Albisser A M, Baidal D, Alejandro R, Ricordi C
The Bioengineering Department, University of California San Diego, La Jolla, CA, USA.
Diabetologia. 2005 Jul;48(7):1273-9. doi: 10.1007/s00125-005-1805-4. Epub 2005 Jun 3.
AIMS/HYPOTHESIS: Diabetic subjects do home monitoring to substantiate their success (or failure) in meeting blood glucose targets set by their providers. To succeed, patients require decision support, which, until now, has not included knowledge of future blood glucose levels or of hypoglycaemia. To remedy this, we devised a glucose prediction engine. This study validates its predictions.
The prediction engine is a computer program that accesses a central database in which daily records of self-monitored blood glucose data and life-style parameters are stored. New data are captured by an interactive voice response server on-line 24 h a day, 7 days a week. Study subjects included 24 patients with debilitating hypoglycaemia (unawareness), which qualified them for islet cell transplantation. Comparison of each prediction with the actually observed data was done using a Clarke Error Grid (CEG). Patients and providers were blinded as to the predictions.
Prior to transplantation, a total of 31,878 blood glucose levels were reported by the study subjects. Some 31,353 blood glucose predictions were made by the engine on a total of 8,733 days-used. Of these, 79.4% were in the clinically acceptable Zones of the CEG. Of 728 observed episodes of hypoglycaemia, 384 were predicted. After transplantation, a total of 45,529 glucose measurements were reported on a total of 12,906 days-used. Some 42,316 glucose predictions were made, of which 97.5% were in the acceptable CEG Zones A and B. Successful transplantation eliminated hypoglycaemia, improved glycaemic control, lowered HbA(1)c and freed 10 of 24 patients from daily insulin therapy.
CONCLUSIONS/INTERPRETATION: It is clinically feasible to generate valid predictions of future blood glucose levels. Prediction accuracy is related to glycaemic stability. Risk of hypoglycaemia can be predicted. Such knowledge may be useful in self-management.
目的/假设：糖尿病患者进行家庭监测，以证实他们在实现医疗服务提供者设定的血糖目标方面的成功（或失败）。为了取得成功，患者需要决策支持，而到目前为止，这种支持并不包括对未来血糖水平或低血糖情况的了解。为了弥补这一点，我们设计了一个血糖预测引擎。本研究对其预测结果进行了验证。
预测引擎是一个计算机程序，它可以访问一个中央数据库，该数据库存储了自我监测的血糖数据和生活方式参数的每日记录。新数据由一个交互式语音应答服务器每周7天、每天24小时在线采集。研究对象包括24例患有严重低血糖（无警觉性）的患者，这些患者符合胰岛细胞移植的条件。使用克拉克误差网格（CEG）将每个预测结果与实际观察数据进行比较。患者和医疗服务提供者对预测结果不知情。
在移植前，研究对象共报告了31878个血糖水平。该引擎在总共8733个使用日中进行了约31353次血糖预测。其中，79.4%处于CEG的临床可接受区域。在728次观察到的低血糖发作中，有384次被预测到。移植后，在总共12906个使用日中，共报告了45529次血糖测量结果。进行了约42316次血糖预测，其中97.5%处于CEG的可接受区域A和B。成功的移植消除了低血糖，改善了血糖控制，降低了糖化血红蛋白（HbA1c），并使24名患者中的10名摆脱了每日胰岛素治疗。
结论/解读：对未来血糖水平进行有效预测在临床上是可行的。预测准确性与血糖稳定性有关。低血糖风险可以被预测。这些信息可能对自我管理有用。
