Kovatchev Boris, Breton Marc
Diabetes Technology Program, University of Virginia, Charlottesville, Virginia 22909, USA.
J Diabetes Sci Technol. 2010 Jan 1;4(1):119-22. doi: 10.1177/193229681000400115.
In this issue of Journal of Diabetes Science and Technology, Keenan and colleagues used archival data from the STAR 1 clinical trial (Medtronic Diabetes) to support the claim that the new Veo calibration algorithm improves the accuracy of continuous glucose monitoring, particularly in the critical hypoglycemic range. Extensive data analyses are presented to support this claim; the results are convincing, and the estimated improvement in hypoglycemic detection from 55% for the standard calibration to 82% for the Veo is particularly impressive. We can therefore conclude that the Veo algorithm has the potential to improve the accuracy of hypoglycemia alarms and ultimately contribute to closed-loop control. However, the presented results should be interpreted cautiously because they are based on retrospective analysis and are heavily dependent on the distribution of blood glucose levels observed in a particular data set.
在本期《糖尿病科学与技术杂志》中,基南及其同事利用来自STAR 1临床试验(美敦力糖尿病公司)的存档数据,来支持新的Veo校准算法可提高连续血糖监测准确性这一说法,尤其是在严重低血糖范围内。文中展示了大量数据分析来支持这一说法;结果令人信服,且估计低血糖检测的改善程度从标准校准的55%提升至Veo的82%,这尤其令人印象深刻。因此,我们可以得出结论,Veo算法有潜力提高低血糖警报的准确性,并最终有助于闭环控制。然而,所呈现的结果应谨慎解读,因为它们基于回顾性分析,且严重依赖于特定数据集中观察到的血糖水平分布。