Skladnev Victor N, Tarnavskii Stanislav, McGregor Thomas, Ghevondian Nejhdeh, Gourlay Steve, Jones Timothy W
AiMedics Pty. Ltd., Eveleigh, New South Wales, Australia.
J Diabetes Sci Technol. 2010 Jan 1;4(1):34-40. doi: 10.1177/193229681000400105.
The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features.
Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions.
The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels.
Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.
随着闭环血糖（BG）控制系统（CGMS）整体低血糖警报性能的提升，其被接受程度可能会提高。本文介绍了一种基于临床数据的计算机模拟分析，该分析针对1型糖尿病（T1DM）患者的CGMS警报系统模型进行训练阈值设置，并通过来自原型低血糖警报系统（HypoMon）的传感器融合进行增强。该原型警报系统主要基于自主神经系统（ANS）的独立反应特征。
使用先前在98名T1DM志愿者身上记录的夜间BG数据来模拟警报性能。这些数据包括HypoMon（AiMedics私人有限公司）系统检测到的相应ANS反应特征。通过对这些夜间BG数据应用概率模型来模拟CGMS数据和警报。所开发的概率模型使用的平均反应延迟为7.1分钟，每个样本的测量误差偏移为+/-标准差（SD）=4.5mg/dl（0.25mmol/升），垂直偏移（校准偏移）为+/-SD =19.8mg/dl（1.1mmol/升）。模拟为每位患者生成90至100次测量。所有分析的警报系统在46名患者的训练集上进行优化，并在56名患者的测试集上进行评估。两组之间的划分基于入组日期。这些分析的优化基于检测准确性而非检测时间。通过在相同评估条件下比较训练后的CGMS和融合数据算法在测试集上的性能，评估这种数据融合形式对低血糖警报性能的贡献。
在相同特异性下，模拟添加HypoMon数据使CGMS低血糖警报性能提高了10%。增强警报系统的灵敏度从87%（CGMS作为独立测量）提高到97%。特异性保持在85%不变。测试集上的阳性预测值从61%提高到66%，阴性预测值从96%提高到99%。在敏感性分析中，这些增强效果是稳定的。敏感性分析还表明，在较低的CGMS警报性能水平下，性能提升幅度更大。
自主神经系统反应特征提供了适合与CGMS数据融合的补充信息，以增强夜间低血糖警报。
