Department of Health Science and Technology, Aalborg University, Denmark.
Department of Health Science and Technology, Aalborg University, Denmark.
Diabetes Metab Syndr. 2024 Feb;18(2):102972. doi: 10.1016/j.dsx.2024.102972. Epub 2024 Feb 25.
Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting.
Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min.
A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two.
The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.
预测糖尿病患者的血糖水平有助于改善血糖控制。然而,并非所有患者的血糖动态都具有可预测性,这可能导致治疗策略无效。我们旨在研究 7 天盲法筛选试验在识别适合血糖预测的糖尿病患者方面的效果。
根据筛查结果(合格和不合格),将 1 型糖尿病(T1D)患者分为高初始误差和低初始误差组。对 334 名接受连续血糖监测(CGM)的个体进行了 64460560 分钟的长期血糖预测(提前 30/60 分钟)评估。
筛查准确性与长期平均绝对相对差异(MARD)之间存在较强相关性(0.661-0.736;p<0.001),表明筛查和长期误差之间具有显著的可预测性。组分析显示,在 Clark 误差网格的 D 区和 C 区,预测结果落在范围内的比例分别降低了三分之二和二分之一。
通过筛查识别适合血糖预测的合格患者是一种实用且有效的策略。实施这种方法可以减少不良血糖预测的发生。