Literature DB >> 29885025

Adjusting insulin doses in patients with type 1 diabetes who use insulin pump and continuous glucose monitoring: Variations among countries and physicians.

Revital Nimri1, Eyal Dassau2, Tomer Segall3, Ido Muller3, Natasa Bratina4, Olga Kordonouri5, Rachel Bello1, Torben Biester5, Klemen Dovc4, Ariel Tenenbaum1,6, Avivit Brener1, Marko Šimunović7, Sophia D Sakka8, Michal Nevo Shenker1, Caroline Gb Passone9, Irene Rutigliano10, Davide Tinti11, Clara Bonura12, Silvana Caiulo12, Anna Ruszala13, Barbara Piccini14, Dinesh Giri15, Ronnie Stein16, Ivana Rabbone11, Patrizia Bruzzi17, Jasna Šuput Omladič4, Caroline Steele18, Guglielmo Beccuti19, Michal Yackobovitch-Gavan1,6, Tadej Battelino4,20, Thomas Danne5, Eran Atlas3, Moshe Phillip1,6.   

Abstract

AIMS: To evaluate physicians' adjustments of insulin pump settings based on continuous glucose monitoring (CGM) for patients with type 1 diabetes and to compare these to automated insulin dose adjustments.
METHODS: A total of 26 physicians from 16 centres in Europe, Israel and South America participated in the study. All were asked to adjust insulin dosing based on insulin pump, CGM and glucometer downloads of 15 patients (mean age 16.2 ± 4.3 years, six female, mean glycated haemoglobin 8.3 ± 0.9% [66.8 ± 7.3 mmol/mol]) gathered over a 3-week period. Recommendations were compared for the relative changes in the basal, carbohydrate to insulin ratio (CR) and correction factor (CF) plans among physicians and among centres and also between the physicians and an automated algorithm, the Advisor Pro (DreaMed Diabetes Ltd, Petah Tikva, Israel). Study endpoints were the percentage of comparison points for which there was full agreement on the trend of insulin dose adjustments (same trend), partial agreement (increase/decrease vs no change) and full disagreement (opposite trend).
RESULTS: The percentages for full agreement between physicians on the trend of insulin adjustments of the basal, CR and CF plans were 41 ± 9%, 45 ± 11% and 45.5 ± 13%, and for complete disagreement they were 12 ± 7%, 9.5 ± 7% and 10 ± 8%, respectively. Significantly similar results were found between the physicians and the automated algorithm. The algorithm magnitude of insulin dose change was at least equal to or less than that proposed by the physicians.
CONCLUSIONS: Physicians provide different insulin dose recommendations based on the same datasets. The automated advice of the Advisor Pro did not differ significantly from the advice given by the physicians in the direction or magnitude of the insulin dosing.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  Advisor Pro; decision support system; insulin pump settings; non-interventional survey; treatment adjustments

Mesh:

Substances:

Year:  2018        PMID: 29885025     DOI: 10.1111/dom.13408

Source DB:  PubMed          Journal:  Diabetes Obes Metab        ISSN: 1462-8902            Impact factor:   6.577


  14 in total

1.  Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

Authors:  Gregory P Forlenza
Journal:  Diabetes Technol Ther       Date:  2019-06       Impact factor: 6.118

Review 2.  Practical Implementation of Diabetes Technology: Real-World Use.

Authors:  Laurel H Messer; Stuart A Weinzimer
Journal:  Diabetes Technol Ther       Date:  2020-02       Impact factor: 6.118

3.  Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes.

Authors:  Revital Nimri; Tadej Battelino; Lori M Laffel; Robert H Slover; Desmond Schatz; Stuart A Weinzimer; Klemen Dovc; Thomas Danne; Moshe Phillip
Journal:  Nat Med       Date:  2020-09-09       Impact factor: 53.440

4.  Diabetes Technology Meeting 2021.

Authors:  Nicole Y Xu; Kevin T Nguyen; Ashley Y DuBord; John Pickup; Jennifer L Sherr; Hazhir Teymourian; Eda Cengiz; Barry H Ginsberg; Claudio Cobelli; David Ahn; Riccardo Bellazzi; B Wayne Bequette; Laura Gandrud Pickett; Linda Parks; Elias K Spanakis; Umesh Masharani; Halis K Akturk; John S Melish; Sarah Kim; Gu Eon Kang; David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2022-05-02

5.  Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management.

Authors:  Tong Sheng; Reid Offringa; David Kerr; Mark Clements; Jerome Fischer; Linda Parks; Michael Greenfield
Journal:  J Diabetes Sci Technol       Date:  2019-09-06

6.  Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians' Recommendations.

Authors:  Revital Nimri; Tal Oron; Ido Muller; Ivana Kraljevic; Montserrat Martín Alonso; Paivi Keskinen; Tanja Milicic; Asaf Oren; Athanasios Christoforidis; Marieke den Brinker; Lutgarda Bozzetto; Andrea Mario Bolla; Michal Krcma; Rosa Anna Rabini; Shadi Tabba; Lizl Smith; Andriani Vazeou; Giulio Maltoni; Elisa Giani; Eran Atlas; Moshe Phillip
Journal:  J Diabetes Sci Technol       Date:  2020-10-26

7.  Smart Insulin Pens: Advancing Digital Transformation and a Connected Diabetes Care Ecosystem.

Authors:  Tejaswi Kompala; Aaron B Neinstein
Journal:  J Diabetes Sci Technol       Date:  2021-01-12

8.  A Pilot Study of Flat and Circadian Insulin Infusion Rates in Continuous Subcutaneous Insulin Infusion (CSII) in Adults with Type 1 Diabetes (FIRST1D).

Authors:  Siân Rilstone; Monika Reddy; Nick Oliver
Journal:  J Diabetes Sci Technol       Date:  2020-02-21

9.  Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health.

Authors:  Johannes O Ferstad; Jacqueline J Vallon; Daniel Jun; Angela Gu; Anastasiya Vitko; Dianelys P Morales; Jeannine Leverenz; Ming Yeh Lee; Brianna Leverenz; Christos Vasilakis; Esli Osmanlliu; Priya Prahalad; David M Maahs; Ramesh Johari; David Scheinker
Journal:  Pediatr Diabetes       Date:  2021-09-01       Impact factor: 3.409

Review 10.  Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Authors:  Nichole S Tyler; Peter G Jacobs
Journal:  Sensors (Basel)       Date:  2020-06-05       Impact factor: 3.576

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