Department of Information Engineering, University of Padova , Padova, Italy .
Diabetes Technol Ther. 2015 May;17(5):355-63. doi: 10.1089/dia.2014.0230. Epub 2015 Feb 11.
Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm.
Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data.
Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively.
The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.
人工胰腺(AP)研究中的血糖控制通常通过以下指标进行评估,例如血糖(BG)浓度低于 70mg/dL 或接近正常范围 70-180mg/dL 的时间百分比(简称低血糖时间和目标时间)。在门诊研究中,这些控制指标只能根据连续血糖监测(CGM)传感器数据计算,由于其准确性不高,可能会导致评估不公平。本文的目的是展示如果使用最近提出的改装算法对 CGM 数据进行预处理,控制指标可以更准确地确定。
考虑了 47 名 1 型糖尿病患者的数据。受试者在闭环控制试验中进行了研究,规定了 3 次 24 小时入院。使用 Dexcom ®(圣地亚哥,CA)SEVEN ®Plus CGM 传感器监测血糖浓度。同时使用 YSI 分析仪(俄亥俄州扬斯敦市的 Yellow Springs Instrument)平行收集频繁的 BG 参考值。为了模拟门诊条件下可用的少量参考值,我们对 YSI 数据进行了下采样,并仅向改装算法提供了将在门诊协议中收集的参考值。基于原始和改装后的 CGM 轨迹计算了低血糖时间、目标时间、血糖谱的平均值和标准差,并将其与使用经常获得的 YSI 数据计算的值进行了比较。
使用改装后的轨迹,与原始 CGM 轨迹相比,估计低血糖时间和目标时间的平均误差减少了约一半(从 4.5%降至 1.9%和从 8.7%降至 4.4%)。平均值和标准差的误差进一步降低,从 10.0mg/dL 降至 3.5mg/dL,从 8.6mg/dL 降至 2.9mg/dL。
改装后的 CGM 相对于原始 CGM 轨迹的准确性提高,使得在门诊 AP 研究中更可靠地评估血糖控制成为可能。