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Effectiveness and safety of an artificial intelligence-based medical decision support system for adjusting insulin pump settings in children with type 1 diabetes mellitus: randomized controlled trial

https://doi.org/10.14341/DM13171

Abstract

BACKGROUND: Previously, we presented the process of developing a clinical decision support system (CDSS) for adjusting insulin pump (IP) settings in children with type 1 diabetes mellitus (T1D) and assessing the agreement of the recommendations it generates with the expert opinion. The CDSS demonstrated satisfactory forecasting of glucose profile and agreement rates between recommendations CDSS and experts.

AIM: To evaluate the effectiveness and safety of using CDSS in children with T1D, testing the hypothesis of non-inferiority (with a limit of -5%) of relative increase of glucose time in range (TIR) over 6 months.

MATERIALS AND METHODS: The trial included 80 children with T1D, divided into two comparable groups of 40 children using the minimization method. Patients in the main group received recommendations for adjusting the IP settings from a physician who uses the CDSS; patients in the control group received recommendations from a physician (control group). Patients were observed for 6 months with remote consultations once a month (7 consultations in total) and monitoring of glycated hemoglobin (HbA1c) at 1, 4 and 7 consultations. The primary outcome is the difference in group mean relative changes in TIR (%), secondary outcomes are TIR (%), HbA1c concentration. 

RESULTS: The trial was completed by 63 patients 32 in the main group, 31 in the control group. The difference in the mean relative increase in TIR at the 7th consultation in the groups was 3.02%, one-sided 95% CI (-4.55%; inf ). Thus, the lower bound of this CI is greater than the noninferiority limit of -5%, and the noninferiority hypothesis can be accepted. There were no statistically significant differences between groups for all outcomes. The dynamics of the indicators were positive in the main group and had a statistical tendency towards positive changes in the control group.

CONCLUSION: The use of CDSS was no less effective in terms of the TIR than the management of the patient by a physician. The use of CDSS in clinical practice can help in regular and frequent monitoring of children with T1D, and standardize at a high level the approach to correction of IP parameters, supplemented with CGM.

About the Authors

D. N. Laptev
Endocrinology Research Centre
Russian Federation

Dmitry N. Laptev, MD, PhD, Professor

Researcher ID: O-1826-2013;

Scopus Author ID: 24341083800

Moscow



D. Yu. Sorokin
Endocrinology Research Centre
Russian Federation

Daniil Yu. Sorokin

Researcher ID: HJY-5714-2023

11 Dm. Ulyanova street, 117036 Moscow



E. S. Trufanova
Endocrinology Research Centre
Russian Federation

Evgeniya S. Trufanova

Moscow



O. Yu. Rebrova
Endocrinology Research Centre; Pirogov National Research Medical University
Russian Federation

Olga Yu. Rebrova, PhD

ResearcherID: A-9071-2010;

Scopus Author ID: 6601986825

Moscow



O. B. Bezlepkina
Endocrinology Research Centre
Russian Federation

Olga B. Bezlepkina, PhD, Professor

ResearcherID: B-6627-2017;

Scopus Author ID: 6507632848

Moscow



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Supplementary files

1. Рисунок 1. Время в целевом и смежных диапазонах по данным систем мониторинга глюкозы пациентов, окончивших клиническое исследование (6 мес.), в основной группе (n=32) и группе контроля (n=31) (средние значения показателя); данные с систем мониторинга глюкозы получены за последние 3 месяца использования.
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Type Исследовательские инструменты
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2. Рисунок 2. Уровни гликированного гемоглобина HbA1c за время клинического исследования в основной группе и группе контроля (Me, Q1-Q3, min, max).
Subject
Type Исследовательские инструменты
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Indexing metadata ▾

Review

For citations:


Laptev D.N., Sorokin D.Yu., Trufanova E.S., Rebrova O.Yu., Bezlepkina O.B. Effectiveness and safety of an artificial intelligence-based medical decision support system for adjusting insulin pump settings in children with type 1 diabetes mellitus: randomized controlled trial. Diabetes mellitus. 2024;27(3):254-264. (In Russ.) https://doi.org/10.14341/DM13171

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ISSN 2072-0351 (Print)
ISSN 2072-0378 (Online)