136例糖尿病患者应用胰岛素泵治疗的护理分析

目的:探讨糖尿病患者应用胰岛素泵治疗的临床护理配合,提高治疗水平.方法:回顾性分析我院自2009年3月～2011年3月收治的136例应用胰岛素泵治疗的糖尿病患者的临床资料,总结临床护理方法.结果:经2周的治疗后,空腹血糖＜6.3mmol/L者48例,空腹血糖＞6.3但≤7.4mm01/L者83例,总有效率为94.9%;治疗期间出现低血糖者2例,血糖控制无效2例,无死亡病例.结论:应用胰岛素泵治疗糖尿病效果理想,可以有效控制血糖、降低临床死亡率,治疗期间配合以护理措施可以提高治疗效果、降低并发症发生.     

血糖检测技术研究进展

血糖是临床上分析病人是否患糖尿病的重要参考指标之一。本文对有创、微创和无创检测三种检测血糖的方法作了阐述,并进行了展望,指出闭环无创胰岛素泵集成系统和半闭环有创胰岛素泵集成系统是今后血糖检测技术研究的主要方向。     

便携式胰岛素泵的研制及其数据通信方案设计

介绍采用MSP430系列单片机为核心研制的一种便携式低功耗胰岛素泵.重点讨论了如何利用单片机内部的RS232接口与PC机进行通信,实现该系统软件的在线升级;同时也可以把存放在flash中的历史记录通过RS232接口传到PC机,由PC机端软件存储及处理,以便医生据此合理指导患者用药;也可以将胰岛素泵运行过程中出现的问题发送给厂商,便于对产品质量进行跟踪.     

一种测试肌肉阻力的方法

介绍了一种利用压力传感器测试药物输送时人体肌肉阻力方法.针对人体腹部肌肉,利用胰岛素泵往人体送药,采用标定法,测试在送药过程中所遇到的阻力.经测试,在胰岛素泵打药的过程中肌肉所承受的阻力是个动态变化值,最大约为9kPa,基础阻力约为0.25～1.09kPa.此测试方法为人体肌肉阻力的测试提供一种方案,在药物输送精度控制方面有很大的参考价值.     

胰岛素泵的原理和发展

<正>一、胰岛素泵的发展概述一型糖尿病又叫青年发病型糖尿病,这是因为它常常在35岁以前发病,一型糖尿病占全部糖尿病例的10%以下。一型糖尿病病友体内胰腺产生胰     

胰岛素泵的总体设计

从总体介绍了以Microchip的PIC系列的单片机为控制单元的便携式胰岛素泵的硬件和软件设计,并对系统各个模块进行调试.对系统的可靠性进行有益的探索,给出了加强胰岛素泵系统可靠性所采取的软件、硬件件方面的措施.     

一种无创血糖检测仪的初步研究

糖尿病是一个威胁人类健康的重要疾病,目前糖尿病检测的主要方法都是有创的,有其局限性。研究了一种基于能量守恒法的无创伤血糖检测仪;设计出了相关的硬件部分、软件部分、传感器探头及相应的血糖浓度提取算法。经临床初步实验,表明测试结果与AMS-AUTOLAB18全自动生化分析仪所测得结果的相关系数达到了0.807。     

可改善糖尿病患者神经感觉的新型装置

糖尿病患者经常会出现感觉神经功能障碍.但一项新的研究发现,一种利用近红外光线的新型装置能显著改善糖尿病患者的感觉神经功能障碍.不过,治疗价值仅限于感觉功能并未受到严重损伤的患者.     

