Sansum Diabetes Research Institute, Santa Barbara, CA 93106, USA.
Diabetes Technol Ther. 2012 Aug;14(8):719-27. doi: 10.1089/dia.2011.0198. Epub 2012 Jun 12.
The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment.
Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia.
The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead.
The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.
本研究旨在开发一种比较低血糖预测算法并为不同应用选择参数设置的方法，例如触发胰岛素泵暂停或提醒进行补救性碳水化合物治疗。
使用 Dexcom™（圣地亚哥，加利福尼亚州）SEVEN™连续血糖监测系统，在包含 30 例 1 型糖尿病患者 490 天的动态数据集上实施具有不同参数设置的低血糖预测算法。使用代表真阳性率、假阳性率和警告时间分布的一组建议指标来评估性能。进行了一项前瞻性的计算机模拟研究，以展示使用不同参数设置预防或缓解低血糖的效果。
回顾性研究结果表明，不同的低血糖缓解方法需要不同的参数设置。当使用补救性碳水化合物时，需要高的真阳性率、最小的假阳性率和短警告时间的警报。通过 30 分钟的预测时间和两个连续的标志来达到警报要求，达到了这些目标：78%的事件以 3.0 次假警报/天和 66%的警报在事件发生后 30 分钟内发生的概率被检测到。这种参数设置选择在计算机模拟中得到了证实：使用补救性碳水化合物治疗将低血糖持续时间从 14.9%减少到 0.5%。然而，对于另一种方法，例如泵暂停，这种参数设置仅将低血糖减少到 8.7%，这可以通过警告时间超过 30 分钟的概率较低来预期。
所提出的指标允许直接比较低血糖预测算法，并为不同类型的低血糖缓解选择参数设置，如前瞻性计算机模拟研究中所示，通过提醒或使用补救性碳水化合物来治疗低血糖。
