Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
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DOI：
10.1089/dia.2008.0065
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发表时间：
2009-04-01
影响因子：
5.4
通讯作者：
Smith, Donald
中科院分区：
文献类型：
--
作者：
Eren-Oruklu, Meriyan;Cinar, Ali;Smith, Donald
背景：对未来血糖浓度的估计是糖尿病管理的关键任务。预测的葡萄糖值可用于早期低血糖/高血糖警报或用于调整手动或自动泵的胰岛素注射或胰岛素输注速率。连续葡萄糖监测（CGM）技术以高频率提供葡萄糖读数，并因此提供对受试者的葡萄糖变化的详细了解。本研究的目的是开发可靠的受试者特定的葡萄糖预测模型，使用CGM data.Methods：两个独立的患者数据库收集下住院（无干扰）和正常的日常生活条件下，用于验证所提出的葡萄糖预测算法。两个数据库均由使用CGM设备以5分钟间隔收集的葡萄糖浓度数据组成。使用时间序列分析，从患者自己的CGM数据开发低阶线性模型。时间序列模型与递归识别和变化检测方法相结合，这使得模型能够动态适应受试者间/受试者内变异性和血糖紊乱。预测性能进行评估的葡萄糖预测误差和克拉克误差网格分析（CG-EGA）。结果：预测误差显着减少与递归识别的模型，和预测进一步提高了包括一个参数变化检测方法。CG-EGA分析结果在90%或以上的准确读数。结论：受试者特定的葡萄糖预测策略已经开发。将变化检测方法包括到递归算法提高了预测精度。所提出的建模算法与少量的参数是一个很好的候选人安装在便携式设备的早期低血糖/高血糖报警和关闭葡萄糖调节回路与胰岛素泵。
Background: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data.Methods: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA).Results: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more.Conclusions: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.