Literature DB >> 28741369

Bolus Advisors: Sources of Error, Targets for Improvement.

John Walsh1, Ruth Roberts2, Timothy S Bailey3, Lutz Heinemann4.   

Abstract

Bolus advisors that are designed to improve the accuracy of individual bolus doses relative to a meal's carb content and the current glucose have not substantially changed since they were introduced 15 years ago despite an obvious need for enhancement and innovation. Although some glycemic benefits have been demonstrated, bolus advisors largely ignore the large amounts of clinical data they gather that could have a significant impact on glucose outcomes. Concerns have also been raised regarding the aggressive nature of largely unpublished or poorly explained bolus advisor algorithms. Hypoglycemia and hyperglycemia remain significant risks due to inaccurate bolus advisor settings and the absence of tracking or an inappropriate handling of bolus on board. This review covers common sources for bolus advisor error such as the selection of physiologically inappropriate bolus advisor settings, the use of short duration of insulin action times, poor algorithm logic that tends to cover all carb intake fully, and an excessive reliance on simplistic dosing algorithms. As well as discussing these areas, we provide 21 ways to improve current bolus calculators.

Entities:  

Keywords:  bolus advisors; bolus calculators; diabetes; insulin pumps; insulin stacking; insulin therapy; prandial insulin dosing; pump settings

Mesh:

Substances:

Year:  2017        PMID: 28741369      PMCID: PMC5761976          DOI: 10.1177/1932296817718213

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  24 in total

1.  Bolus calculator improves long-term metabolic control and reduces glucose variability in pump-treated patients with Type 1 diabetes.

Authors:  G Lepore; A R Dodesini; I Nosari; C Scaranna; A Corsi; R Trevisan
Journal:  Nutr Metab Cardiovasc Dis       Date:  2012-06-04       Impact factor: 4.222

2.  Optimization of insulin pump therapy based on high order run-to-run control scheme.

Authors:  Jianyong Tuo; Huiling Sun; Dong Shen; Hui Wang; Youqing Wang
Journal:  Comput Methods Programs Biomed       Date:  2015-04-28       Impact factor: 5.428

3.  Bolus Calculator Safety Mandates a Need for Standards.

Authors:  John Walsh; Guido Freckmann; Ruth Roberts; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2016-12-20

Review 4.  Evidence-based Mobile Medical Applications in Diabetes.

Authors:  Andjela Drincic; Priya Prahalad; Deborah Greenwood; David C Klonoff
Journal:  Endocrinol Metab Clin North Am       Date:  2016-10-08       Impact factor: 4.741

5.  Prandial insulin dosing using run-to-run control: application of clinical data and medical expertise to define a suitable performance metric.

Authors:  Cesar C Palerm; Howard Zisser; Wendy C Bevier; Lois Jovanovic; Francis J Doyle
Journal:  Diabetes Care       Date:  2007-02-15       Impact factor: 19.112

6.  Application of novel dual wave meal bolus and its impact on glycated hemoglobin A1c level in children with type 1 diabetes.

Authors:  Ewa Pańkowska; Agnieszka Szypowska; Maria Lipka; Monika Szpotańska; Marlena Błazik; Lidia Groele
Journal:  Pediatr Diabetes       Date:  2008-10-20       Impact factor: 4.866

Review 7.  Bolus calculator: a review of four "smart" insulin pumps.

Authors:  Howard Zisser; Lauren Robinson; Wendy Bevier; Eyal Dassau; Christian Ellingsen; Francis J Doyle; Lois Jovanovic
Journal:  Diabetes Technol Ther       Date:  2008-12       Impact factor: 6.118

8.  A Run-to-Run Control Strategy to Adjust Basal Insulin Infusion Rates in Type 1 Diabetes.

Authors:  Cesar C Palerm; Howard Zisser; Lois Jovanovič; Francis J Doyle
Journal:  J Process Control       Date:  2008       Impact factor: 3.666

9.  Computer-assisted insulin dosage adjustment.

Authors:  A Schiffrin; M Mihic; B S Leibel; A M Albisser
Journal:  Diabetes Care       Date:  1985 Nov-Dec       Impact factor: 19.112

10.  The evaluation of a pocket computer as an aid to insulin dose determination by patients.

Authors:  L H Chanoch; L Jovanovic; C M Peterson
Journal:  Diabetes Care       Date:  1985 Mar-Apr       Impact factor: 19.112

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  4 in total

1.  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

2.  Issues and Ideas in Bolus Advisor Research With Commentary on "A Methodology to Compare Insulin Dosing Algorithms in Real-Life Settings".

Authors:  John Walsh
Journal:  J Diabetes Sci Technol       Date:  2017-07-06

3.  LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management.

Authors:  Jeremy Beauchamp; Razvan Bunescu; Cindy Marling; Zhongen Li; Chang Liu
Journal:  Sensors (Basel)       Date:  2021-05-10       Impact factor: 3.576

4.  An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.

Authors:  Taiyu Zhu; Kezhi Li; Lei Kuang; Pau Herrero; Pantelis Georgiou
Journal:  Sensors (Basel)       Date:  2020-09-06       Impact factor: 3.576

  4 in total

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