Literature DB >> 32073311

Time in range in children with type 1 diabetes using treatment strategies based on non-automated insulin delivery systems in the real-world.

Valentino Cherubini1, Riccardo Bonfanti2, Alberto Casertano3, Elena De Nitto4, Antonio Iannilli5, Fortunato Lombardo6, Giulio Maltoni7, Marco Marigliano8, Marta Bassi9, Nicola Minuto10, Enza Mozzillo11, Ivana Rabbone12, Novella Rapini13, Andrea Rigamonti14, Giuseppina Salzano15, Andrea Scaramuzza16, Riccardo Schiaffini17, Davide Tinti18, Sonia Toni19, Luca Zagaroli20, Stefano Zucchini21, Claudio Maffeis22, Rosaria Gesuita23.   

Abstract

Background Glucose sensors consist of real time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM). Their clinical use has been widely increasing during last five years. The aim of this study is to evaluate percentage of time in range (TIR) in a large group of children with type 1 diabetes (T1D) using glucose sensors with non-automated insulin delivery systems, in a real-world setting. Methods An eleven-centre cross-sectional study was conducted during January-May 2019. Children with T1D under the age of 18 years, all using rtCGM or isCGM for more than one year, treated with multiple daily injections (MDI) or non-automated insulin pump (IP) were recruited consecutively. Clinical data, HbA1c measurement, and CGM downloaded data were collected by each centre and included in a centralised database for the analysis. Glucose metrics of four treatment strategies were analysed: isCGM-MDI, rtCGM-MDI, isCGM-IP, rtCGM-IP. Results Data from 666 children with T1D (51% male; 49% female), median age 12 years, diabetes duration 5 years, were analysed. A rtCGM was used by 51% of the participants, and a non-automated insulin pump (IP) by 46%. For isCGM-MDI, rtCGM-MDI, isCGM-IP, and rtCGM-IP, the median time-in-range 70-180 mg/dl (TIR) values were 49%, 56%, 56%, 61% (p<0.001) respectively; HbA1c 7.6%, 7.5%, 7.3%, 7.3% (p<0.001), respectively. Use of rtCGM was associated with significant lower time below target range <70 mg/dl (TBR) and reduced the percentage coefficient of variation of glucose (%CV), independently by the insulin-delivery system used. Conclusions Among non-automated insulin-delivery strategies, simultaneous use of rtCGM and IP was associated with higher percent of TIR, lower time above range >180 mg/dl (TAR) and lower HbA1c. If there are no barriers, an upgrade of the treatment strategy with a higher performing technology should be offered to all children who do not achieve blood glucose metrics within the suggested range.

Entities:  

Year:  2020        PMID: 32073311     DOI: 10.1089/dia.2020.0031

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  15 in total

Review 1.  Current Status and Emerging Options for Automated Insulin Delivery Systems.

Authors:  Gregory P Forlenza; Rayhan A Lal
Journal:  Diabetes Technol Ther       Date:  2022-03-14       Impact factor: 7.337

2.  Universal Subsidized Continuous Glucose Monitoring Funding for Young People With Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based Study.

Authors:  Stephanie R Johnson; Deborah J Holmes-Walker; Melissa Chee; Arul Earnest; Timothy W Jones; Maria Craig; Kym Anderson; Geoff Ambler; Helen Barrett; Jenny Batch; Philip Bergman; Fergus Cameron; Peter Colman; Louise Conwell; Chris Cooper; Jennifer Couper; Elizabeth Davis; Martin de Bock; Kim Donaghue; Jan Fairchild; Gerry Fegan; Spiros Fourlanos; Sarah Glastras; Leonie Gray; Shane Hamblin; Paul Hofman; Dianne Jane Holmes-Walker; Neville Howard; Michelle Jack; Steven James; Craig Jefferies; Stephanie Johnson; Jeff Kao; Bruce R King; Antony Lafferty; Michelle Martin; Robert McCrossin; Mark Pascoe; Ryan Paul; Dorota Pawlak; Alexia Peña; Sarah Price; Darrell Price; Christine Rodda; David Simmons; Richard Sinnott; Alan Sive; Carmel Smart; Monique Stone; Steve Stranks; Elaine Tham; Charles Verge; Glenn Ward; Ben Wheeler; Judy Williams; Helen Woodhead; Nick Woolfield; Anthony Zimmermann
Journal:  Diabetes Care       Date:  2022-02-01       Impact factor: 19.112

3.  Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies.

Authors:  Mary Katherine Ray; Alana McMichael; Maria Rivera-Santana; Jacob Noel; Tamara Hershey
Journal:  JMIR Diabetes       Date:  2021-06-03

4.  The Association between Treatment Modality, Lipid Profile, Metabolic Control in Children with Type 1 Diabetes and Celiac Disease-Data from the International Sweet Registry.

Authors:  Monica Marino; Alexander J Eckert; Shoshana Tell; Nevena Krnic; Grazyna Deja; Vinni Faber Rasmussen; Raquel Coelho; Sladjana Todorovic; Craig A Jefferies; Eman Sherif; Carolina Martinez Mateu; Maria Elena Lionetti
Journal:  Nutrients       Date:  2021-12-15       Impact factor: 5.717

5.  Beyond A1C: A Practical Approach to Interpreting and Optimizing Continuous Glucose Data in Youth.

Authors:  Iman Al-Gadi; Sruthi Menon; Sarah K Lyons; Daniel J DeSalvo
Journal:  Diabetes Spectr       Date:  2021-05-25

6.  Comparison of the effects of lockdown due to COVID-19 on glucose patterns among children, adolescents, and adults with type 1 diabetes: CGM study.

Authors:  Guido Di Dalmazi; Giulio Maltoni; Claudio Bongiorno; Lorenzo Tucci; Valeria Di Natale; Simona Moscatiello; Gilberto Laffi; Andrea Pession; Stefano Zucchini; Uberto Pagotto
Journal:  BMJ Open Diabetes Res Care       Date:  2020-10

Review 7.  Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes?

Authors:  Tatsuhiko Urakami
Journal:  Front Endocrinol (Lausanne)       Date:  2020-09-15       Impact factor: 5.555

8.  Impact of lockdown during COVID-19 emergency on glucose metrics of children and adolescents with type 1 diabetes in Piedmont, Italy.

Authors:  Davide Tinti; Silvia Savastio; Caterina Grosso; Valeria De Donno; Michela Trada; Martina Nugnes; Enrica Bertelli; Luisa Franceschi; Martina Marchisio; Erica Pozzi; Eleonora Tappi; Enrico Felici; Luisa De Sanctis; Ivana Rabbone
Journal:  Acta Diabetol       Date:  2021-03-15       Impact factor: 4.280

9.  Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes.

Authors:  Michael L Ferm; Daniel J DeSalvo; Laura M Prichett; James K Sickler; Risa M Wolf; Roomasa Channa
Journal:  JAMA Netw Open       Date:  2021-09-01

10.  Lessons learned from the continuous glucose monitoring metrics in pediatric patients with type 1 diabetes under COVID-19 lockdown.

Authors:  Avivit Brener; Kineret Mazor-Aronovitch; Marianna Rachmiel; Noa Levek; Galia Barash; Orit Pinhas-Hamiel; Yael Lebenthal; Zohar Landau
Journal:  Acta Diabetol       Date:  2020-10-07       Impact factor: 4.280

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