A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas.
A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas.
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DOI：
10.1177/1932296814553267
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发表时间：
2014-11-01
影响因子：
5
通讯作者：
Bequette, B Wayne
中科院分区：
文献类型：
--
作者：
Baysal, Nihat;Cameron, Fraser;Bequette, B Wayne
连续血糖监测仪 (CGM) 提供实时间质葡萄糖浓度，这对于 1 型糖尿病患者的自动化治疗至关重要。校准错误、噪声尖峰、退出或施加到该部位的压力（例如，睡觉时躺在该部位）可能会导致不准确的葡萄糖信号，从而可能导致不适当的胰岛素剂量决策。这些研究重点关注夜间发生的压力引起的传感器衰减 (PISA) 问题，该问题可能会导致预测性低葡萄糖悬浮系统中泵出现意外关闭。这里介绍的算法使用实时 CGM 读数，无需了解膳食、胰岛素剂量、活动、传感器重新校准或指尖采血测量。实时 PISA 检测技术在门诊“家庭”数据上进行了测试，该数据来自预测性低血糖暂停试验，包含超过 1125 个晚上的数据。通过使用 PISA 检测算法的不同参数，总共创建了 178 个集合，以说明其可用性能范围。一位在分析临床数据集方面拥有丰富专业知识的工程师通过基于网络的分析工具对追踪结果进行了审查，约 3% 的 CGM 读数被标记为 PISA 事件，并用作黄金标准。结果表明，该算法成功检测出88.34%的PISA，通过改变算法参数，误检率可降低至1.70%。使用所提出的 PISA 检测方法可以显着减少夜间不良泵悬浮，并可能导致夜间平均血糖水平降低，同时仍然实现低血糖风险。
Continuous glucose monitors (CGMs) provide real-time interstitial glucose concentrations that are essential for automated treatment of individuals with type 1 diabetes. Miscalibration, noise spikes, dropouts, or pressure applied to the site (e.g., lying on the site while sleeping) can cause inaccurate glucose signals, which could lead to inappropriate insulin dosing decisions. These studies focus on the problem of pressure-induced sensor attenuations (PISAs) that occur overnight and can cause undesirable pump shut-offs in a predictive low glucose suspend system. The algorithm presented here uses real-time CGM readings without knowledge of meals, insulin doses, activity, sensor recalibrations, or fingerstick measurements. The real-time PISA detection technique was tested on outpatient "in-home" data from a predictive low-glucose suspend trial with over 1125 nights of data. A total of 178 sets were created by using different parameters for the PISA detection algorithm to illustrate its range of available performance. The tracings were reviewed via a web-based analysis tool by an engineer with an extensive expertise on analyzing clinical datasets and ~3% of the CGM readings were marked as PISA events which were used as the gold standard. It is shown that 88.34% of the PISAs were successfully detected by the algorithm, and the percentage of false detections could be reduced to 1.70% by altering the algorithm parameters. Use of the proposed PISA detection method can result in a significant decrease in undesirable pump suspensions overnight, and may lead to lower overnight mean glucose levels while still achieving a low risk of hypoglycemia.