Literature DB >> 10078542

Is blood glucose predictable from previous values? A solicitation for data.

T Bremer1, D A Gough.   

Abstract

An important question about blood glucose control in diabetes is, Can present and future blood glucose values be predicted from recent blood glucose history? If this is possible, new continuous blood glucose monitoring technologies under development may lead to qualitatively better therapeutic capabilities. Not only could continuous monitoring technologies alert a user when a hypoglycemic episode or other blood glucose excursion is underway, but measurements may also provide sufficient information to predict near-future blood glucose values. A predictive capability based only on recent blood glucose history would be advantageous because there would be no need to involve models of glucose and insulin distribution, with their inherent requirement for detailed accounting of vascular glucose loads and insulin availability. Published data analyzed here indicate that blood glucose dynamics are not random, and that blood glucose values can be predicted, at least for the near future, from frequently sampled previous values. Data useful in further exploring this concept are limited, however, and an appeal is made for collection of more.

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Year:  1999        PMID: 10078542     DOI: 10.2337/diabetes.48.3.445

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  19 in total

1.  Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms.

Authors:  B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2010-03-01

2.  Should diabetes healthcare abandon its gold standard?

Authors:  George Treviño
Journal:  MedGenMed       Date:  2007-11-01

3.  Combined insulin pump therapy with real-time continuous glucose monitoring significantly improves glycemic control compared to multiple daily injection therapy in pump naïve patients with type 1 diabetes; single center pilot study experience.

Authors:  Scott W Lee; Tom Sweeney; Debbie Clausen; Celia Kolbach; Allen Hassen; Anthony Firek; Charles Brinegar; Jerrold Petrofsky
Journal:  J Diabetes Sci Technol       Date:  2007-05

Review 4.  Utility of different glycemic control metrics for optimizing management of diabetes.

Authors:  Klaus-Dieter Kohnert; Peter Heinke; Lutz Vogt; Eckhard Salzsieder
Journal:  World J Diabetes       Date:  2015-02-15

5.  Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.

Authors:  Darpit Dave; Daniel J DeSalvo; Balakrishna Haridas; Siripoom McKay; Akhil Shenoy; Chester J Koh; Mark Lawley; Madhav Erraguntla
Journal:  J Diabetes Sci Technol       Date:  2020-06-01

6.  Automatic data processing to achieve a safe telemedical artificial pancreas.

Authors:  M Elena Hernando; Gema García-Sáez; Iñaki Martínez-Sarriegui; Agustín Rodríguez-Herrero; Carmen Pérez-Gandía; Mercedes Rigla; Alberto de Leiva; Ismael Capel; Belén Pons; Enrique J Gómez
Journal:  J Diabetes Sci Technol       Date:  2009-09-01

7.  Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates.

Authors:  A M Albisser; D Baidal; R Alejandro; C Ricordi
Journal:  Diabetologia       Date:  2005-06-03       Impact factor: 10.122

8.  Glucose Monitoring in Individuals With Diabetes Using a Long-Term Implanted Sensor/Telemetry System and Model.

Authors:  Joseph Y Lucisano; Timothy L Routh; Joe T Lin; David A Gough
Journal:  IEEE Trans Biomed Eng       Date:  2016-10-19       Impact factor: 4.538

9.  Hypoglycemia detection and prediction using continuous glucose monitoring-a study on hypoglycemic clamp data.

Authors:  Cesar C Palerm; B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2007-09

10.  Nonlinear modeling of the dynamic effects of infused insulin on glucose: comparison of compartmental with Volterra models.

Authors:  Georgios D Mitsis; Mihalis G Markakis; Vasilis Z Marmarelis
Journal:  IEEE Trans Biomed Eng       Date:  2009-06-02       Impact factor: 4.538

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