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选择连续血糖监测的持续时间以可靠评估达标时间:一种克服基于相关方法局限性的新分析方法。

Choosing the duration of continuous glucose monitoring for reliable assessment of time in range: A new analytical approach to overcome the limitations of correlation-based methods.

机构信息

Department of Information Engineering, University of Padova, Padova, Italy.

Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria.

出版信息

Diabet Med. 2022 May;39(5):e14758. doi: 10.1111/dme.14758. Epub 2021 Dec 16.

Abstract

AIMS

Reliable estimation of the time spent in different glycaemic ranges (time-in-ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino et al., Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593-1603), an international panel of experts suggested using a correlation-based approach to obtain the minimum number of days for reliable time-in-ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446-2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time-in-ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70-180) mg/dL range (TIR).

METHODS

The first 100 and 150 days of data were extracted from study A (148 subjects, ~180 days), and the first 100, 150, 200, 250 and 300 days of data from study B (45 subjects, ~365 days). For each of these data windows, the minimum monitoring duration was computed using correlation-based and equation-based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies.

RESULTS

When changing the dataset duration, the correlation-based approach produces inconsistent results, ranging from 23 to 64 days, for TIR. The equation-based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18 days for windows from study A, and 17 days for windows from study B. Similar considerations hold for other time-in-ranges.

CONCLUSIONS

The equation-based approach offers advantages for the design of clinical trials having time-in-ranges as final end points, with focus on trial duration.

摘要

目的

可靠估计不同血糖范围(time-in-ranges)所花费的时间需要足够长的连续血糖监测。在 2019 年的一篇论文(Battelino 等人,连续血糖监测数据解释的临床目标:time-in-range 的国际共识建议。糖尿病护理。2019;42:1593-1603)中,一个国际专家小组建议使用基于相关性的方法来获得可靠的 time-in-ranges 估计所需的最短天数。最近(在 Camerlingo 等人的研究中,设计评估糖尿病治疗的临床试验:使用连续血糖监测数据估计 time-in-ranges 的最小持续时间,以达到所需的精度。糖尿病肥胖代谢。2021;23:2446-2454),我们提出了一个数学方程,将监测天数与 time-in-ranges 估计值的不确定性联系起来。在这项工作中,我们比较了这两种方法,主要关注(70-180)mg/dL 范围内(TIR)的时间。

方法

从研究 A(148 例患者,180 天)中提取前 100 和 150 天的数据,从研究 B(45 例患者,365 天)中提取前 100、150、200、250 和 300 天的数据。对于每个这些数据窗口,使用基于相关性和基于方程的方法计算最小监测持续时间。对于从同一研究中提取的不同持续时间的窗口,以及从不同研究中提取的相同持续时间的窗口,对建议进行了比较。

结果

当改变数据集持续时间时,基于相关性的方法会产生不一致的结果,TIR 的范围从 23 天到 64 天不等。基于方程的方法被发现对这个问题具有稳健性,因为它仅受监测人群特征的影响。事实上,为了在 TIR 周围获得 5%的置信区间,它建议研究 A 的窗口为 18 天,研究 B 的窗口为 17 天。对于其他 time-in-ranges,也有类似的考虑。

结论

对于以 time-in-ranges 为最终终点的临床试验设计,基于方程的方法具有优势,重点是试验持续时间。

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