Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections.
Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections.
复制标题
将不确定性感知预测模型与睡前智能零食干预相结合，预防每日多次注射的 1 型糖尿病患者出现夜间低血糖。
DOI：
10.1093/jamia/ocad196
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Jacobs,PeterG
中科院分区：
文献类型：
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作者：
Mosquera-Lopez,Clara;Roquemen-Echeverri,Valentina;Tyler,NicholeS;Patton,SusanaR;Clements,MarkA;Martin,CorbyK;Riddell,MichaelC;Gal,RobinL;Gillingham,Melanie;Wilson,LeahM;Castle,JessicaR;Jacobs,PeterG
夜间低血糖是1型糖尿病患者面临的一个已知挑战，特别是对于身体活跃的人或每天多次注射的人。我们开发了一种证据神经网络（ENN），根据几种血糖指标和体力活动模式，在睡前预测夜间低血糖的概率和时间（睡前0-4小时vs 4-8小时）。我们利用这些predictionin silicotto规定睡前碳水化合物与aSmart Snackintervention具体到预测的最低夜间血糖和夜间低血糖的时间。Materials and methodsWe leverage free living datasets collected from 366 individuals from the T1 DEXI Study and Glooko.用于模拟夜间低血糖的ENN输入来自人口统计学信息、连续血糖监测和体力活动数据。我们评估的准确性ENN使用的接收器工作曲线下面积，和thesmartSnackintervention通过simulation.ResultsThe ENN实现了0.80和0.71的接收器工作曲线下面积预测夜间低血糖事件在0-4和4-8小时后就寝，分别优于所有评估的基线方法。通过计算机模拟，使用Smart Snack干预将夜间低血糖的概率从23.9 ± 14.1%降低到14.0 ± 13.3%，持续时间从7.4 ± 7.0%降低到2.4 ± 3.3%。基于智能零食干预有可能显着减少夜间低血糖事件的频率和持续时间。预测夜间最低血糖水平并积极推荐睡前碳水化合物摄入量可能有效预防夜间低血糖并减轻血糖自我管理的负担。
ObjectiveNocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictionsin silicoto prescribe bedtime carbohydrates with aSmart Snackintervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia.Materials and methodsWe leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of theSmart Snackintervention through simulations.ResultsThe ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of theSmart Snackintervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3%in silico.DiscussionOur findings indicate that the ENN-basedSmart Snackintervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events.ConclusionA decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.