Weimer James, Chen Sanjian, Peleckis Amy, Rickels Michael R, Lee Insup
1 Department of Computer and Information Science, University of Pennsylvania , Philadelphia, Pennsylvania.
2 Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania.
Diabetes Technol Ther. 2016 Oct;18(10):616-624. doi: 10.1089/dia.2015.0266. Epub 2016 Oct 5.
Fully automated artificial pancreas systems require meal detectors to supplement blood glucose level regulation, where false meal detections can cause unnecessary insulin delivery with potentially fatal consequences, and missed detections may cause the patient to experience extreme hyperglycemia. Most existing meal detectors monitor various measures of glucose rate-of-change to detect meals where varying physiology and meal content complicate balancing detector sensitivity versus specificity.
We developed a novel meal detector based on a minimal glucose-insulin metabolism model and show that the detector is, by design, invariant to patient-specific physiological parameters in the minimal model. Our physiological parameter-invariant (PAIN) detector achieves a near-constant false alarm rate across all individuals and is evaluated against three other major existing meal detectors on a clinical type 1 diabetes data set.
In the clinical evaluation, the PAIN-based detector achieves an 86.9% sensitivity for an average false alarm rate of two alarms per day. In addition, for all false alarm rates, the PAIN-based detector performance is significantly better than three other existing meal detectors. In addition, the evaluation results show that the PAIN-based detector uniquely (as compared with the other meal detectors) has low variance in detection and false alarm rates across all patients, without patient-specific personalization.
The PAIN-based meal detector has demonstrated better detection performance than existing meal detectors, and it has the unique strength of achieving a consistent performance across a population with varying physiology without any individual-level parameter tuning or training.
全自动化人工胰腺系统需要餐食检测器来辅助调节血糖水平，其中误餐检测可能导致不必要的胰岛素注射，从而带来潜在的致命后果，而漏检可能会使患者出现严重高血糖。大多数现有的餐食检测器通过监测血糖变化率的各种指标来检测餐食，但个体生理差异和餐食成分各不相同，这使得平衡检测器的灵敏度和特异性变得复杂。
我们基于一个简化的葡萄糖 - 胰岛素代谢模型开发了一种新型餐食检测器，并证明该检测器在设计上对简化模型中患者特定的生理参数具有不变性。我们的生理参数不变（PAIN）检测器在所有个体中实现了近乎恒定的误报率，并在一个临床1型糖尿病数据集上与其他三种主要的现有餐食检测器进行了对比评估。
在临床评估中，基于PAIN的检测器实现了86.9%的灵敏度，平均误报率为每天两次警报。此外，对于所有误报率，基于PAIN的检测器的性能显著优于其他三种现有餐食检测器。此外，评估结果表明，基于PAIN的检测器（与其他餐食检测器相比）在所有患者中的检测率和误报率方差较低，无需针对患者进行个性化设置。
基于PAIN的餐食检测器已证明其检测性能优于现有餐食检测器，并且具有独特的优势，即在不同生理状态的人群中无需任何个体水平的参数调整或训练就能实现一致的性能。
