Albisser A M, Sakkal S, Wright C
Bioengineering Department, University of California, San Diego, California, USA.
Diabetes Technol Ther. 2005 Jun;7(3):487-96. doi: 10.1089/dia.2005.7.487.
Patients with diabetes do daily self-monitoring of blood glucose (SMBG). For such patients, we devised an engine that predicts not only the expected blood glucose level at the next meal but also the pending risks of hypoglycemia. The purpose of this study was to validate the predictions and provide evidence of the safety and efficacy of using predicted data in dosing decision support for routine patient care.
The prediction engine is a computer program that accesses a central database into which daily records of self-monitored blood glucose data are captured by direct access either across the WWW or by an interactive voice response service on-line 24/7. Validation was done by comparison of predicted values to the subsequently observed data using a Clarke Error Grid. Safety focused on body weight and the frequency of hypoglycemia. Efficacy was judged according to glycated hemoglobin and daily insulin dosages. The experimental design contrasted patients in the tight control (TC) group who had been recently converted to intensified (basal-bolus) therapy with patients in the poor control (PC) group on conventional therapy and who were referred to begin intensified therapy. Both groups accessed the remote database to report their daily SMBG. Predicted glucose values were used in dosing decision support for the PC but not the TC group.
Over the 6-month study period a total of 30,129 blood glucose levels were reported by the 54 study patients, and some 24,953 blood glucose predictions were made. Of these, 83% were in the clinically acceptable zones of the Clarke Error Grid. When these data were used for dosing decision support in the PC group, glycated hemoglobin levels fell significantly from 9.7 +/- 1.7% to 7.9 +/- 1.2%, and hypoglycemia dropped fourfold. Total daily insulin doses declined 22%, while body weight remained constant. In the parallel TC group (n = 24), glycated hemoglobin also fell, but only slightly from 7.6 +/- 0.9% to 7.2 +/- 1.1%, while daily insulin doses, rates of hypoglycemia and body weight remained constant.
A novel engine is capable of generating meaningful predictions of blood glucose levels. Use of these validated predictions in decision support for managing medication doses proved safe and efficacious.
糖尿病患者需每日进行血糖自我监测（SMBG）。针对此类患者，我们设计了一种引擎，它不仅能预测下一顿饭前的预期血糖水平，还能预测即将发生的低血糖风险。本研究的目的是验证这些预测，并为在日常患者护理的给药决策支持中使用预测数据的安全性和有效性提供证据。
该预测引擎是一个计算机程序，可访问中央数据库，通过万维网直接访问或通过全天候在线交互式语音应答服务，将自我监测的血糖数据的每日记录录入该数据库。通过使用克拉克误差网格将预测值与随后观察到的数据进行比较来进行验证。安全性关注体重和低血糖发生频率。疗效根据糖化血红蛋白和每日胰岛素剂量来判断。实验设计将最近转换为强化（基础 - 餐时）治疗的严格控制（TC）组患者与接受常规治疗且控制不佳（PC）组且被转诊开始强化治疗的患者进行对比。两组均访问远程数据库以报告其每日的血糖自我监测情况。预测的血糖值用于PC组的给药决策支持，但不用于TC组。
在为期6个月的研究期间，54名研究患者共报告了30129个血糖水平，并做出了约24953次血糖预测。其中，83%处于克拉克误差网格的临床可接受区域。当这些数据用于PC组的给药决策支持时，糖化血红蛋白水平从9.7±1.7%显著降至7.9±1.2%，低血糖发生率下降了四倍。每日胰岛素总剂量下降了22%，而体重保持不变。在平行的TC组（n = 24）中，糖化血红蛋白也有所下降，但仅从7.6±0.9%略微降至7.2±1.1%，而每日胰岛素剂量、低血糖发生率和体重保持不变。
一种新型引擎能够生成有意义的血糖水平预测。在管理药物剂量的决策支持中使用这些经过验证的预测被证明是安全有效的。
