A Real-Time Continuous Glucose Monitoring Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events
A Real-Time Continuous Glucose Monitoring Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events
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DOI：
10.1089/dia.2019.0139
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发表时间：
2019-07-25
影响因子：
5.4
通讯作者：
Facchinetti, Andrea
中科院分区：
文献类型：
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作者：
Camerlingo, Nunzio;Vettoretti, Martina;Facchinetti, Andrea
背景：美国糖尿病协会(ADA)推荐的低血糖标准疗法建议糖尿病患者在血糖浓度低于70 mg/dL时立即服用少量碳水化合物，即所谓的减糖疗法(HTS)。然而，由于使用了由连续血糖监测(CGM)传感器数据提供的适当实时算法的预测能力，可以通过提前触发HTS来预防或至少缓解低血糖事件。材料和方法：本文提出的触发HTS预防即将发生的低血糖事件的算法是基于“动态风险”的计算，存在一个结合当前血糖及其变化率的非线性函数，两者都由CGM提供。将所提出的算法的性能与ADA指南进行了比较，在Silico中，在100名进行单餐实验的虚拟患者的数据集上，使用UVA/Padova 1型糖尿病模拟器产生了诱导性餐后低血糖。结果：在无噪声的CGM数据中，建议的算法将低血糖所花费的时间从36[29-43]分钟减少到0[0-11]分钟(P<；0.0001)，并伴随着治疗后血糖浓度的反弹(Ptr)从136[25-75百分位]减少到121[116-127]mg/dL(P<；0.0001)。在有噪声的CGM数据中，低血糖时间从41[28-49]分钟减少到25[0-41]分钟(P<；0.0001)，PTR从174[146-189]mg/dL减少到137[123-151]mg/dL(P<；0.0001)。结论：新算法在生成预防性HTS方面的潜力，可以在不增加高血糖的情况下显著降低低血糖，这表明它在硅胶中的进一步开发和测试，例如，模拟胰岛素泵和每日多次注射疗法。
Background: The standard treatment for hypoglycemia recommended by the American Diabetes Association (ADA) suggests patients with diabetes to take small amounts of carbohydrates, the so-called hypotreatments (HTs), as soon as blood glucose concentration goes below 70 mg/dL. However, prevention, or at least mitigation, of hypoglycemic events could be achieved by triggering HTs ahead of time thanks to the use of the predictive capabilities of suitable real-time algorithms fed by continuous glucose monitoring (CGM) sensor data. Materials and Methods: The algorithm proposed in this article to trigger HTs for preventing forthcoming hypoglycemic events is based on the computation of the "dynamic risk", there is a nonlinear function combining current glycemia with its rate-of-change, both provided by CGM. A comparison of performance of the proposed algorithm against the ADA guidelines is made, in silico, on datasets of 100 virtual patients undergoing a single-meal experiment, with induced postmeal hypoglycemia, generated by the UVA/Padova type 1 diabetes simulator. Results: On noise-free CGM data, the proposed algorithm reduces the time spent in hypoglycemia, on median [25th-75th percentiles] from 36 [29-43] to 0 [0-11] min (P < 0.0001), with a concomitant decrease of the post-treatment rebound (PTR) in glucose concentration, on median [25th-75th percentiles] from 136 [121-148] to 121 [116-127] mg/dL (P < 0.0001). On noisy CGM data, there is still a reduction of both time spent in hypoglycemia from 41 [28-49] min to 25 [0-41] min (P < 0.0001) and PTR from 174 [146-189] mg/dL to 137 [123-151] mg/dL (P < 0.0001). Conclusions: The potentiality of the new algorithm in generating preventive HTs, which can allow significant reduction of hypoglycemia without concomitant increase of hyperglycemia, suggests its further development and test in silico, for example, simulating both insulin pump and multiple-daily-injection therapies.