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Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis
OBJECTIVE: To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use. RESEARCH DESIGN AND METHODS: We conducted analysis of 2,329,166 days (6,381 patient-years) of conti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Diabetes Association
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862393/ https://www.ncbi.nlm.nih.gov/pubmed/36126177 http://dx.doi.org/10.2337/dc22-1217 |
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author | Kovatchev, Boris P. Singh, Harsimran Mueller, Lars Gonder-Frederick, Linda A. |
author_facet | Kovatchev, Boris P. Singh, Harsimran Mueller, Lars Gonder-Frederick, Linda A. |
author_sort | Kovatchev, Boris P. |
collection | PubMed |
description | OBJECTIVE: To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use. RESEARCH DESIGN AND METHODS: We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.4% female, age (median/quartiles/range) 39/19–58/1–92 years, mean ± SD glucose management indicator (GMI) 7.5 ± 0.8. Primary outcome was time in target range (TIR) (70–180 mg/dL). Secondary outcomes included CGM-based glycemic control metrics and frequency of user-initiated boluses. RESULTS: Compared with PLGS, AID increased TIR on average from 58.4 to 70.5%. GMI and percent time above and below target range improved as well: from 7.5 to 7.1, 39.9 to 28.1%, and 1.66 to 1.46%, respectively; all P values <0.0001. Stratification of outcomes by age and baseline GMI revealed clinically significant differences. Glycemic improvements were most pronounced in those <18 years old (TIR improvement 14.0 percentage points) and those with baseline GMI >8.0 (TIR improvement 13.2 percentage points). User-initiated correction boluses decreased from 2.7 to 1.8 per day, while user-initiated meal boluses remained stable at 3.6 to 3.8 per day. CONCLUSIONS: Observed in real life of >19,000 individuals with type 1 diabetes, transitions from PLGS to AID resulted in improvement of all glycemic parameters, equivalent to improvements observed in randomized clinical trials, and reduced user-initiated boluses. However, glycemic and behavioral changes with AID use may differ greatly across different demographic and clinical groups. |
format | Online Article Text |
id | pubmed-9862393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Diabetes Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-98623932023-02-03 Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis Kovatchev, Boris P. Singh, Harsimran Mueller, Lars Gonder-Frederick, Linda A. Diabetes Care Emerging Technologies: Data Systems and Devices OBJECTIVE: To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use. RESEARCH DESIGN AND METHODS: We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.4% female, age (median/quartiles/range) 39/19–58/1–92 years, mean ± SD glucose management indicator (GMI) 7.5 ± 0.8. Primary outcome was time in target range (TIR) (70–180 mg/dL). Secondary outcomes included CGM-based glycemic control metrics and frequency of user-initiated boluses. RESULTS: Compared with PLGS, AID increased TIR on average from 58.4 to 70.5%. GMI and percent time above and below target range improved as well: from 7.5 to 7.1, 39.9 to 28.1%, and 1.66 to 1.46%, respectively; all P values <0.0001. Stratification of outcomes by age and baseline GMI revealed clinically significant differences. Glycemic improvements were most pronounced in those <18 years old (TIR improvement 14.0 percentage points) and those with baseline GMI >8.0 (TIR improvement 13.2 percentage points). User-initiated correction boluses decreased from 2.7 to 1.8 per day, while user-initiated meal boluses remained stable at 3.6 to 3.8 per day. CONCLUSIONS: Observed in real life of >19,000 individuals with type 1 diabetes, transitions from PLGS to AID resulted in improvement of all glycemic parameters, equivalent to improvements observed in randomized clinical trials, and reduced user-initiated boluses. However, glycemic and behavioral changes with AID use may differ greatly across different demographic and clinical groups. American Diabetes Association 2022-11 2022-09-20 /pmc/articles/PMC9862393/ /pubmed/36126177 http://dx.doi.org/10.2337/dc22-1217 Text en © 2022 by the American Diabetes Association https://www.diabetesjournals.org/content/licenseReaders may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license. |
spellingShingle | Emerging Technologies: Data Systems and Devices Kovatchev, Boris P. Singh, Harsimran Mueller, Lars Gonder-Frederick, Linda A. Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title | Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title_full | Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title_fullStr | Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title_full_unstemmed | Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title_short | Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis |
title_sort | biobehavioral changes following transition to automated insulin delivery: a large real-life database analysis |
topic | Emerging Technologies: Data Systems and Devices |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862393/ https://www.ncbi.nlm.nih.gov/pubmed/36126177 http://dx.doi.org/10.2337/dc22-1217 |
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