Model-based decision support for tight glucose control without hypoglycemia

基于模型的决策支持，可严格控制血糖而不会发生低血糖

• 批准号: 8309053
• 负责人: Gilles Clermont
• 金额: $ 17.7万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8309053
• 关键词: Address; Algorithms; Anatomy; Artificial Pancreas; Blood Glucose; Characteristics; Clinical; Clinical Data; Clinical Trials; Computer Simulation; Critical Care; Critical Illness; Data; Data Set; Decision Support Systems; Development; Devices; Dose; Electronics; Engineering; Event; Frequencies; Glucagon; Glucose; Goals; Hyperglycemia; Hypoglycemia; Incidence; Individual; Information Systems; Inpatients; Insulin; Intervention; Literature; Logistics; Measurement; Measures; Medical; Modeling; Notification; Nurses; Operative Surgical Procedures; Outcome; Patients; Physiological; Physiology; Population; Process; Randomized Clinical Trials; Recommendation; Recovery; Regulation; Research; Science; Structure; System; Technology; Testing; Time; Validation; Variant; Workload; base; blood glucose regulation; cohort; data modeling; design; improved; mathematical model; meetings; minimally invasive; model development; predictive modeling; prevent; programs; sensor; tool; treatment strategy

DESCRIPTION (provided by applicant): While tight glucose control has been shown to improve the outcomes of some critical care patients, much controversy regarding its overall benefit persists; in part due an unacceptable incidence of hypoglycemia, or low blood sugar. A decision support system for glucose control in critical care, much like an artificial pancreas, is comprised of three essential components: (1) a glucose measuring device, (2) an algorithm that interprets this measurement and recommends a treatment strategy, and (3) a delivery device that implements this strategy, delivering insulin, glucose, or some other agent (e.g., glucagon) to a patient. This proposal will use systems engineering tools to provide a robust answer to the following questions: given the characteristics of a minimally invasive glucose measuring device, what is the tightest glucose control achievable while avoiding hypoglycemia, and what is the strategy to achieve this control? We propose to use a very large multi-center dataset of critically ill patients receiving insulin, aiming to (1) calibrate and validate a mathematical model of glucose and insulin dynamics and (2) characterize between-patient variations as embodied in model parameters. Such a model will then be used to (3) design and deliver a patient-tailored decision support system, in the form of a portable interface that would forewarn clinical practitioners of potential hypoglycemic episodes and recommend insulin or dextrose dose administration. The ultimate goal of this proposal is to put all necessary tools in place for a randomized clinical trial of tight glucose control in critically ill patients, while completely avoiding episodes of hypoglycemia. It is expected that a successful completion of this proposal will have high translational impact and contribute to systems engineering science, specifically in the tailoring of sophisticated algorithms to patient- specific needs.

描述（由申请人提供）：虽然已显示紧密的葡萄糖控制可改善某些重症监护患者的结果，但关于其整体福利的争议仍然存在；部分原因是低血糖或低血糖的发生率不可接受。重症护理中葡萄糖控制的决策支持系统，就像人造胰腺一样，由三个基本组成部分组成：（1）一种葡萄糖测量手段，（2）一种解释该测量并建议治疗策略的算法，以及（3 ）一种实现此策略，将胰岛素，葡萄糖或其他药物（例如胰高血糖素）传递到患者的输送装置。该提案将使用系统工程工具为以下问题提供强大的答案：鉴于微创葡萄糖测量装置的特征，在避免降低低血糖的同时，最紧密的葡萄糖控制能力是什么？实现此控制的策略是什么？我们建议使用接受胰岛素的重症患者的非常大的多中心数据集，旨在（1）校准和验证葡萄糖和胰岛素动力学的数学模型，（2）表征在模型参数中体现的患者之间的变化。然后，这种模型将以（3）设计和提供患者量的决策支持系统，形式是一种便携式界面，该界面将预见潜在的降血糖发作的临床从业人员，并建议使用胰岛素或脱氧剂量。该提案的最终目的是为重症患者的紧密葡萄糖控制进行随机临床试验，同时完全避免低血糖发作。预计该提案的成功完成将产生很高的翻译影响，并有助于系统工程科学，特别是在针对患者特定需求的复杂算法量身定制方面。