Jacobs Peter G, Tyler Nichole S, Vanderwerf Scott M, Mosquera-Lopez Clara, Seidl Thomas, Cargill Robert, Branigan Deborah, Ramsey Katrina, Morris Kristin, Benware Sheila, Ward W Kenneth, Castle Jessica R
Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, 3303 SW Bond Ave., Portland, OR, 97232, USA. Electronic address: http://www.ohsu.edu/jacobs.
Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, 3303 SW Bond Ave., Portland, OR, 97232, USA.
Biosens Bioelectron. 2020 Oct 1;165:112221. doi: 10.1016/j.bios.2020.112221. Epub 2020 Apr 29.
Automated insulin delivery systems for people with type 1 diabetes rely on an accurate subcutaneous glucose sensor and an infusion cannula that delivers insulin in response to measured glucose. Integrating the sensor with the infusion cannula would provide substantial benefit by reducing the number of devices inserted into subcutaneous tissue. We describe the sensor chemistry and a calibration algorithm to minimize impact of insulin delivery artifacts in a new glucose sensing cannula. Seven people with type 1 diabetes undergoing automated insulin delivery used two sensing cannulae whereby one delivered a rapidly-acting insulin analog and the other delivered a control phosphate buffered saline (PBS) solution with no insulin. While there was a small artifact in both conditions that increased for larger volumes, there was no difference between the artifacts in the sensing cannula delivering insulin compared with the sensing cannula delivering PBS as determined by integrating the area-under-the-curve of the sensor values following delivery of larger amounts of fluid (P = 0.7). The time for the sensor to recover from the artifact was found to be longer for larger fluid amounts compared with smaller fluid amounts (10.3 ± 8.5 min vs. 41.2 ± 78.3 s, P < 0.05). Using a smart-sampling Kalman filtering smoothing algorithm improved sensor accuracy. When using an all-point calibration on all sensors, the smart-sampling Kalman filter reduced the mean absolute relative difference from 10.9% to 9.5% and resulted in 96.7% of the data points falling within the A and B regions of the Clarke error grid. Despite a small artifact, which is likely due to dilution by fluid delivery, it is possible to continuously measure glucose in a cannula that simultaneously delivers insulin.
用于1型糖尿病患者的自动胰岛素输送系统依赖于精确的皮下葡萄糖传感器和根据测得的葡萄糖输送胰岛素的输注套管。将传感器与输注套管集成在一起,可减少插入皮下组织的设备数量,从而带来显著益处。我们描述了一种传感器化学和校准算法,以尽量减少新型葡萄糖传感套管中胰岛素输送伪影的影响。7名接受自动胰岛素输送的1型糖尿病患者使用了两种传感套管,其中一种输送速效胰岛素类似物,另一种输送不含胰岛素的对照磷酸盐缓冲盐水(PBS)溶液。虽然在两种情况下都存在一个小的伪影,且随着输送量的增加而增大,但通过对输送大量液体后传感器值的曲线下面积进行积分确定,输送胰岛素的传感套管中的伪影与输送PBS的传感套管中的伪影之间没有差异(P = 0.7)。发现与较小液体量相比,较大液体量时传感器从伪影中恢复的时间更长(10.3±8.5分钟 vs. 41.2±78.3秒,P < 0.05)。使用智能采样卡尔曼滤波平滑算法提高了传感器的准确性。当对所有传感器进行全点校准时,智能采样卡尔曼滤波器将平均绝对相对差异从10.9%降低到9.5%,并使96.7%的数据点落在克拉克误差网格的A区和B区。尽管存在一个可能由于液体输送稀释导致的小伪影,但在同时输送胰岛素的套管中连续测量葡萄糖是可行的。