面向智能血糖管理的餐前胰岛素剂量贝叶斯学习优化方法

餐前胰岛素剂量精准决策是改善糖尿病患者血糖管理的关键.临床治疗中胰岛素剂量调整一般在较短时间内完成,具有典型的小样本特征;数据驱动建模在该情形下无法准确学习患者餐后血糖代谢规律,难以确保胰岛素剂量的安全和有效决策.针对这一问题,设计一种临床经验辅助的餐前胰岛素剂量自适应优化决策框架,构建高斯过程血糖预测模型和模型有效性在线评估机制,提出基于历史剂量和临床经验决策约束的贝叶斯优化方法,实现小样本下餐后血糖轨迹的安全预测和餐前胰岛素注射剂量的优化决策.该方法的安全性和有效性通过美国食品药品监督管理局接受的UVA/Padova T1DM平台测试结果和1型糖尿病患者实际临床数据决策结果充分验证.可为餐前胰岛素剂量智能决策及临床试验提供方法基础和技术支持,也为中国糖尿病患者血糖管理水平的有效改善,提供了精准医学治疗手段.     

基于Java手机的血糖浓度监测系统

开发一种基于MotorolaA388c手机的血糖浓度监测系统。通过分析葡萄糖酶电极的基本结构和影响血糖浓度监测的因素,给出了血糖浓度的测量原理和实现方法。血糖测试设备与手机的无缝结合为用户提供了准确方便的测试环境,网络化的信息反馈实现了病人对血糖的自我监护。该系统经过初步测试,符合美国糖尿病协会对血糖仪测试误差的要求。     

Challenges and recent progress in the development of a closed-loop artificial pancreas

Pursuit of a closed-loop artificial pancreas that automatically controls the blood glucose of individuals with type 1 diabetes has intensified during the past 6 years. Here we discuss the recent progress and challenges in the major steps towards a closed-loop system. Continuous insulin infusion pumps have been widely available for over two decades, but “smart pump” technology has made the devices easier to use and more powerful. Continuous glucose monitoring (CGM) technology has improved and the devices are more widely available. A number of approaches are currently under study for fully closed-loop systems; most manipulate only insulin, while others manipulate insulin and glucagon. Algorithms include on–off (for prevention of overnight hypoglycemia), proportional–integral–derivative (PID), model predictive control (MPC) and fuzzy logic based learning control. Meals cause a major “disturbance” to blood glucose, and we discuss techniques that our group has developed to predict when a meal is likely to be consumed and its effect. We further examine both physiology and device-related challenges, including insulin infusion set failure and sensor signal attenuation. Finally, we discuss the next steps required to make a closed-loop artificial pancreas a commercial reality.     

Weighting Restriction for Intravenous Insulin Delivery on T1DM Patient via $H_{infty}$ Control

A weighting restriction with frequency components is proposed for the insulin delivery on Type 1 Diabetics Mellitus (T1DM) towards the control of the blood glucose level. The weighting restriction is stated from a model of healthy subjects which includes a rate for insulin delivery. The frequency components are incorporated via a transfer function from the plasma glucose to the free-plasma insulin such that a H _infin-based controller is designed. In this way, the control synthesis involves the frequency components on which a healthy pancreas delivers insulin for the glucose homeostasis. In order to test controller performance, a dynamical model of an actuator is also included in the closed-loop system to add its effects in the closed-loop evaluation of the H _infin -based controller. The actuator is a pump to deliver of an insulin infusion according with the rate computed by the controller. Note that the contribution is particularly focused on T1DM; however, the inclusion of weighting restriction can be used also onto critical care conditions.     

Physiologic insulin delivery with insulin feedback: A control systems perspective

Individuals with type 1 diabetes mellitus must effectively manage glycemia to avoid acute and chronic complications related to aberrations of glucose levels. Because optimal diabetes management can be difficult to achieve and burdensome, research into a closed-loop insulin delivery system has been of interest for several decades. This paper provides an overview, from a control systems perspective, of the research and development effort of a particular algorithm—the external physiologic insulin delivery system. In particular the introduction of insulin feedback, based on β-cell physiology, is covered in detail. A summary of human clinical trials is provided in the context of the evolution of this algorithm, and this paper outlines some of the research avenues that show particular promise.     

Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose-insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.     

