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: 8176486
• 负责人: Gilles Clermont
• 金额: $ 20.48万
• 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 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8176486
• 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. PUBLIC HEALTH RELEVANCE: Critically ill surgical and medical patients demonstrate better survival with tight glucose control, but tight glucose control in clinical trial populations has often been achieved with an unacceptable rate of hypoglycemic (low blood sugar) episodes requiring further treatment. The research program proposed will develop an interactive model-based Decision Support System 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 via our decision support system.

描述（由申请人提供）：虽然严格的血糖控制已被证明可改善某些重症监护患者的结局，但其总体获益仍存在许多争议;部分原因是低血糖或低血糖的发生率不可接受。用于重症监护中的葡萄糖控制的决策支持系统，很像人工胰腺，由三个基本部件组成：（1）葡萄糖测量装置，（2）解释该测量并推荐治疗策略的算法，以及（3）实施该策略的递送装置，其递送胰岛素、葡萄糖或一些其他试剂（例如，胰高血糖素）给患者。本提案将使用系统工程工具为以下问题提供可靠的答案：鉴于微创葡萄糖测量器械的特性，在避免低血糖的同时可实现的最严格葡萄糖控制是什么，以及实现该控制的策略是什么？我们建议使用接受胰岛素治疗的危重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，以及（2）表征模型参数中体现的患者间变化。然后，这种模型将用于（3）设计和提供患者定制的决策支持系统，其形式为便携式界面，其将预先警告临床从业者潜在的低血糖发作并推荐胰岛素或葡萄糖剂量施用。该提案的最终目标是为重症患者严格血糖控制的随机临床试验提供所有必要的工具，同时完全避免低血糖发作。预计该提案的成功完成将具有很高的转化影响，并有助于系统工程科学，特别是在根据患者特定需求定制复杂算法方面。 公共卫生相关性：重症手术和内科患者在严格血糖控制下表现出更好的生存率，但临床试验人群中的严格血糖控制通常是在不可接受的低血糖（低血糖）发作率下实现的，需要进一步治疗。提出的研究计划将开发一个基于交互式模型的决策支持系统，该系统将预先警告临床医生潜在的低血糖发作，并推荐胰岛素或葡萄糖剂量给药。该提案的最终目标是为重症患者严格血糖控制的随机临床试验提供所有必要的工具，同时通过我们的决策支持系统完全避免低血糖发作。