Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool

Invited journal symposium paperObjectives: To develop a safe and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues. To develop an in silico simulation tool of Type 1 diabetes to predict long-term glycaemic control outcomes of clinical interventions. Methods: The virtual patient method is used to develop a simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n=40). The tool is used to test the adaptive protocol (AC) and a conventional intensive insulin therapy (CC) against results from a representative control cohort. Optimal and suboptimal basal insulin replacement are evaluated as a function of self-monitoring blood glucose (SMBG) frequency in conjunction with the (AC and CC) prandial control protocols. Results: In long-term glycaemic control, the AC protocol significantly decreases HbA1c in conditions of suboptimal basal insulin replacement for SMBG frequencies =6/day, and reduced the occurrence of mild and severe hypoglycaemia by 86-100% over controls over all SMBG frequencies in conditions of optimal basal insulin. Conclusions: A simulation tool to predict long-term glycaemic control outcomes from clinical interventions is developed to test a novel, adaptive control protocol for Type 1 diabetes. The protocol is effective and safe compared to conventional intensive insulin therapy and controls. As fear of hypoglycaemia is a large psychological barrier to glycaemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycaemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept

Overview of Glycemic Control in Critical Care - Relating Performance and Clinical Results

Inagural review article invited for inaugural journalBackground: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95th percentile (±2 ) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary: Model-based methods provide tighter, more adaptable “one method fits all” solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to current “one size fits all” sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or “one method fits all”, control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient

Variability of insulin sensitivity during the first 4 days of critical illness

1-pageSafe, effective tight glycaemic control (TGC) can improve outcomes in critical care patients, but is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between insulin concentration and insulin mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycaemia. This study investigates the daily evolution of model-based insulin sensitivity level and variability for critical care patients receiving TGC during the first four days of their ICU stay

Validation of a virtual patient and virtual trials method for accurate prediction of tight glycemic control protocol performance

Peer reviewe

Does tight glycemic control positively impact on patient mortality?

peer reviewe

Pilot Trials of STAR Target to Range Glycemic Control

ESICM 2011 programme is available in files INTRODUCTION. Tight glycemic control (TGC) has shown benefits in cardiac surgery ICU patients. STAR (Stochastic TARgeted) is a flexible, model-based TGC protocol accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 90 mg/dL. OBJECTIVES. To assess the safety, efficacy and clinical workload of the STAR TGC controller in pilot trials

Pulmonary embolism diagnostics from the driver function

Ventricular driver functions are not readily measured in the ICU, but can clearly indicate the development of pulmonary embolism (PE) otherwise difficult to diagnose. Recent work has developed accurate methods of measuring these driver functions from readily available ICU measurements. This research tests those methods by assessing the ability of these driver functions to diagnose the evolution of PE

Enhanced insulin sensitivity variability in the first 3 days of ICU stay: implications for tight glycemic control

Effective tight glycemic control (TGC) can improve outcomes, particularly in cardiovascular surgery, but is difficult to achieve. Variability in insulin sensitivity/resistance resulting from the level and evolution of stress response, particularly early in a patient’s stay, can lead to hyperglycemia and variability, which are associated with mortality. This study quantifies the daily evolution of the variability of insulin sensitivity for cardiovascular surgical and all other ICU patients

A Study And Application of Face Recognition System

人脸识别是近几年来非常受关注的研究课题之一。这一研究领域综合了多个学科：图像处理、模式识别、计算机视觉、神经网络，心理学等等。人脸识别所要解决的问题可以概述如下：给定场景下的静态图像或动态图像序列，应用已知人脸库，从场景里识别一个或多个人。本文研究静态人脸图像识别，这个问题的解决包括：从场景中分割人脸（人脸检测），人脸区域的特征提取、识别或验证。在识别问题中，输入系统人脸图像是未知的人脸，系统将从人脸数据库中找出与输入一致的人脸。本文的主要研究工作：1.本文第一章主要研究与人脸识别相关的神经科学和计算机人脸识别的各种方法，探讨生物识别和计算机识别的相互联系，以及生物识别方法，特别是人脸识别技术...Machine recognition of human face still and video images is one of the active research areas including several disciplines such as image processing, pattern recognition, computer vision and neural networks, psychology and so forth. A general statement of the problem can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a sto...学位：工学硕士院系专业：物理与机电工程学院机电工程系_测试计量技术及仪器学号：20032901