Estimation of insulin sensitivity in diabetic Göttingen Minipigs

In patients with type 1 diabetes mellitus, insulin sensitivity is a parameter which strongly affects insulin therapy. Due to its time-dependent variation, this parameter can improve the strategy for automatic closed-loop blood glucose control. The aim of this work is to estimate the insulin sensitivity of patients with type 1 diabetes mellitus based on measured blood glucose concentrations. For this, an Extended Kalman Filter is used, based on a simplified version of the well-known Sorensen model. The compartment model of Sorensen was adapted to the glucose metabolic behaviour in diabetic Göttingen Minipigs by means of experimental data and reduced by neglecting unobservable state variables. Here, the Extended Kalman Filter is designed for simultaneous state and parameter estimation of insulin sensitivity using the insulin infusion rate and the meal size as input signals, and measurements of blood glucose concentration as output signal. The performance of the Extended Kalman Filter was tested in in silico studies using the minipig model, and is analysed by comparing the output signal of the filter with measurement data from the animal trials.     

A consultation system for insulin therapy

This paper describes a computer system to advice on insulin therapy for diabetic in-patients. A mathematical model was developed to describe the effect of insulin on blood glucose (BG) level. The system uses an adaptive approach to analyse the response to an applied insulin dosage. It learns the patient's individual parameters. All conventional injection and insulin pump regimens are supported. The individualised model is used to predict BG level of the proposed insulin dosage. The system uses a generate-reject strategy to output optimum insulin therapy in terms of optimum BG. The predictive capability of the system was tested and it is able to predict BG with a precision of 2.5 mmol/l after 3 days and 6 days of insulin pump treatment and conventional injection therapy, respectively.     

Adaptive-learning model predictive control for complex physiological systems: Automated insulin delivery in diabetes

An adaptive-learning model predictive control (AL-MPC) framework is proposed for incorporating disturbance prediction, model uncertainty quantification, pattern learning, and recursive subspace identification for use in controlling complex dynamic systems with periodically recurring large random disturbances. The AL-MPC integrates online learning from historical data to predict the future evolution of the model output over a specified horizon and proactively mitigate significant disturbances. This goal is accomplished using dynamic regularized latent variable regression (DrLVR) approach to quantify disturbances from the past data and forecast their future progression time series. An enveloped path for the future behavior of the model output is extracted to further enhance the robustness of the closed-loop system. The controller set-point, penalty weights of the objective function, and constraints criteria can be modified in advance for the expected periods of the disturbance effects. The proposed AL-MPC is used to regulate glucose concentration in people with Type 1 diabetes by an automated insulin delivery system. Simulation results demonstrate the effectiveness of the proposed technique by improving the performance indices of the closed-loop system. The MPC algorithm integrated with DrLVR disturbance predictor has compared to MPC reinforced with dynamic principal component analysis linked with K-nearest neighbors and hyper-spherical clustering (k-means) technique. The simulation results illustrate that the AL-MPC can regulate the glucose concentrations of people with Type 1 diabetes to stay in the desired range (70–180) mg/dL 84.4% of the time without causing any hypoglycemia and hyperglycemia events.     

基于胰岛素基础率估计的人工胰腺系统自抗扰控制

胰岛素基础率是人工胰腺系统实现人体血糖闭环控制的基准,但该变量在临床治疗中难以准确确定.针对这一问题,本文设计了一种基于胰岛素基础率动态估计的人工胰腺自抗扰控制方法,通过扩张状态观测器(Extended state ob-server,ESO)实时估计血糖代谢过程中的内部与外界干扰,构建具备参数自适应能力的反馈控制律和...     

Neural predictive controller for closed-loop control of glucose using the subcutaneous route: A simulation study

The objective of this study was to develop and evaluate a strategy for closed-loop control of glucose using subcutaneous (s.c.) glucose measurement and s.c. infusion of monomeric insulin analogues. The method was based on off-line identification of the glucoregulatory system using neural networks and a nonlinear model predictive controller. Numerical studies on system identification and closed-loop control of glucose were carried out using a comprehensive model of glucose regulation. The proposed control strategy was robust against noise and time delays, and enabled stable control also for slow time variations of the controlled process. In conclusion, closed-loop control of glucose is feasible using the s.c. route and a neural predictive controller.