Skladnev Victor N, Ghevondian Nejhdeh, Tarnavskii Stanislav, Paramalingam Nirubasini, Jones Timothy W
AiMedics Pty. Ltd., Eveleigh, New South Wales, Australia.
J Diabetes Sci Technol. 2010 Jan 1;4(1):67-74. doi: 10.1177/193229681000400109.
The aim of this study was to evaluate the performance of a prototype noninvasive alarm system (HypoMon) for the detection of nocturnal hypoglycemia. A prospective cohort study evaluated an alarm system that included a sensor belt, a radio frequency transmitter for chest belt signals, and a receiver. The receiver incorporated integrated "real-time" algorithms designed to recognize hypoglycemia "signatures" in the physiological parameters monitored by the sensor belt.
Fifty-two children and young adults with type 1 diabetes mellitus (T1DM) participated in this blinded, prospective, in-clinic, overnight study. Participants had a mean age of 16 years (standard deviation 2.1, range 12-20 years) and were asked to follow their normal meal and insulin routines for the day of the study. Participants had physiological parameters monitored overnight by a single HypoMon system. Their BG levels were also monitored overnight at regular intervals via an intravenous cannula and read on two independent Yellow Springs Instruments analyzers. Hypoglycemia was not induced by any manipulations of diabetes management, rather the subjects were monitored overnight for "natural" occurrences of hypoglycemia. Performance analyses included comparing HypoMon system alarm times with allowed time windows associated with each hypoglycemic event.
The primary recognition algorithm in the prototype alarm system performed at a level consistent with expectations based on prior user surveys. The HypoMon system correctly recognized 8 out of the 11 naturally occurring overnight hypoglycemic events and falsely alarmed on 13 out of the remaining 41 normal nights [sensitivity 73% (8/11), specificity 68% (28/41), positive predictive value 38%,negative predictive value 90%].
The prototype HypoMon shows potential as an adjunct method for noninvasive overnight monitoring for hypoglycemia events in young people with T1DM.
本研究旨在评估一种用于检测夜间低血糖的非侵入性报警系统（HypoMon）原型的性能。一项前瞻性队列研究对一个报警系统进行了评估，该系统包括一个传感器腰带、一个用于传输胸带信号的射频发射器以及一个接收器。接收器集成了“实时”算法，旨在识别传感器腰带监测的生理参数中的低血糖“特征”。
52名1型糖尿病（T1DM）儿童和青年参与了这项单盲、前瞻性、临床内过夜研究。参与者的平均年龄为16岁（标准差2.1，范围12 - 20岁），并被要求在研究当天遵循正常的饮食和胰岛素使用习惯。参与者通过单个HypoMon系统进行过夜生理参数监测。他们的血糖水平也通过静脉插管定期进行过夜监测，并在两台独立的黄泉仪器分析仪上读取。低血糖并非由任何糖尿病管理操作诱发，而是对受试者进行过夜监测以观察低血糖的“自然”发生情况。性能分析包括将HypoMon系统的报警时间与每个低血糖事件的允许时间窗进行比较。
基于先前的用户调查，原型报警系统中的主要识别算法表现出与预期一致的水平。HypoMon系统正确识别出11次自然发生的过夜低血糖事件中的8次，并在其余41个正常夜晚中的13次发出误报[灵敏度73%（8/11），特异性68%（28/41），阳性预测值38%，阴性预测值90%]。
HypoMon原型显示出作为T1DM青年患者夜间低血糖事件非侵入性监测辅助方法的潜力。
