背景:1型糖尿病(T1D)影响全球900多万人,需要细致的自我管理来控制血糖(BG)。利用BG预测技术允许增加BG控制和减少由自我管理要求引起的糖尿病负担。本文综述了T1D中的BG预测模型,其中包括营养成分。
方法:系统搜索,利用PRISMA准则,确定的文章侧重于纳入营养变量的T1DBG预测算法。筛选并分析了符合条件的研究的模型类型,在模型中包含其他方面,预测范围,患者群体,输入,和准确性。
结果:该研究将138个血糖预测模型分类为数据驱动(54%),生理(14%),和混合(33%)类型。36%的模型使用≤30分钟的预测范围,在34%中31-60分钟,61-90分钟在11%,在10%中91-120分钟,和>120分钟在9%。神经网络是最常用的数据驱动技术(47%),简单的碳水化合物摄入量通常包含在模型中(数据驱动:72%,生理:52%,杂种:67%)。主要使用真实或自由生活数据(83%)。
结论:T1D血糖预测的主要目标是做出明智的决策并保持安全的BG水平。考虑所有营养素对膳食计划和临床相关性的影响。
BACKGROUND: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.
METHODS: A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.
RESULTS: The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).
CONCLUSIONS: The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.