DeJournett Jeremy, DeJournett Leon
1 Ideal Medical Technologies Inc, Asheville, NC, USA.
J Diabetes Sci Technol. 2017 Nov;11(6):1207-1217. doi: 10.1177/1932296817711297. Epub 2017 Jun 22.
Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.
We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.
For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.
The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.
在重症监护病房(ICU)环境中有效控制血糖有可能降低发病率和死亡率,从而降低医疗保健支出。为了评估什么构成有效的血糖控制,通常会报告几个指标,包括血糖处于目标范围内的时间、轻度和重度低血糖时间、变异系数等。迄今为止,还没有一个指标能将所有这些单独的指标结合起来给出一个表示整体性能的数字。我们提出了一个综合指标,该指标结合了5个常用的报告指标,并使用这个综合指标来比较6种血糖控制器。
我们评估了以下控制器:理想医疗技术公司(IMT)基于人工智能的控制器、耶鲁方案、Glucommander、温特格斯特等人的PID控制器、GRIP和NICE-SUGAR。我们在80名模拟患者、4种临床相关的外源性葡萄糖输注以及一种非临床输注中评估了每个控制器,以此测试控制器处理困难情况的能力。这总共产生了2400次为期5天的模拟,以及585604个用于分析的个体血糖值。我们使用了一个随机游走传感器误差模型,该模型给出了10%的平均相对绝对误差(MARD)。对于每个控制器,我们计算了严重低血糖(<40mg/dL)、轻度低血糖(40 - 69mg/dL)、正常血糖(70 - 140mg/dL)、高血糖(>140mg/dL)、变异系数(CV)以及我们新的控制器指标。
对于所测试的控制器,我们的新控制器评分指标达到了以下中位数:IMT:88.1,耶鲁:46.7,Glucommander:47.2,PID:50,GRIP:48.2,NICE:46.4。
本研究中采用的新评分指标有望成为评估新型和现有基于ICU的血糖控制器的一种手段,并且未来可用于比较重症监护中血糖控制研究的结果。基于这一新指标,IMT基于人工智能的血糖控制器表现出最一致性能结果。