■尽管有大量证据表明连续血糖监测(CGM)在糖尿病管理中的益处,使用这项技术的很大一部分人仍在努力实现血糖目标。为了应对这一挑战,我们提出了Accu-Chek®SmartGuide预测应用程序,创新的CGM数字伴侣,包含一套先进的葡萄糖预测功能,旨在早期告知用户有关急性血糖情况。
■应用程序的功能,由三种机器学习模型提供动力,包括两小时的血糖预测,30分钟的低血糖检测,以及用于睡前干预的夜间低血糖预测。对模型性能的评估包括三个数据集,包括MDI上患有T1D的受试者(n=21),在MDI上患有2型糖尿病(T2D)的受试者(n=59),和接受胰岛素泵治疗的T1D受试者(n=226)。
■在聚合数据集上,两小时血糖预测模型,在30、45、60和120分钟的预测范围内,在一致性误差网格的区域A和B中实现的数据点百分比为:99.8%,99.3%,98.7%,96.3%,分别。30分钟低血糖预测模型实现了准确性,灵敏度,特异性,平均提前期,受试者工作特征曲线下面积(ROCAUC)为:98.9%,95.2%,98.9%,16.2分钟,和0.958。夜间低血糖预测模型取得了一定的准确性,灵敏度,特异性,和ROCAUC为:86.5%,55.3%,91.6%,和0.859。
■对不同胰岛素治疗的T1D和T2D受试者的不同队列进行评估时,三种预测模型的性能一致性,包括真实世界的数据,为现实世界的功效提供保证。
UNASSIGNED: Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.
UNASSIGNED: The app\'s functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models\' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).
UNASSIGNED: On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.
UNASSIGNED: The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.