Glucose Predictions Improve Glycemic Control: A Digital Twin Evaluation.

IF 7.4 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Diabetes technology & therapeutics Pub Date : 2026-10-01 Epub Date: 2026-03-12 DOI:10.1177/15209156261432144
Pau Herrero, Magí Andorrà, Marc D Breton, Ajandek Peak, Matthias Koehler, Yannick Klopfenstein, Eemeli Leppäaho, Mattia Zanon, Christian Ringemann, Patrick Lustenberger, Timor Glatzer
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引用次数: 0

Abstract

Background: Glucose predictions aim to empower continuous glucose monitoring (CGM) users by enabling preventive actions to reduce adverse glycemic events. The Accu-Chek® SmartGuide Predict app offers several AI-enabled predictive features, driven by machine learning algorithms. These include notifications for a low glucose predict within 30 min (LGP) and for nighttime low glucose risk, as well as a 2-h continuous glucose forecast.

Aims: This study aimed to quantify the potential glycemic benefits of using the Predict app's predictive features in an adult population with type 1 diabetes (T1D).

Methods: A comparative in silico study was conducted using the clinically backed University of Virginia Replay digital twin simulator. A control arm, simulating standard hypoglycemia and hyperglycemia mitigation strategies in line with international guidelines, was compared against intervention arms that incorporated probabilistic user behavior models responding to the app's predictive features. The evaluation was performed on 204 digital twins, representing 29,929 days of data, generated from the REPLACE-BG clinical trial dataset.

Results: Results demonstrated that using the app's predictive features has the potential to improve glycemic control in adults with T1D. The simulated intervention led to an average 2.9 percentage point reduction in time below range (<70 mg/dL), and a clinically significant increase of more than 3.6 percentage points in time in range (70-180 mg/dL). Furthermore, the daily number of CGM hypoglycemia alarms (<70 mg/dL) was reduced by 67%. The findings also suggest that consuming 10 g of fast-acting carbohydrates in response to LGP notifications provides an optimal balance, effectively preventing hypoglycemia while limiting rebound hyperglycemia.

Conclusions: This in silico evaluation provides strong evidence supporting the potential clinical utility of the Accu-Chek SmartGuide Predict app for improving glycemic management in adults with T1D.

葡萄糖预测改善血糖控制:数字孪生评估。
背景:血糖预测旨在通过采取预防措施来减少不良血糖事件,从而增强连续血糖监测(CGM)用户的能力。Accu-Chek®SmartGuide Predict应用程序提供了几个由机器学习算法驱动的人工智能预测功能。其中包括30分钟内低血糖预测(LGP)和夜间低血糖风险通知,以及2小时连续血糖预测。目的:本研究旨在量化在1型糖尿病(T1D)成年人群中使用Predict应用程序的预测功能的潜在血糖益处。方法:采用临床支持的弗吉尼亚大学Replay数字双胞胎模拟器进行计算机对比研究。对照组模拟符合国际指南的标准低血糖和高血糖缓解策略,与干预组进行比较,干预组采用响应应用程序预测功能的概率用户行为模型。对204对数字双胞胎进行了评估,代表29,929天的数据,来自REPLACE-BG临床试验数据集。结果:结果表明,使用该应用程序的预测功能有可能改善成人T1D患者的血糖控制。模拟干预导致平均2.9个百分点的时间低于范围(结论:这一计算机评估提供了强有力的证据,支持Accu-Chek SmartGuide Predict应用程序在改善成人T1D患者血糖管理方面的潜在临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Diabetes technology & therapeutics
Diabetes technology & therapeutics 医学-内分泌学与代谢
CiteScore
10.60
自引率
14.80%
发文量
145
审稿时长
3-8 weeks
期刊介绍: Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.
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