An Online Failure Detection Method of the Glucose Sensor-Insulin Pump System: Improved Overnight Safety of Type-1 Diabetic Subjects
An Online Failure Detection Method of the Glucose Sensor-Insulin Pump System: Improved Overnight Safety of Type-1 Diabetic Subjects
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DOI：
10.1109/tbme.2012.2227256
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发表时间：
2013-02-01
影响因子：
4.6
通讯作者：
Cobelli, Claudio
中科院分区：
文献类型：
--
作者：
Facchinetti, Andrea;Del Favero, Simone;Cobelli, Claudio
用于实时连续葡萄糖监测（CGM）的传感器和用于连续皮下胰岛素输注（CSII）的泵为1型糖尿病治疗开辟了新的场景。然而，CGM或CSII的偶尔失败可能使糖尿病患者暴露于可能的严重风险，特别是过夜（例如，例如，在一个实施例中，胰岛素给药不当）。在这方面的贡献，我们提出了一种方法来检测在真实的时间，同时使用CGM和CSII数据流和黑箱模型的葡萄糖-胰岛素系统的故障。首先，从先前监测期间收集的CGM和CSII数据离线识别葡萄糖-胰岛素系统的个体化状态空间模型。然后，在线使用该模型、CGM和CSII实时数据流，通过利用卡尔曼滤波方法获得未来葡萄糖浓度的预测及其置信区间。如果CGM探头测量的葡萄糖值与预测值不一致，则生成故障警报，以缓解患者安全风险。该方法进行了测试，通过使用UVA/Padova 1型糖尿病模拟器创建的100个虚拟患者。模拟了三种不同类型的故障：CGM曲线中的尖峰、葡萄糖探头灵敏度损失和胰岛素泵输送故障。结果表明，在所有情况下，该方法都能够正确地生成警报，具有非常有限数量的误报和误报，平均低于10%。在三名受试者中使用该方法支持了模拟结果，证明该方法在CGM传感器-CSII泵系统出现故障时生成警报的准确性可以显著提高1型糖尿病患者的夜间安全性。
Sensors for real-time continuous glucose monitoring (CGM) and pumps for continuous subcutaneous insulin infusion (CSII) have opened new scenarios for Type-1 diabetes treatment. However, occasional failures of either CGM or CSII may expose diabetic patients to possibly severe risks, especially overnight (e. g., inappropriate insulin administration). In this contribution, we present a method to detect in real time such failures by simultaneously using CGM and CSII data streams and a black-box model of the glucose-insulin system. First, an individualized state-space model of the glucose-insulin system is identified offline from CGM and CSII data collected during a previous monitoring. Then, this model, CGM and CSII real-time data streams are used online to obtain predictions of future glucose concentrations together with their confidence intervals by exploiting a Kalman filtering approach. If glucose values measured by the CGM sensor are not consistent with the predictions, a failure alert is generated in order to mitigate the risks for patient safety. The method is tested on 100 virtual patients created by using the UVA/Padova Type-1 diabetic simulator. Three different types of failures have been simulated: spike in the CGM profile, loss of sensitivity of glucose sensor, and failure in the pump delivery of insulin. Results show that, in all cases, the method is able to correctly generate alerts, with a very limited number of false negatives and a number of false positives, on average, lower than 10%. The use of the method in three subjects supports the simulation results, demonstrating that the accuracy of the method in generating alerts in presence of failures of the CGM sensor-CSII pump system can significantly improve safety of Type-1 diabetic patients overnight.