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.Journal of medical Internet research · 2019Review
- A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas.Diabetes technology & therapeutics · 2017Article
- Machine Learning and Data Mining Methods in Diabetes Research.Computational and structural biotechnology journal · 2017Review
- Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis.Journal of diabetes science and technology · 2015Article
- Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies.BMC endocrine disorders · 2015Article
- Hypoglycemia in Type 2 Diabetes--More Common Than You Think: A Continuous Glucose Monitoring Study.Journal of diabetes science and technology · 2015Article
- Differences in Glycemic Variability Between Normoglycemic and Prediabetic Subjects.Journal of diabetes science and technology · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGlycemic variability (GV) is an important component of overall glycemic control for patients with diabetes mellitus. Physicians are able to recognize excessive GV from continuous glucose monitoring (CGM) plots; however, there is currently no universally agreed upon GV metric. The objective of this study was to develop a consensus perceived glycemic variability (CPGV) metric that could be routinely applied to CGM data to assess diabetes mellitus control.
methodsTwelve physicians actively managing patients with type 1 diabetes mellitus rated a total of 250 24 h CGM plots as exhibiting low, borderline, high, or extremely high GV. Ratings were averaged to obtain a consensus and then input into two machine learning algorithms: multilayer perceptrons (MPs) and support vector machines for regression (SVR). In silica experiments were run using each algorithm with different combinations of 12 descriptive input features. Ten-fold cross validation was used to evaluate the performance of each model.
resultsThe SVR models approximated the physician consensus ratings of unseen CGM plots better than the MP models. When judged by the root mean square error, the best SVR model performed comparably to individual physicians at matching consensus ratings. When applied to 262 different CGM plots as a screen for excessive GV, this model had accuracy, sensitivity, and specificity of 90.1%, 97.0%, and 74.1%, respectively. It significantly outperformed mean amplitude of glycemic excursion, standard deviation, distance traveled, and excursion frequency.
conclusionsThis new CPGV metric could be used as a routine measure of overall glucose control to supplement glycosylated hemoglobin in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.