ArticleDiabetes technology & therapeutics2014
Accuracy and robustness of dynamical tracking of average glycemia (A1c) to provide real-time estimation of hemoglobin A1c using routine self-monitored blood glucose data.
Article in Diabetes technology & therapeutics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 33 citations in OpenAlex.
- Evaluation of a Methodology for Estimating HbA1c Value by a New Glucose Meter.Journal of diabetes science and technology · 2015Trial
- GLM-DM: language model boosted neural networks for HbA1c trend prediction in diabetes mellitus.Future science OA · 2025Article
- Evaluation of HbA1c from CGM traces in an Indian population.Frontiers in endocrinology · 2023Article
- Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk: an expert-based multidisciplinary Delphi consensus.Cardiovascular diabetology · 2022Article
- Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment.Journal of diabetes investigation · 2021Observational
- A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management.Journal of diabetes science and technology · 2021Article
- Expert Panel Recommendations for Use of Standardized Glucose Reporting System Based on Standardized Glucometrics Plus Visual Ambulatory Glucose Profile (AGP) Data in Clinical Practice.Frontiers in endocrinology · 2021Review
- Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?Diabetes technology & therapeutics · 2020Article
- A View Beyond HbA1c: Role of Continuous Glucose Monitoring.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2019Review
- 10th Annual Symposium on Self-Monitoring of Blood Glucose, April 27-29, 2017, Warsaw, Poland.Diabetes technology & therapeutics · 2018Article
- International Consensus on Use of Continuous Glucose Monitoring.Diabetes care · 2017Review
- Digital Health Care by In Silico Glycation of HbA1 Blood Cells.Journal of diabetes science and technology · 2017Article
- Metrics for glycaemic control - from HbANature reviews. Endocrinology · 2017 · on this mapReview
- Patterns of diabetes care in Slovenia, Croatia, Serbia, Bulgaria and Romania : An observational, non-interventional, cross-sectional study.Wiener klinische Wochenschrift · 2017Observational
- Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring.Science translational medicine · 2016Article
- Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes.Journal of diabetes science and technology · 2015Article
- What Do Your Fingernails Say About You? Can They Indicate That You Have Diabetes?Journal of diabetes science and technology · 2015Article
- The Glucose Measurement Industry and Hemoglobin A1c: An Opportunity for Creative Destruction.Journal of diabetes science and technology · 2015Article
- Validation of a hemoglobin A1c model in patients with type 1 and type 2 diabetes and its use to go beyond the averaged relationship of hemoglobin A1c and mean glucose level.Journal of translational medicine · 2014Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLaboratory hemoglobin A1c (HbA1c) assays are typically done only every few months. However, self-monitored blood glucose (SMBG) readings offer the possibility for real-time estimation of HbA1c. We present a new dynamical method tracking changes in average glycemia to provide real-time estimation of A1c (eA1c). MATERIALS AND
methodsA new two-step algorithm was constructed that includes: (1) tracking fasting glycemia to compute base eA1c updated with every fasting SMBG data point and (2) calibration of the base eA1c trace with monthly seven-point SMBG profiles to capture the principal components of blood glucose variability and produce eA1c. A training data set (n=379 subjects) was used to estimate model parameters. The model was then fixed and applied to an independent test data set (n=375 subjects). Accuracy was evaluated in the test data set by computing mean absolute deviation (MAD) and mean absolute relative deviation (MARD) of eA1c from reference HbA1c, as well as eA1c-HbA1c correlation.
resultsMAD was 0.50, MARD was 6.7%, and correlation between eA1c and reference HbA1c was r=0.76. Using an HbA1c error grid plot, 77.5% of all eA1c fell within 10% from reference HbA1c, and 97.9% fell within 20% from reference.
conclusionsA dynamical estimation model was developed that achieved accurate tracking of average glycemia over time. The model is capable of working with infrequent SMBG data typical for type 2 diabetes, thereby providing a new tool for HbA1c estimation at the patient level. The computational demands of the procedure are low; thus it is readily implementable into home SMBG meters. Real-time HbA1c estimation could increase patients' motivation to improve diabetes control.
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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 OpenQuestion graph.